{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Predicting bank's customer response\n", "\n", "Banks strive to increase the efficiency of their contacts with customers. One of the areas which require this is offering new products to existing clients (cross-selling). Instead of offering new products to all clients, it is a good idea to predict the probability of a positive response. Then the offers could be sent to those clients, for whom the probability of response is higher than some threshold value.\n", "\n", "In this notebook I try to solve this problem. In 2011 OTP-Bank in Russia has organized a competition reflecting the aforementioned situation. The data is taken from that [site](http://machinelearning.ru/wiki/index.php?title=%D0%97%D0%B0%D0%B4%D0%B0%D1%87%D0%B0_%D0%BF%D1%80%D0%B5%D0%B4%D1%81%D0%BA%D0%B0%D0%B7%D0%B0%D0%BD%D0%B8%D1%8F_%D0%BE%D1%82%D0%BA%D0%BB%D0%B8%D0%BA%D0%B0_%D0%BA%D0%BB%D0%B8%D0%B5%D0%BD%D1%82%D0%BE%D0%B2_%D0%9E%D0%A2%D0%9F_%D0%91%D0%B0%D0%BD%D0%BA%D0%B0_%28%D0%BA%D0%BE%D0%BD%D0%BA%D1%83%D1%80%D1%81%29).\n", "The competition's description and some data is in Russian, but I'll translate the necessary termins. Column names are already in English.\n", "\n", "Dataset contains 15223 clients; 1812 of them had a positive response. I can't use test set, as competition is finished and quality of predictions on test data can't be verified. So I can only split data in train and test and check the accuracy this way.\n", "\n", "The metric for the competition is AUC (area under curve). The winner achieved 0,6935, top-7 places have AUC higher than 0,67.\n", "\n", "I don't aim to beat these values, my goal is to explore and visualize the data. Also I want to show how to process the data and make predictions so that model is stable and can be interpreted." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import numpy as np\n", "import pandas as pd\n", "import seaborn as sns\n", "import matplotlib.pyplot as plt\n", "%matplotlib inline\n", "\n", "from sklearn.metrics import auc, roc_curve\n", "from sklearn.model_selection import train_test_split, cross_val_score\n", "from sklearn import preprocessing\n", "from sklearn import linear_model\n", "pd.set_option(\"display.max_columns\", 200)\n", "pd.set_option(\"display.max_rows\", 100)\n", "#from IPython.core.interactiveshell import InteractiveShell\n", "#InteractiveShell.ast_node_interactivity = \"all\"\n", "\n", "import functions\n", "\n", "%load_ext autoreload\n", "%autoreload 2" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Data loading and initial preprocessing" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Wall time: 6.37 s\n" ] } ], "source": [ "%%time\n", "data = pd.read_excel('data set.xls', sheetname='данные')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "I'll rename values for several columns first of all, and I'll drop some unnecessary columns." ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "data.loc[data['EDUCATION'] == 'Среднее специальное', 'EDUCATION'] = 'Professional School'\n", "data.loc[data['EDUCATION'] == 'Среднее', 'EDUCATION'] = 'Some High School'\n", "data.loc[data['EDUCATION'] == 'Неполное среднее', 'EDUCATION'] = 'Some Primary School'\n", "data.loc[data['EDUCATION'] == 'Высшее', 'EDUCATION'] = 'Undergraduate Degree'\n", "data.loc[data['EDUCATION'] == 'Неоконченное высшее', 'EDUCATION'] = 'No Formal Education'\n", "data.loc[data['EDUCATION'] == 'Два и более высших образования', 'EDUCATION'] = 'Post-Graduate Work'\n", "data.loc[data['EDUCATION'] == 'Ученая степень', 'EDUCATION'] = 'Graduate Degree'" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true }, "outputs": [], "source": [ "data.loc[data['MARITAL_STATUS'] == 'Состою в браке', 'MARITAL_STATUS'] = 'Married'\n", "data.loc[data['MARITAL_STATUS'] == 'Гражданский брак', 'MARITAL_STATUS'] = 'Partner'\n", "data.loc[data['MARITAL_STATUS'] == 'Разведен(а)', 'MARITAL_STATUS'] = 'Separated'\n", "data.loc[data['MARITAL_STATUS'] == 'Не состоял в браке', 'MARITAL_STATUS'] = 'Single'\n", "data.loc[data['MARITAL_STATUS'] == 'Вдовец/Вдова', 'MARITAL_STATUS'] = 'Widowed'" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": true }, "outputs": [], "source": [ "data.loc[data['GEN_INDUSTRY'] == 'Металлургия/Промышленность/Машиностроение', 'GEN_INDUSTRY'] = 'Iron & Steel'\n", "data.loc[data['GEN_INDUSTRY'] == 'Строительство', 'GEN_INDUSTRY'] = 'Construction - Raw Materials'\n", "data.loc[data['GEN_INDUSTRY'] == 'Нефтегазовая промышленность', 'GEN_INDUSTRY'] = 'Oil & Gas Operations'\n", "data.loc[data['GEN_INDUSTRY'] == 'Энергетика', 'GEN_INDUSTRY'] = 'Oil Well Services & Equipment'\n", "data.loc[data['GEN_INDUSTRY'] == 'Страхование', 'GEN_INDUSTRY'] = 'Insurance (Accident & Health)'\n", "data.loc[data['GEN_INDUSTRY'] == 'Банк/Финансы', 'GEN_INDUSTRY'] = 'Regional Banks'\n", "data.loc[data['GEN_INDUSTRY'] == 'Здравоохранение', 'GEN_INDUSTRY'] = 'Healthcare'\n", "data.loc[data['GEN_INDUSTRY'] == 'Управляющая компания', 'GEN_INDUSTRY'] = 'Business Services'\n", "data.loc[data['GEN_INDUSTRY'] == 'Туризм', 'GEN_INDUSTRY'] = 'Hotels & Motels'\n", "data.loc[data['GEN_INDUSTRY'] == 'Юридические услуги/нотариальные услуги', 'GEN_INDUSTRY'] = 'Personal Services'\n", "data.loc[data['GEN_INDUSTRY'] == 'Недвижимость', 'GEN_INDUSTRY'] = 'Real Estate Operations'\n", "data.loc[data['GEN_INDUSTRY'] == 'Развлечения/Искусство', 'GEN_INDUSTRY'] = 'Recreational Activities'\n", "data.loc[data['GEN_INDUSTRY'] == 'Ресторанный бизнес /общественное питание', 'GEN_INDUSTRY'] = 'Restaurants'\n", "data.loc[data['GEN_INDUSTRY'] == 'Образование', 'GEN_INDUSTRY'] = 'Schools'\n", "data.loc[data['GEN_INDUSTRY'] == 'Наука', 'GEN_INDUSTRY'] = 'Scientific & Technical Instr.'\n", "data.loc[data['GEN_INDUSTRY'] == 'Информационные технологии', 'GEN_INDUSTRY'] = 'Software & Programming'\n", "data.loc[data['GEN_INDUSTRY'] == 'Транспорт', 'GEN_INDUSTRY'] = 'Transportation'\n", "data.loc[data['GEN_INDUSTRY'] == 'Логистика', 'GEN_INDUSTRY'] = 'Trucking'\n", "data.loc[data['GEN_INDUSTRY'] == 'Ресторанный бизнес/Общественное питание', 'GEN_INDUSTRY'] = 'Restaurant & Catering'\n", "data.loc[data['GEN_INDUSTRY'] == 'Коммунальное хоз-во/Дорожные службы', 'GEN_INDUSTRY'] = 'Municipal economy/Road service'\n", "data.loc[data['GEN_INDUSTRY'] == 'Салоны красоты и здоровья', 'GEN_INDUSTRY'] = 'Beauty shop'\n", "data.loc[data['GEN_INDUSTRY'] == 'Сборочные производства', 'GEN_INDUSTRY'] = 'Assembly production'\n", "data.loc[data['GEN_INDUSTRY'] == 'Сельское хозяйство', 'GEN_INDUSTRY'] = 'Agriculture'\n", "data.loc[data['GEN_INDUSTRY'] == 'Химия/Парфюмерия/Фармацевтика', 'GEN_INDUSTRY'] = 'Chemistry/Perfumery/Pharmaceut'\n", "data.loc[data['GEN_INDUSTRY'] == 'ЧОП/Детективная д-ть', 'GEN_INDUSTRY'] = 'Detective'\n", "data.loc[data['GEN_INDUSTRY'] == 'Другие сферы', 'GEN_INDUSTRY'] = 'Others fields'\n", "data.loc[data['GEN_INDUSTRY'] == 'Государственная служба', 'GEN_INDUSTRY'] = 'Public & municipal administ.'\n", "data.loc[data['GEN_INDUSTRY'] == 'Информационные услуги', 'GEN_INDUSTRY'] = 'Information service'\n", "data.loc[data['GEN_INDUSTRY'] == 'Торговля', 'GEN_INDUSTRY'] = 'Market, real estate'\n", "data.loc[data['GEN_INDUSTRY'] == 'Маркетинг', 'GEN_INDUSTRY'] = 'Marketing'\n", "data.loc[data['GEN_INDUSTRY'] == 'Подбор персонала', 'GEN_INDUSTRY'] = 'Staff recruitment'\n", "data.loc[data['GEN_INDUSTRY'] == 'СМИ/Реклама/PR-агенства', 'GEN_INDUSTRY'] = 'Mass media'" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": true }, "outputs": [], "source": [ "data.loc[data['FAMILY_INCOME'] == 'от 10000 до 20000 руб.', 'FAMILY_INCOME'] = '10000-20000'\n", "data.loc[data['FAMILY_INCOME'] == 'от 20000 до 50000 руб.', 'FAMILY_INCOME'] = '20000-50000'\n", "data.loc[data['FAMILY_INCOME'] == 'от 5000 до 10000 руб.', 'FAMILY_INCOME'] = '5000-10000'\n", "data.loc[data['FAMILY_INCOME'] == 'свыше 50000 руб.', 'FAMILY_INCOME'] = '50000+'\n", "data.loc[data['FAMILY_INCOME'] == 'до 5000 руб.', 'FAMILY_INCOME'] = 'up to 5000'" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": true }, "outputs": [], "source": [ "data.drop(['GEN_TITLE', 'ORG_TP_STATE', 'ORG_TP_FCAPITAL', 'JOB_DIR', 'REG_ADDRESS_PROVINCE',\n", " 'FACT_ADDRESS_PROVINCE', 'POSTAL_ADDRESS_PROVINCE', 'TP_PROVINCE', 'REGION_NM'], axis=1, inplace=True)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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AGREEMENT_RKTARGETAGESOCSTATUS_WORK_FLSOCSTATUS_PENS_FLGENDERCHILD_TOTALDEPENDANTSEDUCATIONMARITAL_STATUSGEN_INDUSTRYFAMILY_INCOMEPERSONAL_INCOMEREG_FACT_FLFACT_POST_FLREG_POST_FLREG_FACT_POST_FLREG_FACT_POST_TP_FLFL_PRESENCE_FLOWN_AUTOAUTO_RUS_FLHS_PRESENCE_FLCOT_PRESENCE_FLGAR_PRESENCE_FLLAND_PRESENCE_FLCREDITTERMFST_PAYMENTDL_DOCUMENT_FLGPF_DOCUMENT_FLFACT_LIVING_TERMWORK_TIMEFACT_PHONE_FLREG_PHONE_FLGEN_PHONE_FLLOAN_NUM_TOTALLOAN_NUM_CLOSEDLOAN_NUM_PAYMLOAN_DLQ_NUMLOAN_MAX_DLQLOAN_AVG_DLQ_AMTLOAN_MAX_DLQ_AMTPREVIOUS_CARD_NUM_UTILIZED
05991015004910121Professional SchoolMarriedMarket, real estate10000-200005000.01111100000008000.0068650.00122018.0001116211580.0000001580.0NaN
15991023003210133Some High SchoolMarriedMarket, real estate10000-2000012000.011110000000021650.0064000.00113797.0101116114020.0000004020.0NaN
25991052505210140Some Primary SchoolMarriedSoftware & Programming10000-200009000.011111000100033126.00124000.00125184.00012111000.0000000.0NaN
35991080303910111Undergraduate DegreeMarriedSchools20000-5000025000.00100110000008491.8265000.00036168.0111116311589.9233331590.0NaN
45991178103010000Some High SchoolMarriedPublic & municipal administ.10000-2000012000.011110000100021990.00124000.00183101.01012116211152.1500002230.0NaN
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" ], "text/plain": [ " AGREEMENT_RK TARGET AGE SOCSTATUS_WORK_FL SOCSTATUS_PENS_FL GENDER \\\n", "0 59910150 0 49 1 0 1 \n", "1 59910230 0 32 1 0 1 \n", "2 59910525 0 52 1 0 1 \n", "3 59910803 0 39 1 0 1 \n", "4 59911781 0 30 1 0 0 \n", "\n", " CHILD_TOTAL DEPENDANTS EDUCATION MARITAL_STATUS \\\n", "0 2 1 Professional School Married \n", "1 3 3 Some High School Married \n", "2 4 0 Some Primary School Married \n", "3 1 1 Undergraduate Degree Married \n", "4 0 0 Some High School Married \n", "\n", " GEN_INDUSTRY FAMILY_INCOME PERSONAL_INCOME REG_FACT_FL \\\n", "0 Market, real estate 10000-20000 5000.0 1 \n", "1 Market, real estate 10000-20000 12000.0 1 \n", "2 Software & Programming 10000-20000 9000.0 1 \n", "3 Schools 20000-50000 25000.0 0 \n", "4 Public & municipal administ. 10000-20000 12000.0 1 \n", "\n", " FACT_POST_FL REG_POST_FL REG_FACT_POST_FL REG_FACT_POST_TP_FL \\\n", "0 1 1 1 1 \n", "1 1 1 1 0 \n", "2 1 1 1 1 \n", "3 1 0 0 1 \n", "4 1 1 1 0 \n", "\n", " FL_PRESENCE_FL OWN_AUTO AUTO_RUS_FL HS_PRESENCE_FL COT_PRESENCE_FL \\\n", "0 0 0 0 0 0 \n", "1 0 0 0 0 0 \n", "2 0 0 0 1 0 \n", "3 1 0 0 0 0 \n", "4 0 0 0 1 0 \n", "\n", " GAR_PRESENCE_FL LAND_PRESENCE_FL CREDIT TERM FST_PAYMENT \\\n", "0 0 0 8000.00 6 8650.0 \n", "1 0 0 21650.00 6 4000.0 \n", "2 0 0 33126.00 12 4000.0 \n", "3 0 0 8491.82 6 5000.0 \n", "4 0 0 21990.00 12 4000.0 \n", "\n", " DL_DOCUMENT_FL GPF_DOCUMENT_FL FACT_LIVING_TERM WORK_TIME \\\n", "0 0 1 220 18.0 \n", "1 0 1 137 97.0 \n", "2 0 1 251 84.0 \n", "3 0 0 36 168.0 \n", "4 0 1 83 101.0 \n", "\n", " FACT_PHONE_FL REG_PHONE_FL GEN_PHONE_FL LOAN_NUM_TOTAL LOAN_NUM_CLOSED \\\n", "0 0 0 1 1 1 \n", "1 1 0 1 1 1 \n", "2 0 0 1 2 1 \n", "3 1 1 1 1 1 \n", "4 1 0 1 2 1 \n", "\n", " LOAN_NUM_PAYM LOAN_DLQ_NUM LOAN_MAX_DLQ LOAN_AVG_DLQ_AMT \\\n", "0 6 2 1 1580.000000 \n", "1 6 1 1 4020.000000 \n", "2 11 0 0 0.000000 \n", "3 6 3 1 1589.923333 \n", "4 16 2 1 1152.150000 \n", "\n", " LOAN_MAX_DLQ_AMT PREVIOUS_CARD_NUM_UTILIZED \n", "0 1580.0 NaN \n", "1 4020.0 NaN \n", "2 0.0 NaN \n", "3 1590.0 NaN \n", "4 2230.0 NaN " ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data.head()" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "RangeIndex: 15223 entries, 0 to 15222\n", "Data columns (total 43 columns):\n", "AGREEMENT_RK 15223 non-null int64\n", "TARGET 15223 non-null int64\n", "AGE 15223 non-null int64\n", "SOCSTATUS_WORK_FL 15223 non-null int64\n", "SOCSTATUS_PENS_FL 15223 non-null int64\n", "GENDER 15223 non-null int64\n", "CHILD_TOTAL 15223 non-null int64\n", "DEPENDANTS 15223 non-null int64\n", "EDUCATION 15223 non-null object\n", "MARITAL_STATUS 15223 non-null object\n", "GEN_INDUSTRY 13856 non-null object\n", "FAMILY_INCOME 15223 non-null object\n", "PERSONAL_INCOME 15223 non-null float64\n", "REG_FACT_FL 15223 non-null int64\n", "FACT_POST_FL 15223 non-null int64\n", "REG_POST_FL 15223 non-null int64\n", "REG_FACT_POST_FL 15223 non-null int64\n", "REG_FACT_POST_TP_FL 15223 non-null int64\n", "FL_PRESENCE_FL 15223 non-null int64\n", "OWN_AUTO 15223 non-null int64\n", "AUTO_RUS_FL 15223 non-null int64\n", "HS_PRESENCE_FL 15223 non-null int64\n", "COT_PRESENCE_FL 15223 non-null int64\n", "GAR_PRESENCE_FL 15223 non-null int64\n", "LAND_PRESENCE_FL 15223 non-null int64\n", "CREDIT 15223 non-null float64\n", "TERM 15223 non-null int64\n", "FST_PAYMENT 15223 non-null float64\n", "DL_DOCUMENT_FL 15223 non-null int64\n", "GPF_DOCUMENT_FL 15223 non-null int64\n", "FACT_LIVING_TERM 15223 non-null int64\n", "WORK_TIME 13855 non-null float64\n", "FACT_PHONE_FL 15223 non-null int64\n", "REG_PHONE_FL 15223 non-null int64\n", "GEN_PHONE_FL 15223 non-null int64\n", "LOAN_NUM_TOTAL 15223 non-null int64\n", "LOAN_NUM_CLOSED 15223 non-null int64\n", "LOAN_NUM_PAYM 15223 non-null int64\n", "LOAN_DLQ_NUM 15223 non-null int64\n", "LOAN_MAX_DLQ 15223 non-null int64\n", "LOAN_AVG_DLQ_AMT 15223 non-null float64\n", "LOAN_MAX_DLQ_AMT 15223 non-null float64\n", "PREVIOUS_CARD_NUM_UTILIZED 288 non-null float64\n", "dtypes: float64(7), int64(32), object(4)\n", "memory usage: 5.0+ MB\n" ] } ], "source": [ "data.info()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This is how the data looks like. 43 columns and several of them have missing values.\n", "I'll do the following things:\n", "\n", "* drop several columns, where one of the values is too prevalent (has 95% or more). This is an arbitrary value and can be changed. The reason to do this is that if other categories in the variable have less that 5% in total and the target has ~11% positive response, than the variable will be hardly useful. Of course, maybe one of less common classes always has positive response (this needs to be checkes), in this case the feature should be used;\n", "* process continuous variables;\n", "* process categorical variables;\n", "* select variables and build the model;" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "FACT_POST_FL\n", "COT_PRESENCE_FL\n", "GAR_PRESENCE_FL\n", "LAND_PRESENCE_FL\n", "DL_DOCUMENT_FL\n", "PREVIOUS_CARD_NUM_UTILIZED\n" ] } ], "source": [ "for col in data.columns:\n", " if data[col].value_counts(dropna=False, normalize=True).values[0] > 0.95:\n", " if col == 'TARGET':\n", " pass\n", " else:\n", " print(col)\n", " data.drop([col], axis=1, inplace=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Continuous\n", "\n", "It is worth noticing that often it makes sense to create new variables from the ones already existing. While separate variables can have some impact on the model performance, their interaction may bring much more value. As an example I create a new variable as the value of income divided by the credit amount. If credit amount is much higher than income, there could be problems in paying it, if credit is many times lower, it could be of little interest to the customer. Of course, the dependences are more difficults, but you get the gist." ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": true }, "outputs": [], "source": [ "data['Income_to_limit'] = data['PERSONAL_INCOME'] / data['CREDIT']" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "And now there is a question about what to do with continuous variables. Usually I use them as they are, or use some kind of transformation (for example log) if necessary or normalize the values. But if the model needs to be interpretable, this won't do. The model should show how certain values impact the probability of positive response. So I'll split continuous variables into bins, so that each variable will have a separate coefficient in the model. I have written the function **split_best_iv** for this in [this file](https://github.com/Erlemar/Erlemar.github.io/blob/master/Notebooks/functions.py). It splits the continuous variable into bins to maximize IV (Information Value).\n", "\n", "What is IV? In fact it was and still is widely used in bank analysis. In simple terms it shows how useful is the variable for predicting the target. It is calculated in the following way (you can see an example below for \"GENDER\"):\n", "* For each category % of responders is calculated - how many people in the category have positive class;\n", "* The same is calculated for negative class;\n", "* WOE (Weight of Evidence) is calculated as logarithm of responders rate divided by non-resonders rate. WOE shows how good is the category in separating positive and negative outcomes. Also negative WOE shows that there are more non-responders, positive implies more responders;\n", "* Difference between distributions of positive and negative incomes is calculated;\n", "* IV for each category is a multiplication of the aforementioned difference and WOE;\n", "* IV for the variable is the sum of IV for each category;\n", "\n", "Rule of thumb for IV is the following:\n", "* < 0.02 - feature isn't useful for prediction;\n", "* 0.02 - 0.1 - weak impact on prediction quality;\n", "* 0.1 - 0.3 - medium impact;\n", "* 0.3 - 0.5 - strong impact;\n", "* 0.5+ - may cause overfitting;\n", "\n", "These aren't definite thesholds, but we should pay attention to them." ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "IV is 0.01.\n" ] } ], "source": [ "df = pd.DataFrame(index = data['GENDER'].unique(),\n", " data={'% responders': data.groupby('GENDER')['TARGET'].sum() / np.sum(data['TARGET'])})\n", "df['% non-responders'] = (data.groupby('GENDER')['TARGET'].count() - data.groupby('GENDER')['TARGET'].sum()) \\\n", " / (len(data['TARGET']) - np.sum(data['TARGET']))\n", "df['WOE'] = np.log(df['% responders'] / df['% non-responders'])\n", "df['DG-DB'] = df['% responders'] - df['% non-responders']\n", "df['IV'] = df['WOE'] * df['DG-DB']\n", "df\n", "print('IV is {:.2f}.'.format(np.sum(df['IV'])))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Back to the function. Function **split_best_iv** calls function **cont_split**, which tries to split the variable into bins. I use DecisionTreeClassifier for this, which is really great for the purpose. Interesting parameters:\n", "* criterion='entropy': to maximize information gain while branching trees;\n", "* min_samples_split=0.05, min_samples_leaf=0.05: so that there are at least 5% values in each category. The reasons for choosing this value were mentioned higher;\n", "* class_weight='balanced': great option for working with unbalanced classes;\n", "* max_leaf_nodes=leafs: how many categories will be created, more about this lower;\n", "\n", "After this I use **tree_to_thresholds** function to walk the tree and gather the thresholds for the decision rules. The code was adopted from [this](https://stackoverflow.com/questions/20224526/how-to-extract-the-decision-rules-from-scikit-learn-decision-tree) stackoverflow question. I round values, as having fractional age for example makes little sense.\n", "Then I calculate and save IV value.\n", "At the beginning there are 2 leafs. Then **split_best_iv** function increases number of leafs until IV stops increasing. This will be the optimal number of leafs and optimal split into the bins. The examples will be lower." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Outliers\n", "\n", "It is very important to deal with outliers. Some of the usual ways are:\n", "* Dropping rows with these values;\n", "* Replacing these values with more reasonable figures;\n", "* Building a separate model for them;\n", "\n", "I'll go with the first choice.\n", "\n", "To identify outliers I use either boxplots or simply look at the top values." ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYUAAAD3CAYAAADyvkg2AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nktQYCpKkxlCQJDWGgiSpMRQkSc3/BXh8jpFZRaZ9AAAAAElFTkSuQmCC\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "data['PERSONAL_INCOME'].plot(kind='box')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Boxplot shows that while median value is reasonable, max values are very high. In fact it is necessary to investigate whether these values are normal. Maybe they are VIP clients, maybe there is an error in the data, maybe this is completely normal or there could be some other reason. I have no additional data, so I'll just get rid of top-1% and low-1%." ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": true }, "outputs": [], "source": [ "data = data[(data.PERSONAL_INCOME < np.percentile(data.PERSONAL_INCOME, 99))\n", " & (data.PERSONAL_INCOME > np.percentile(data.PERSONAL_INCOME, 1))]" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "8984 2867959.0\n", "4296 10000.0\n", "2532 4320.0\n", "5375 3500.0\n", "9852 1500.0\n", "1092 1312.0\n", "11720 1254.0\n", "13928 1120.0\n", "9983 976.0\n", "10677 864.0\n", "10171 860.0\n", "676 780.0\n", "7711 730.0\n", "3323 612.0\n", "2983 600.0\n", "8864 540.0\n", "4122 528.0\n", "9536 528.0\n", "4571 519.0\n", "1068 516.0\n", "Name: WORK_TIME, dtype: float64" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data['WORK_TIME'].nlargest(20)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "I may believe that people work at the current place for 10, 30, maybe even 50 years. More is quite unlikely. I'll drop these values. There is a possibility to replace these figures with more adequate values, but there is enough data, so dropping is okay." ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": true }, "outputs": [], "source": [ "data.drop([8984, 4296, 2532, 5375, 9852, 1092, 11720, 13928, 9983, 10677, 10171, 676, 7711, 3323], inplace=True)" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "4124 140000.0\n", "14367 75606.0\n", "4874 75570.0\n", "4162 75500.0\n", "11300 70940.0\n", "Name: FST_PAYMENT, dtype: float64" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data['FST_PAYMENT'].nlargest()" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "485" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data.loc[data['FST_PAYMENT'] > data['CREDIT']][['CREDIT', 'FST_PAYMENT']][:10]\n", "len(data.loc[data['FST_PAYMENT'] > data['CREDIT']][['CREDIT', 'FST_PAYMENT']])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We see that there are 485 rows where initial payment is higher than the credit amount. This definitely isn't normal." ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": true }, "outputs": [], "source": [ "data = data.loc[data['FST_PAYMENT'] < data['CREDIT']]" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "6186 28101997\n", "12261 16091983\n", "8562 23916\n", "14739 7200\n", "988 6534\n", "12869 6336\n", "7650 3612\n", "12134 3228\n", "5681 3168\n", "11004 2520\n", "14707 1278\n", "12232 1000\n", "5369 980\n", "1420 890\n", "3789 720\n", "5888 720\n", "1937 708\n", "4463 700\n", "4705 696\n", "1013 684\n", "Name: FACT_LIVING_TERM, dtype: int64" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#Living in the place, months.\n", "data['FACT_LIVING_TERM'].nlargest(20)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "While it is possible that people can live in the same place all their life, I don't think that there are many people living for 100+ years :)" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": true }, "outputs": [], "source": [ "data.drop([6186, 12261, 8562, 14739, 988, 12869, 7650, 12134, 5681, 11004, 14707], inplace=True)" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "((14276, 38), 1720)" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data.shape, np.sum(data['TARGET'])" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#This will be used lated.\n", "initial_data = data.copy()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "947 values were dropped, but only 92 of them had positive response. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### PERSONAL_INCOME" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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PRTOBdRGxIP/skjQFWAnMJS3yvlZSK7ACOBYR84HtwOp8jE2k9YHnAbNzqJiZ2TU0kJvA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\nZlao/ws0q3OKH35uMwAAAABJRU5ErkJggg==\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "data['PERSONAL_INCOME'].plot(kind='box')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It is time to try splitting the variable." ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(0.0, 7600.0] 0.180513\n", "(9300.0, 11000.0] 0.161600\n", "(15300.0, 20800.0] 0.151863\n", "(11000.0, 14800.0] 0.140866\n", "(7600.0, 9300.0] 0.131690\n", "(20800.0, 44000.0] 0.118941\n", "(14800.0, 15300.0] 0.114528\n", "Name: PERSONAL_INCOME, dtype: float64\n", "IV: 0.0910365540526\n" ] } ], "source": [ "data['PERSONAL_INCOME'] = functions.split_best_iv(data, 'PERSONAL_INCOME', 'TARGET')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Done, and there are two more functions. Second one was already used, it caculates IV. The first one shows the following things:\n", "* Counts of each category;\n", "* Normalized counts including missing values;\n", "* Graph with blue bars for counts and red line for mean value of target (or what percent of values in category have positive income);" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Counts:\n", "PERSONAL_INCOME\n", "(0.0, 7600.0] 2577\n", "(7600.0, 9300.0] 1880\n", "(9300.0, 11000.0] 2307\n", "(11000.0, 14800.0] 2011\n", "(14800.0, 15300.0] 1635\n", "(15300.0, 20800.0] 2168\n", "(20800.0, 44000.0] 1698\n", "Name: TARGET, dtype: int64\n", "Frequencies:\n", "(0.0, 7600.0] 0.180513\n", "(9300.0, 11000.0] 0.161600\n", "(15300.0, 20800.0] 0.151863\n", "(11000.0, 14800.0] 0.140866\n", "(7600.0, 9300.0] 0.131690\n", "(20800.0, 44000.0] 0.118941\n", "(14800.0, 15300.0] 0.114528\n", "Name: PERSONAL_INCOME, dtype: float64\n" ] }, { "data": { "image/png": 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G5xQRSYzcp0aS9+xT1Oy+J+X3P6Jt1kWSowrHaQVYoCOu6xLnAkW8BWQqsLnr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\nsOrdTaQYi1BAimvhBsIF9zTVOvd9idTaJwiX2yu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% responders% non-respondersWOEDG-DBIV
(11000.0, 14800.0]0.1395350.141048-0.010786-0.0015130.000016
(7600.0, 9300.0]0.1029070.135632-0.276123-0.0327250.009036
(20800.0, 44000.0]0.1808140.1104650.4927690.0703490.034666
(0.0, 7600.0]0.1174420.189153-0.476611-0.0717110.034178
(15300.0, 20800.0]0.1750000.1486940.1628960.0263060.004285
(9300.0, 11000.0]0.1447670.163906-0.124163-0.0191380.002376
(14800.0, 15300.0]0.1395350.1111020.2278640.0284330.006479
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" ], "text/plain": [ " % responders % non-responders WOE DG-DB \\\n", "(11000.0, 14800.0] 0.139535 0.141048 -0.010786 -0.001513 \n", "(7600.0, 9300.0] 0.102907 0.135632 -0.276123 -0.032725 \n", "(20800.0, 44000.0] 0.180814 0.110465 0.492769 0.070349 \n", "(0.0, 7600.0] 0.117442 0.189153 -0.476611 -0.071711 \n", "(15300.0, 20800.0] 0.175000 0.148694 0.162896 0.026306 \n", "(9300.0, 11000.0] 0.144767 0.163906 -0.124163 -0.019138 \n", "(14800.0, 15300.0] 0.139535 0.111102 0.227864 0.028433 \n", "\n", " IV \n", "(11000.0, 14800.0] 0.000016 \n", "(7600.0, 9300.0] 0.009036 \n", "(20800.0, 44000.0] 0.034666 \n", "(0.0, 7600.0] 0.034178 \n", "(15300.0, 20800.0] 0.004285 \n", "(9300.0, 11000.0] 0.002376 \n", "(14800.0, 15300.0] 0.006479 " ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ "functions.feature_stat(data, 'PERSONAL_INCOME', 'TARGET')\n", "functions.calc_iv(data, 'TARGET', 'PERSONAL_INCOME')[0]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "People with higher income tend to have higher positive response rate." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Age" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "data['AGE'].plot(kind='box')" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(42.0, 50.0] 0.180583\n", "(54.0, 67.0] 0.146820\n", "(0.0, 26.0] 0.135332\n", "(30.0, 34.0] 0.119992\n", "(26.0, 30.0] 0.118170\n", "(34.0, 38.0] 0.108854\n", "(38.0, 42.0] 0.101989\n", "(50.0, 54.0] 0.088260\n", "Name: AGE, dtype: float64\n", "IV: 0.123625857889\n" ] } ], "source": [ "data['AGE'] = functions.split_best_iv(data, 'AGE', 'TARGET')" ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Counts:\n", "AGE\n", "(0.0, 26.0] 1932\n", "(26.0, 30.0] 1687\n", "(30.0, 34.0] 1713\n", "(34.0, 38.0] 1554\n", "(38.0, 42.0] 1456\n", "(42.0, 50.0] 2578\n", "(50.0, 54.0] 1260\n", "(54.0, 67.0] 2096\n", "Name: TARGET, dtype: int64\n", "Frequencies:\n", "(42.0, 50.0] 0.180583\n", "(54.0, 67.0] 0.146820\n", "(0.0, 26.0] 0.135332\n", "(30.0, 34.0] 0.119992\n", "(26.0, 30.0] 0.118170\n", "(34.0, 38.0] 0.108854\n", "(38.0, 42.0] 0.101989\n", "(50.0, 54.0] 0.088260\n", "Name: AGE, dtype: float64\n" ] }, { "data": { "image/png": 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mJ5MyabwuARep41RApPY4Dvm33cmOBx/B/8vPZA7qS+Djj7xOJVHy/eufZAwZRIMxd+Cm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\nFRAREZE9LO47vHwW5GXNgsSICoiIiMg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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "IV: 0.123625857889\n" ] }, { "data": { "text/html": [ "
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% responders% non-respondersWOEDG-DBIV
(30.0, 34.0]0.1313950.1184290.1038930.0129660.001347
(50.0, 54.0]0.0686050.090953-0.281977-0.0223480.006302
(38.0, 42.0]0.1273260.0985190.2565020.0288070.007389
(26.0, 30.0]0.1470930.1142080.2530410.0328850.008321
(42.0, 50.0]0.1552330.184055-0.170313-0.0288230.004909
(0.0, 26.0]0.1790700.1293410.3253270.0497290.016178
(54.0, 67.0]0.0674420.157694-0.849388-0.0902520.076659
(34.0, 38.0]0.1238370.1068020.1479960.0170360.002521
\n", "
" ], "text/plain": [ " % responders % non-responders WOE DG-DB IV\n", "(30.0, 34.0] 0.131395 0.118429 0.103893 0.012966 0.001347\n", "(50.0, 54.0] 0.068605 0.090953 -0.281977 -0.022348 0.006302\n", "(38.0, 42.0] 0.127326 0.098519 0.256502 0.028807 0.007389\n", "(26.0, 30.0] 0.147093 0.114208 0.253041 0.032885 0.008321\n", "(42.0, 50.0] 0.155233 0.184055 -0.170313 -0.028823 0.004909\n", "(0.0, 26.0] 0.179070 0.129341 0.325327 0.049729 0.016178\n", "(54.0, 67.0] 0.067442 0.157694 -0.849388 -0.090252 0.076659\n", "(34.0, 38.0] 0.123837 0.106802 0.147996 0.017036 0.002521" ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" } ], "source": [ "functions.feature_stat(data, 'AGE', 'TARGET')\n", "functions.calc_iv(data, 'TARGET', 'AGE')[0]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Younger people take more credits, while only a fraction of elder people have positive response." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### WORK_TIME\n", "\n", "Time of work on the current workplace in months." ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#I assume that missing values mean that the person didn't work at all.\n", "data['WORK_TIME'].fillna(0, inplace=True)" ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 31, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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XYvd14PKIqAFXA++nHeBrgDXAF4HrOmbE0/6HdrwBnlfNrFcCzwM+C3yw49zdtJdRLhng\nuP8Z+Bvgr4HfA/4RuKHj+M0R8dsdj+/IzIcHeH9doJypa1Grfk3rv4DrgP/NzNO0l0nWAq8FHgJO\nRMTzZjz1pcB3q+3p5ZdfAQ4Az2TmjzrO3QC8FdgWEZcPaOjfA2oR8aJqrAdmHN+emes7/hh0DYRR\n17PBw7Rn6Hurx48DvwwsycwfAvcAOyJiOUBEXAJ8CPipNevMPAu8C3hbRPxax6HDmfk94Gbg/oh4\nzoDG/S/A3cATmek352lBGHU9GzxMe1b+BYDMfAY4Rns9ncy8B/ga8FhEPA58BviLzPzyzAtl5k+A\ndwL3RMTFM47tBr4C3Dugcd9P+/8AfuadNLSXX/Z1/PnEgO6pC5xfvStJBfGFUmmGiHgx8OlZDu3P\nzA8t9Hik+XCmLkkFcU1dkgpi1CWpIEZdkgpi1CWpIEZdkgryfwAX0YRMFDX6AAAAAElFTkSuQmCC\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "data['WORK_TIME'].plot(kind='box')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here I add another line. If variable has zero values, DecisionTreeClassifier has problems with it. I combine zero values with the nearest interval." ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(35.5, 53.5] 0.164122\n", "(151.0, 600.0] 0.158238\n", "(6.5, 21.5] 0.156276\n", "(53.5, 85.5] 0.149902\n", "(85.5, 151.0] 0.119011\n", "(21.5, 35.5] 0.109344\n", "NaN 0.086579\n", "(0.0, 6.5] 0.056528\n", "Name: WORK_TIME, dtype: float64\n", "IV: 0.075887395125\n" ] } ], "source": [ "data['WORK_TIME'] = functions.split_best_iv(data, 'WORK_TIME', 'TARGET')\n", "data['WORK_TIME'].fillna(data['WORK_TIME'].cat.categories[0], inplace=True)" ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Counts:\n", "WORK_TIME\n", "(0.0, 6.5] 2043\n", "(6.5, 21.5] 2231\n", "(21.5, 35.5] 1561\n", "(35.5, 53.5] 2343\n", "(53.5, 85.5] 2140\n", "(85.5, 151.0] 1699\n", "(151.0, 600.0] 2259\n", "Name: TARGET, dtype: int64\n", "Frequencies:\n", "(35.5, 53.5] 0.164122\n", "(151.0, 600.0] 0.158238\n", "(6.5, 21.5] 0.156276\n", "(53.5, 85.5] 0.149902\n", "(0.0, 6.5] 0.143107\n", "(85.5, 151.0] 0.119011\n", "(21.5, 35.5] 0.109344\n", "Name: WORK_TIME, dtype: float64\n" ] }, { "data": { "image/png": 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BkZhY/1EjIYGSq6/FqKgg5YlHnU4jIiJ7oAIiuKs/AdMzugaQ7UnpoMFUtmlLynNPY+zc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\n7RtImrX87gtg+13gGeA/Kw/X/YGBkma0/SJwMHB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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "IV: 0.0913808898649\n" ] }, { "data": { "text/html": [ "
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% responders% non-respondersWOEDG-DBIV
(85.5, 151.0]0.1029070.121217-0.163756-0.0183100.002998
(53.5, 85.5]0.1523260.1495700.0182560.0027560.000050
(151.0, 600.0]0.1052330.165499-0.452790-0.0602660.027288
(35.5, 53.5]0.2000000.1592070.2281140.0407930.009305
(0.0, 6.5]0.0994190.149092-0.405225-0.0496730.020129
(21.5, 35.5]0.1180230.1081550.0873120.0098680.000862
(6.5, 21.5]0.2220930.1472600.4108950.0748330.030748
\n", "
" ], "text/plain": [ " % responders % non-responders WOE DG-DB IV\n", "(85.5, 151.0] 0.102907 0.121217 -0.163756 -0.018310 0.002998\n", "(53.5, 85.5] 0.152326 0.149570 0.018256 0.002756 0.000050\n", "(151.0, 600.0] 0.105233 0.165499 -0.452790 -0.060266 0.027288\n", "(35.5, 53.5] 0.200000 0.159207 0.228114 0.040793 0.009305\n", "(0.0, 6.5] 0.099419 0.149092 -0.405225 -0.049673 0.020129\n", "(21.5, 35.5] 0.118023 0.108155 0.087312 0.009868 0.000862\n", "(6.5, 21.5] 0.222093 0.147260 0.410895 0.074833 0.030748" ] }, "execution_count": 33, "metadata": {}, "output_type": "execute_result" } ], "source": [ "functions.feature_stat(data, 'WORK_TIME', 'TARGET')\n", "functions.calc_iv(data, 'TARGET', 'WORK_TIME')[0]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### CREDIT\n", "\n", "Credit amount in roubles." ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 34, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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sjohHgINUm8sA64D7gCGqu48ebdg/SVIDTfcUPkYVAoe7bJLaO4E7D2vbB7x/ktqfAMua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\nAAAAAElFTkSuQmCC\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "data['CREDIT'].plot(kind='box')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Some of credits have much higher values than median, but maybe these are special kinds of credit, how which these amounts are normal." ] }, { "cell_type": "code", "execution_count": 35, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(9400.0, 14100.0] 0.200897\n", "(22500.0, 119700.0] 0.175960\n", "(0.0, 5400.0] 0.169585\n", "(14100.0, 18100.0] 0.127487\n", "(5400.0, 7200.0] 0.116629\n", "(18100.0, 22500.0] 0.110325\n", "(7200.0, 9400.0] 0.099117\n", "Name: CREDIT, dtype: float64\n", "IV: 0.0155129903385\n" ] } ], "source": [ "data['CREDIT'] = functions.split_best_iv(data, 'CREDIT', 'TARGET')" ] }, { "cell_type": "code", "execution_count": 36, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Counts:\n", "CREDIT\n", "(0.0, 5400.0] 2421\n", "(5400.0, 7200.0] 1665\n", "(7200.0, 9400.0] 1415\n", "(9400.0, 14100.0] 2868\n", "(14100.0, 18100.0] 1820\n", "(18100.0, 22500.0] 1575\n", "(22500.0, 119700.0] 2512\n", "Name: TARGET, dtype: int64\n", "Frequencies:\n", "(9400.0, 14100.0] 0.200897\n", "(22500.0, 119700.0] 0.175960\n", "(0.0, 5400.0] 0.169585\n", "(14100.0, 18100.0] 0.127487\n", "(5400.0, 7200.0] 0.116629\n", "(18100.0, 22500.0] 0.110325\n", "(7200.0, 9400.0] 0.099117\n", "Name: CREDIT, dtype: float64\n" ] }, { "data": { "image/png": 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ilentycgthUkbHmxnuE5rEr19TIbtOKd5BP2Ezh1Bvy/hfgcR6ncQnkULIisNy7hrIVJD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\nLJv+IbVeJG8DfYELsDDeXqp6c1j+aLpRArHXPepEGZ3jZZjH8DGgj4j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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "IV: 0.0155129903385\n" ] }, { "data": { "text/html": [ "
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% responders% non-respondersWOEDG-DBIV
(18100.0, 22500.0]0.1029070.111341-0.078774-0.0084346.643955e-04
(22500.0, 119700.0]0.2052330.1719500.1769420.0332835.889141e-03
(7200.0, 9400.0]0.1156980.0968460.1778570.0188523.352881e-03
(14100.0, 18100.0]0.1273260.127509-0.001438-0.0001832.633451e-07
(0.0, 5400.0]0.1523260.171950-0.121182-0.0196242.378077e-03
(9400.0, 14100.0]0.1790700.203887-0.129788-0.0248173.220936e-03
(5400.0, 7200.0]0.1174420.1165180.0078980.0009247.296327e-06
\n", "
" ], "text/plain": [ " % responders % non-responders WOE DG-DB \\\n", "(18100.0, 22500.0] 0.102907 0.111341 -0.078774 -0.008434 \n", "(22500.0, 119700.0] 0.205233 0.171950 0.176942 0.033283 \n", "(7200.0, 9400.0] 0.115698 0.096846 0.177857 0.018852 \n", "(14100.0, 18100.0] 0.127326 0.127509 -0.001438 -0.000183 \n", "(0.0, 5400.0] 0.152326 0.171950 -0.121182 -0.019624 \n", "(9400.0, 14100.0] 0.179070 0.203887 -0.129788 -0.024817 \n", "(5400.0, 7200.0] 0.117442 0.116518 0.007898 0.000924 \n", "\n", " IV \n", "(18100.0, 22500.0] 6.643955e-04 \n", "(22500.0, 119700.0] 5.889141e-03 \n", "(7200.0, 9400.0] 3.352881e-03 \n", "(14100.0, 18100.0] 2.633451e-07 \n", "(0.0, 5400.0] 2.378077e-03 \n", "(9400.0, 14100.0] 3.220936e-03 \n", "(5400.0, 7200.0] 7.296327e-06 " ] }, "execution_count": 36, "metadata": {}, "output_type": "execute_result" } ], "source": [ "functions.feature_stat(data, 'CREDIT', 'TARGET')\n", "functions.calc_iv(data, 'TARGET', 'CREDIT')[0]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### TERM\n", "\n", "Credit length. I think in months." ] }, { "cell_type": "code", "execution_count": 37, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 37, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "data['TERM'].plot(kind='box')" ] }, { "cell_type": "code", "execution_count": 38, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(4.5, 8.5] 0.410759\n", "(11.5, 36.0] 0.241454\n", "(8.5, 11.5] 0.209793\n", "(0.0, 4.5] 0.137994\n", "Name: TERM, dtype: float64\n", "IV: 0.032100382616\n" ] } ], "source": [ "data['TERM'] = functions.split_best_iv(data, 'TERM', 'TARGET')" ] }, { "cell_type": "code", "execution_count": 39, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Counts:\n", "TERM\n", "(0.0, 4.5] 1970\n", "(4.5, 8.5] 5864\n", "(8.5, 11.5] 2995\n", "(11.5, 36.0] 3447\n", "Name: TARGET, dtype: int64\n", "Frequencies:\n", "(4.5, 8.5] 0.410759\n", "(11.5, 36.0] 0.241454\n", "(8.5, 11.5] 0.209793\n", "(0.0, 4.5] 0.137994\n", "Name: TERM, dtype: float64\n" ] }, { "data": { "image/png": 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T15GkCdElGBGRJsgpLaGoR1fSvv2asmuup7JXH68jSROjAiIi0tRUV1PYtycZH31ARa8+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\n5p1FdJ4+irRQkOe7XsFb7c/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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "IV: 0.032100382616\n" ] }, { "data": { "text/html": [ "
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% responders% non-respondersWOEDG-DBIV
(4.5, 8.5]0.3680230.416614-0.124013-0.0485900.006026
(11.5, 36.0]0.3017440.2331950.2577030.0685490.017665
(8.5, 11.5]0.2197670.2084260.0529850.0113410.000601
(0.0, 4.5]0.1104650.141765-0.249470-0.0313000.007808
\n", "
" ], "text/plain": [ " % responders % non-responders WOE DG-DB IV\n", "(4.5, 8.5] 0.368023 0.416614 -0.124013 -0.048590 0.006026\n", "(11.5, 36.0] 0.301744 0.233195 0.257703 0.068549 0.017665\n", "(8.5, 11.5] 0.219767 0.208426 0.052985 0.011341 0.000601\n", "(0.0, 4.5] 0.110465 0.141765 -0.249470 -0.031300 0.007808" ] }, "execution_count": 39, "metadata": {}, "output_type": "execute_result" } ], "source": [ "functions.feature_stat(data, 'TERM', 'TARGET')\n", "functions.calc_iv(data, 'TARGET', 'TERM')[0]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### FST_PAYMENT\n", "\n", "Initial fee amount in roubles" ] }, { "cell_type": "code", "execution_count": 40, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 40, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYAAAAD3CAYAAAAUl4NyAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\noQwASSqUASBJhfp/y5e6yoCOvVsAAAAASUVORK5CYII=\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "data['FST_PAYMENT'].plot(kind='box')" ] }, { "cell_type": "code", "execution_count": 41, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(2100.0, 3800.0] 0.195853\n", "(200.0, 900.0] 0.145139\n", "(3800.0, 6000.0] 0.134071\n", "(900.0, 1200.0] 0.118801\n", "(1700.0, 2100.0] 0.115158\n", "(1200.0, 1700.0] 0.105492\n", "(6000.0, 75600.0] 0.099958\n", "NaN 0.082726\n", "(0.0, 200.0] 0.002802\n", "Name: FST_PAYMENT, dtype: float64\n", "IV: 0.025642074029\n" ] } ], "source": [ "data['FST_PAYMENT'] = functions.split_best_iv(data, 'FST_PAYMENT', 'TARGET')\n", "data['FST_PAYMENT'].fillna(data['FST_PAYMENT'].cat.categories[0], inplace=True)" ] }, { "cell_type": "code", "execution_count": 42, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Counts:\n", "FST_PAYMENT\n", "(0.0, 200.0] 1221\n", "(200.0, 900.0] 2072\n", "(900.0, 1200.0] 1696\n", "(1200.0, 1700.0] 1506\n", "(1700.0, 2100.0] 1644\n", "(2100.0, 3800.0] 2796\n", "(3800.0, 6000.0] 1914\n", "(6000.0, 75600.0] 1427\n", "Name: TARGET, dtype: int64\n", "Frequencies:\n", "(2100.0, 3800.0] 0.195853\n", "(200.0, 900.0] 0.145139\n", "(3800.0, 6000.0] 0.134071\n", "(900.0, 1200.0] 0.118801\n", "(1700.0, 2100.0] 0.115158\n", "(1200.0, 1700.0] 0.105492\n", "(6000.0, 75600.0] 0.099958\n", "(0.0, 200.0] 0.085528\n", "Name: FST_PAYMENT, dtype: float64\n" ] }, { "data": { "image/png": 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JHow7rbVlt2rsARoBDYFd5ZoeaHvZtkoVFOTj9+ccdv7qat68gTcnvqAHnH46wdmv0Nxf\nAgUFlTYJBKv2o092f8++B4chEzMnI1uvCzLr2vRn7T8yMXOysvnaUiVlC5EZY44DpgMPWGufMcb8\ntdzHDYCdwO7S14faXratUjt2FB5u7Gpr3rwB27bt8ez8eZeNoP4dv2PPI08SvnJ8pfsnu7AYVG0h\nMi+/B9Xh9c8tVbL1uiDzrk1/1hIy7edWFV5eWyYXPikZgjHGtADmAL+y1j5WunmNMaZX6ev+wCJg\nBdDdGBMyxjQCTicxQXUJcFGFfeUQIpcPw3UcQlqaXUREMkCq5oD8BigAbjXGLDDGLCAxDHO7MeZN\nIABMtdZuAe4jUWC8DvzWWhsGHgTONMYsBq4Bbk9RzqwRP/oYinv0InfVCnI+2Ox1HBERkUNK1RyQ\nG4AbDvBRzwPs+zDwcIVthcDQVGTLZuHhowi8MZ/g5Gcp/PVtXscRERE5KC1ElkUi/QcQr1ef0JRJ\nEI97HUdEROSgVIBkk3r1iFwymJzPPyN36WKv04iIiByUCpAsE9m/NLsmo4qISPpSAZJlirt0JXbc\n8QRmvAj79nkdR0RE5IBStg6IeMTnIzx0OPXu/hvBV2YQGTrC60QikqXunbIu6X2rssbJDUPbVDeS\nZBD1gGSh/cMwWppdRKTOMsb4jDEPGWPeLF0S45QD7JNvjFlijDmtwvYjjDGfVdxek1SAZKHYSadQ\nfE4nchctwPfF517HERERbwwGQtbaLsDNwF3lPzTGdAAWAidX2J4L/AsoSmU4FSBZKjxsJI7rEpw2\n2esoIiLijf0PdrXWLgM6VPg8CFwKbKqw/U7gIeDLVIZTAZKlIoMuxQ0GCU16Bly38gYiIpJtKj7w\nNWaM2T/301q7xFr7WfkGxpgrgW3W2tmpDqcCJEu5jQuI9L0I/+b38K99y+s4IiJS+yo+8NVnra1s\nJvA44MLSR6i0BSYYY45MRTgVIFksMrxsMuozHicREREP7H+wqzGmM7ChsgbW2h7W2p7W2l7AWmBM\n6XPbapwKkCwW7dWbeLPmBKdPhWjU6zgiIlK7pgNhY8xS4B7gRmPMKGPMNR7nArQOSHbLzSV82TDy\n//VPAnNnE714oNeJRESkllhr48B1FTZXnHBKaW/HgdofcHtNUQ9IlgsPHwVoGEZERNKLCpAsF2vV\nmpIzWhGYNxtn+3av44iIiAAqQOqE8PBROCUlBKdP8TqKiIgIoAKkTggPGYqbk6Mn5IqISNpQAVIH\nuC1aED2vN7lr15Bj/2v+kYiISK1TAVJH7H9AnXpBREQkDagAqSMi/S4m3rARwSnPQSzmdRwREanj\nVIDUFaEQkUFDyNnyFbkLF3idRkRE6jgVIHVIWMMwIiKSJlSA1CElHTsRO+FEgq/MIFi0z+s4IiJS\nh6kAqUsch/CwkThFRZy5doHXaUREpA5TAVLHhIeOAKDd8lkeJxERkbpMBUgdE295AtEuXTlx81oa\nb//K6zgiIlJHqQCpgyKlD6hru2K2x0lERKSuUgFSB0UGDiKaG6Tt8tngul7HkRqWu3QxDB+O7/PP\nvI4iInJQKkDqILdBQza26UGzbZ9z3Edvex1HalDoqSdpdPklMHky+ffd7XUcEZGD8nsdQLyxpnM/\n2qyay9nLZvHZSa29jlOj7p2yLul9A0E/0UhJUvveMLRNdSOlXixGvd/fRv6D/yBeUICTm0twyiT2\n3fZ73PoNvE4nIvJf1ANSR31g2rO7UTNavfU6/uKI13HkMDh799Bw7EjyH/wHJad+jx2z5sMPf4hv\n316Cz0/1Op6IZDvHqYfjnIXjODhOvWSbqQCpo1xfDms79iGvaC+nrV/idRypJt9nn9L44j4E58wi\n2ut8dr4yj/iJJ8H48bg5OYSefEzzfEQkdRynN7AOeBE4EvgYx+mTTFMVIHXY2o79AGi7QmuCZCL/\nyuUU9D0P/8Z3KBp3NbuemYrbqHHiw2OOIdr3InI3rMO/ZrW3QUUkm/0J6AbsxHW/AnoCf0umoQqQ\nOmzr0SfyxfGGU99dQb3d33odR6ogOHUSjYcMwNnxLXv+fCd7/3IX+L87pato7DiARC+IiEhq+HDd\nLfvfue67yTbUJNQ6bk2nfgz49F7arJzD0t4jvI4jlYnHyf/rH6l399+IN2zE7glPUHxe7wPuWtzz\nPGItTyD0wjT23f5H3MYFtRw2eXVy4rBIdvgcxxkAuDhOY+CHwKfJNFQPSB23vn1vYr4c2i3XomRp\nr7CQhldfSb27/0as5QnsfGXeQYsPAHw+isaMwykqIjTludrLKSJ1ybXA94HjgA+AtsDVyTRUAVLH\nFTYowLbqwlFfvM+Rn7/vdRw5CN+Wr2g8uD/BGS8Q7dKVHbPmE/ueqbRdeORo3NxcQhMe12RUEUmF\nNrjuSFy3Oa7bFNcdCpybTEMVIMLaTqWTUfWAurTkX7+Wxn16kbt2DUUjR7Nryou4TZsm1dZt1ozI\nwEH47SZyl7+Z4qQiUmc4znAcZwzwMI4zptzXOOCvyRxCBYhgz+xCYb2GtFk5F18subF1qR2BmS/R\neGBffF9vYe9tf2Dv3/8JgUCVjhEeOx6A0BOPpiKiiNRNDYHzgAal/y376gL8NpkDJF2AOA5Hlf63\nu+PwQ8ch6cVGJL3FcgOsb9+bBnu+5ZSNK72OIwCuS969d9Fo3GhwfOx+8lmKfnQDOE6VD1Xc+VxK\nvmcIznwR55tvUhBWROoc130Y170KGIbrXlXu62pcd1Iyh0iqAHEcHgRucRzOAJ4BzgYmVDu4pJ01\npcMw7TQM471IhAY/vo76f7yd2DHHsmPmHKL9Lqr+8RyH8NhxONEooeeerrmcIiIQwXFexHFew3Fe\nx3HewHE+TqZhsj0gHYEfAcOAR12X8cDx1csq6eiLlqezrcXxnLZ+MaHCPV7HqbOcb76h8WUDCU1+\nluL2HRKTTVsd/rN6wkNH4OblkTfhMYjHayCpiAgAjwAvkFjW45/AZmB6Mg2TLUBySvcdBLzqOOSD\nhmCyiuOwplM/ckuitHprvtdp6qScje9S0O88clcsIzzkcnY+/zJuixY1cmy3cQHhwZeR8/FH5C5c\nUCPHFBEBinDdx4EFwA4St+D2TKZhsgXIBOAr4GPXZTmwGvhX1XNKOlvXsQ9xx6Hd8le9jlLnBObN\npvHFF5Lz6Sfs++Vv2PPgo5CXV6PnCJeujJqnlVFF6gRjjM8Y85Ax5k1jzAJjzCkH2CffGLPEGHNa\n6fscY8xjpdsWG2NaVXKaMI7TBLBAZ1zXJckOimQLkNnAUa7LpaXvuwPLk2wrGWJXQQs++t7ZtPzw\nbZps/dzrOHWD65L3r3/ScPRwnJJidj/8BIU33VytyaaVKWnXnuLWbQjMehnflq9q/PgiknYGAyFr\nbRfgZuCu8h8aYzoAC4GTy20eCGCt7QrcAvyxknPcBUwCZgBjcJx3gFXJhDtkAeI4dHUcepAYzznX\ncehR+v4sNAk1K+2fjKoH1KVecTH1b/op9W/9NfFmzdn5witEBg1J3fnKJqPGYoSe1h9fkTqgGzAL\nwFq7DOhQ4fMgcCmwqWyDtfYF4JrSty2BnZWcowjog+vuAdoDo4ErkglXWQ/IhcDtwFHA70tf3w78\nGg3BZKV32/QgEsij7Yo5OJqsmDLOjm9pNGIIeRMfp7jVWeycPZ+Ssyv+bqh5kSGXE6/fgNDEJ6BE\na76IZLmGwK5y72PGmP3PgLPWLrHWflaxkbW2xBjzJPAPoLJb5/5aOuwCrrsP112D6yb1l8chH0bn\nuvwvgONwhesyMZkDlmeM6QT8n7W2lzGmHTCTxAxZgAettZOMMVeTWEu+BLjDWjvTGJMHPAUcAewB\nxlprt1X1/FJ10VA+77TrydnLZ9Hyg3V8fGo7ryNlnZwPNtPw+8Pwf/gBkf4D2P3Pf0P9+rVybrd+\nAyKXDSPvyUcJvDaXaN/+tXJeEfHEbhILhZXxWWuT+peHtXasMeZXwHJjzBnW2n0H2fUDHOcxEtMy\nivZvdd1Ku1mTnQOy0HH4m+PwqOPwWNnXoRoYY35J4vacUOmm9sDd1tpepV+TjDFHAj8BugJ9gT8b\nY4LA9cAGa213EkM9tySZU2pA2dLs7ZZpGKam5S56g8b9e+P/8AMKf3wjux9/qtaKjzJFpZNRQ09q\nZVSRLLcEuAjAGNMZ2FBZA2PMFcaYX5e+LQTipV8Hsx1wgM78ZzXUXsmEO2QPSDmTgUWlX8k+0eoD\nYAjs7zlpDxhjzCASvSA/JbG+yBJrbQSIGGPeJzG/pBv/WUv+VeDWJM8pNeCjU9uxs6AFrdbMZ+bw\nGyFYu39BZqvQhMepf/PPwXHYfd+DREZ835McsVatKW5/DoHX5uL79BPix7f0JIeIpNx04EJjzFIS\nRcJVxphRQH1r7b8P0uZ54HFjzEIgF/iptbboIPtSuhpqtSRbgOS6LjdV5cDW2mnGmBPKbVoBPGKt\nXW2M+S3wO2At3x2f2gM04rvjVmXbKlVQkI/fn1OVmDWqefMGle+UJgLBQ//oN5zbn+4vP0Hrd5bw\ndue+le5fJh2+B8lmrer+1b62WAxuugn+/ndo2hSmT6dh9+7VOtTvH11WvQwVnHVWXwavXsm7t91J\n95efqJFjHo60+5nVIF1b1fdPh2urqnTMbK2NA9dV2LzpAPv1Kvd6H4lFR1Mu2f97FjsOA4HZrku0\nmueabq0tm007ncTkloV8d3yqAYkZt+XHrcq2VWrHjsJqRjt8zZs3YNu2zFlBNBo59DDg6vYXJgqQ\nJS/zdue+le5fJh2+B8lmhcQvxFRem7NnNw2uHUdw3hxKzGnsmjiJ+AknQjW/T8lmrey61p7Vi755\nf6ftohls+2J7lR9wV9PS6WdW05LNevQnmzhyxxe81bZ3Uvtn0rVB5v3cqsLL3//pWPgkK9k5IJcD\nLwJhxyFe+hWr4rlmG2M6lr7uTWIxsxVAd2NMyBjTCDgdeJty41ZAfxJDP1KLvmlxPJ+eeCYnb1pN\ngx1bvY6TkXyffkLjAX0IzptD9PwL2Pny3ETxkQZKAkHe6tyfBnu+JTDrZa/j1Hk5xVFGPfxbhjz8\nvzTb8onXcURqRVIFiOtytOviq/BV1bGO64F7jDELSEw6vcNauwW4j0SB8TrwW2ttGHgQONMYs5jE\n/ci3V/FcUgPWduyLz43T68WHqb9LT1GtCv/yZRT0Ow//xncpvPo6dj01GbdhUiOJtWZlt0GAVkZN\nB+2Wz6JxaaHfduUcj9OIVIHj9MVxVuE4H+A4H+I4H+E4HybTNKkhGMfhtgNtd11+f6h21tqPScyM\nxVr7FonCo+I+DwMPV9hWCAxNJpukzvoOF3DeK0/QbvFM2ix5hQ9Oa8/ajn15t00PioM1u0x4NglO\nfpYGP/sxxGLs+es9hK8c73WkA/rmyJZ8eGo7Tlr0BjnvbyZ2yqleR6qTckqK6Tl7IsW5AeI+P22X\nz+a1i8fj+pLtoBbx1D+An5EYvUj2JhUg+Tkg5deFzgX6oaXYs144vwH33TKBduvm03rpq5y6cSWn\nblxJJJDHu217sLZjXz40Z+P6vJv4m1bicer9+Q/k33sX8UaN2f3IkxT3PM/rVIe0ovsgTtq8htCE\nx9n3+z95HadOart8FgXfbmFpr8vJKwnTbvFMrcEjmeQbXHdmdRomVYC47neHQByHPwDqJ6wDiuo3\nYtX5l7O062Cafv0pbVfOoc2KObRbMZt2K2azu1Ez1nW4gHUd+7Ll2P96zlHdsW8fDX90LcGXX6Lk\nxJPY/fSUjOhR2NimB/FmzQhNepp9v7kNQqHKG0mN8cVKEr0f/gCLLvw+R+74knaLZ9Ju+WwVIJIp\nFuE4d5NY8j28f6vrLqysYdXuofqP+sDx1WwrGWp7i+N5bcAPeO3i8bT8YANtV8ym1Vuv0/215+j+\n2nNsOfok8r6+ishlQ4kfdbTXcWuN76svaXjFCHLXryXatTu7H52A26Sp17GSEvPnEh41hvz77iY4\n4wUiQ0d4HalOabtiNk22f8WyHkPY07gZkeZHsLOgBWeumc/MYT+lOKCCUNJe2c0l5StmFzi/soZJ\nDTI6Dh85Dh+Wfn1MYpGxR6qaUrKE4/DJKWfx4qhf8Jc/v8gzV9/Bu2260+zrT6n/+1tp0vZ0Gl0+\niOCkZ2DrjQDYAAAgAElEQVTvXq/TppR/7Vs07tOL3PVrKRo9ll2TpmdM8VGmaPRYXMfRZNRa5ouV\n0HPWREr8uSzsU7oonc/H2o59CIULOW39Ym8DiiTDdc87wFelxQck3wPSq/zpgJ2uy+6q5pTsE8sN\n8G7bnrzbtid5e3fxE94jNOU5AgvnE1g4H/dXPyPSfwDhoSMo7tEL/NXtdEs/gRkv0PBH10I4zN7b\n/0TRdT8Ex6m8YZqJn3Aixef1JvD6PHLefYfYGWd6HalOOGvVPJp+8wXLuw9md8ER+7ev7diXXrMn\n0nb5bDZ0uMDDhHXbvVPWJb1vVdY4uWFom+pGSk+O0w34BYmREQfIAVriuidU1jTZadafkliX4y4S\nt81e6ThJt5U6oqh+I8JX/YCdr8xj+7I17LvpZuLNjyA0bTKNRwyhSdvTqXfbb8jZsB7cKk2WTi+u\nS/7df6XR+DG4vhx2T3yOout/lJHFR5misYk7dfImqBekNvhiJfSa9SQlOX4W9hn9nc++ObIln7U8\nnVM3rqD+ru0eJRRJ2iPACyQ6NP5J4lEr05NpmGwR8VcSD4ubADxOYmzn7irHlDojftLJFP7yN3y7\nYh07Zs6laOx4nGiE/Ifup0nvbhT06kLeP/6O78svvI5aNeEwDf7naur95Q5ixx7HzplziPbJ/CfK\nRi/sS+yoowlOfi7rh83SQevVr9Fs6+es6XwRu5q0+K/Py9bgOWv1PA/SiVRJEa77OLAA2AFcDfRM\npmGyBUgfYIjr8pLr8iKJlVH7ViOo1DWOQ0nHTuz92z1s37CZXY8/TeSigeS8v5n6f7iNJu3OoNFl\nAwk+9zTO3vReftnZupXGQwYQmjaZ4g4d2TFrPrEzW3kdq2b4/YRHj8W3dw+hF6Z5nSarOfEYvWZN\nIObL4Y2+ow+4z4YOvYn5cmi7fHYtpxOpsjCO0wSwQGdc1wXqJdMw2QLEz3fni/ihykuxS10XDBK9\neCC7n3ia7W9vZs/f/k7JOZ0ILHqDhj+5nqZnnkKD68YReG0OlCT/jIna0OKLDyjodx65q1YQvmwY\nO5+fiXvEEZU3zCDh0WNxc3IIaTJqSrV+az7Nv/6UNZ37sbPpUQfcp7B+Y2yrLhz9+WZafPFBLScU\nqZK7gUnADGAMjvMOsCqZhskWIE8DCxyHHzsOPyaxbPoz1UkqAuAWNCE8dhw7Z85h+4p17Pvlb4gd\neRSh56fSaOTlNG1zGvVuvRn/+rWezxcxG5ZwzV3Xk/P5Z+z79a3seeDhrFwvI37U0UT79Cd33Rr8\na9/yOk5WcuIxer36RGnvx5hD7ru2Y6KTue0K9YJIGnPdKUAfXHcP0B4YDVyRTNNKCxDHoYDEUul/\nILH2x5XAg66Llk2UGhE/4UQKb7qZHcvWsOOVeRRd9QMoKSb/Xw9QcEEPCnp0Iu++u/F98XntBnNd\nzn3tOb7/r1/jxOPsenQChTf+IqMnm1amaOw4APWCpMiZa97giC2fsLZjX3Y0O/RaObbVuRTl1afN\nyrk4cXU4S5pynALg3zjO60AI+DGQ1IOvDlmAOA7tgHeB9q7Lq67LL4DZwF8ch7MOL7VIBY5DSYeO\n7P2/uxPzRZ58lsiAQeR89CH17/hfmpx9Jo2GDCD47FM4e1J7F3hOSTGDn/krFz3/T/Y2bMojN95P\ndODglJ4zHRT3Op/Y8ScQmj4VZ9dOr+NkFSce57z9vR+V/wMxlhtgQ/veNNz1DSdZ9UhJ2noYWAk0\nBfYAXwFPJdOwsh6QO4GRrsussg2uy2+AceguGEmlQIBo/4vZ/djExHyRO++lpGNnAosX0vCG/0nM\nF7n2KgJzZ0FxcY2eOm/vLsbe/3M6LJ3JF8d9jwd/+W++bHlajZ4jbfl8FI25EqewkODUSV6nySpn\nrH2DFl99xPpzLuTbI45Nqs2aTolhmHbLZ1Wyp4hnTsR1/w3Ecd0orvtbIKn/wSsrQApclwUVN7ou\ns4FmVY4pUg1u4wLCY65i54zZbF+5nn0330LsmGMJTZ9Go+8Po2kbQ73f/jIxb+Ew54s02/IJ1955\nHSdtXsM7bXvyyI33s6dx8xq6kswQHjEaNzc3sTJqJq/XkkaceJxes54k7vhYkETvR5nPTmzF9mbH\ncMa6hQTChSlMKFJtJThOI8qehOs4pwLxZBpWVoDkHmjBsdJtgSqGFDls8ZYnUPizX7Jj6Wp2zHqd\novHXJBYGe/ghCvr0oqDbOfScNYHG27dU+dgnb1rFtXdeR7Ntn7Og7xU8N/73FAfzUnAV6c094ggi\nFw/Ev2kj/uXLvI6TFU5bv5ijvviA9R16s71FFR6j5Tis7diXQDTMGesqfbaXiBd+R2INkJY4zgvA\nYuCWZBpWVoC8UXrwim4hydtsRFLCcSg5uwN7/3wn29e/x66Jkwhfcik5n37ChTMe5qbbhjL+7z+m\n/dKZBIsqX1ir48LpjPnnTeQWR5g65rfMu+QaXF/dXew3XLYy6pOPepwkC7gu5736BHHHYUG/sVVu\nvlbDMJLOXHcWcCEwBngMOAvXfTmZppU9mOPXwCuOw/dJTDJxgLOBrcAl1Q4sUpNyc4n27U+0b3/2\n7trJot/dT9sVszlx81pO3LyWAZPvYVPrbqzt2IfNZ3QinvOf/+2dWAkXT/47Xd6Yxt76jXnmmj/y\n6cmaX118bjdKTjmV4IwX2HvH/+E2zawH7KWT09Yv5ujPN7OuwwV8c2TLKrff0exoPjmpNSe+9xYN\nd2z9znNjRDzjOAe7j7wvjgOuO6GyQxyyAHFd9jgOPYDzSDxqNw7803VZVOWwIrXAbdSY1V0Hsrrr\nQBpv/4o2K+fSZsUcWr/1Oq3fep299RuzoX1v1nbqyzdHHMfIx/+XU95ZztdHncjE6//voAtD1TmO\nQ3jsOOrf+mtCzz1N0Q9/4nWizPSd3o9Dr/txKGs69aPlhxtos3Iui8qenCvirSdIdEbMA6IkOijK\nuCQe3XJIlT6a1HVxSSw89nq1Iop4ZGfTo3ij3xje6HsFR39qabtiNmetmkeXN6bR5Y1pRHODBIoj\n2DM7M/mq/yWSl9TqwXVGeNhI6v3xdkITHks8bK8OD0lVV2DuLJp/9h4bzj6fbUedWO3jvH32eVw8\n5V7aLp/FogtHZfVaNJIxzgaGkxh+WQc8B8zDdZOagArJr4Qqkrkchy9bnsYrQ2/gr3+azoTr/4/1\n7c/H9fl488KRPHXdX1R8HIBb0ITIoCH4P/qQ3EVveB0n87gu+Xf+BYD5/as+96O8cH4DbOtzabHl\nY4767L2aSCdyeFx3La77a1y3A/AgiUJkBY7zEI7TK5lDVNoDIpJN4jl+3mt1Lu+1OheAQNCPG0mv\n586kk6Kx4whNeoa8Jx+juOd5XsfJKIHX55K7dg1vt+3J1qNPOuzjrenUj1ZrFtBuxWy+Ot7UQEKR\nGuK6q4BVOE534C8klmOvX1kz9YCIyEGVtD+HkjNbE3h1Jr4tX3kdJ3OU7/246MoaOeTmMzqxr34j\nzlo5F19MRbOkAcdxcJyeOM79OM4HwE+BfwAtkmmuAkREDs5xKBo7DicWI/TMRK/TZIzc+a+Ru3oV\nkYsG8vUxp9TIMeM5fta3v4D6e3dyysYVNXJMkWpznAeBD4EbSKz9cRauexmu+xyuuy+ZQ6gAEZFD\nilw+jHi9+oSeehJieihapVyXeqW9H4U//2WNHnpNp34AtFuuJ+SK564lMczSDvgzsAHH+XD/VxI0\nB0REDsmt34DIZcPIm/AYgdfnEr2wn9eR0lruwgXkrlpBpN9FlLRuA5vW1dixvzzesLVFS05bv5hd\nu3biNmpcY8cWqaLq39ZVSj0gIlKp8NirAAg9+ZjHSdLcd3o/flXzx3cc1nbqS25JlOCMF2v++CLJ\nct1PDvmVBPWAHMK9U5L/l0sg6Cea5N0UNwxtU91IIp4oad2G4vYdCMydje+zT4kfV4XnmdQhuUsW\nkbv8TSIX9qWkTbuUnGPdOX24YMbDBCc/S3j04d3eK9nNGOMDHgDaABHgB9ba9yvskw/MBcZbazcZ\nY3JJLKl+AhAE7rDWvpSKfOoBEZGkFI0dj+O6hJ5+0usoaavszpfCm25O2Tl2NWnBx6e2I7BsKb5P\nPk7ZeSQrDAZC1touwM3AXeU/NMZ0ABYCJ5fbPBrYbq3tDvQD7k9VOBUgIpKUyCWXEm/UmNBTE6C4\n2Os4aSd36WICSxcT6X0hJe3ap/RcazomHlAXmjoppeeRjNcNmAVgrV0GdKjweRC4FNhUbtsU4NbS\n1w6Qsnu+VYCISHLy8wkPH0nO1q8JzHrF6zRpJ/+u/wNSNPejgnfa9cLNyyM4+Vlw3ZSfTzJWQ2BX\nufcxY8z+qRfW2iXW2s/KN7DW7rXW7jHGNACmArekKpwKEBFJWnjMOADyNBn1O/zL3iSw6A2ivc6n\npEPHlJ8vGson0n8A/o8+xL96ZcrPJxlrN9Cg3HuftbbSHg1jzHHAfGCitfaZVIVTASIiSYt9zxDt\n0pXAwvnkfPh+5Q3qiLI7X/bd9OtaO2d42EgAQpOfrbVzSsZZAlwEYIzpDGyorIExpgUwB/iVtTal\n/9JQASIiVRIem+gFCU14wtsgacK/YjmBhfOJdu9FScdOtXbe4h69iB3RguCLz0MkUmvnlYwyHQgb\nY5YC9wA3GmNGGWOuOUSb3wAFwK3GmAWlX3mpCKfbcEWkSiIXX0K8aVNCzz3FvptvgVDI60ieqndX\n6Z0vv0jdnS8H5PcTuWwY+Q/+g8C8OUQvHli755e0Z62NA9dV2LzpAPv1Kvf6BhLLq6ecekBEpGqC\nQcIjr8D37bcEZ9btxbD8q1cSmP8a0W49KO58bq2fX8MwkslUgIhIlRVdcSWgyai1eefLgcTObEXJ\nGa0IzJuN8+12TzKIVJcKEBGpsviJJxHtdT65y98kZ+O7XsfxhH/NaoLz5hDt0pXirt09yxEeNhKn\nuJjgC897lkGkOlSAiEi1FI0dD0DehLrZC7K/9yOFq54mI3LZUFyfj9AUDcNIZlEBIiLVEu3Tj9iR\nRxGc/Bzs2+d1nFrlX7+W4JxZFHfqQnG3Hp5mibc4kuKe55G7ehU5H2z2NItIVagAEZHqyc0l/P0x\n+PbsJvTCNK/T1Kr8OxO9H/t+/itwHI/T/GcyanDKcx4nEUmeChARqbbw6LGJ7v8nH/U6Sq3J2bCe\n4KyXKe7QkeKe53kdB4BI/wHE69UnNHUyxONexxFJigoQEam2+DHHEu3Tj9y1a/CvfcvrOLWiXunc\nj3033ZwWvR8A5OcTHTiInE8/IXf5m16nEUmKChAROSz/WRn1cY+TpF7OO28TfGUGxWe3p/i83l7H\n+Y79wzBaE0QyhAoQETks0V69iR3fktDzU3F276q8QQard/dfgdI7X9Kl96NU8bndiB1zLMGXXoCi\nIq/jiFRKBYiIHJ6cHIquuBKncB/BqZO9TpMyORvfJTjjBYrbtiPau4/Xcf6bz0fk8uH49uwmOPsV\nr9OIVEoFiIgctvDIK3D9/sTKqK7rdZyUyL+ntPfj5+nX+1EmPHQEoGEYyQwqQETksLlHHEHk4kvw\nb3wH/8oVXsepcTl2E8EXp1N8Vluiffp5HeegYt8zFLdtR2D+azhbt3odR+SQVICISI0om4yal4W3\n5Obf81cc10088yVNez/KhIeNxInFCE2f4nUUkUNKaQFijOlkjFlQ+voUY8xiY8wiY8yDxhhf6far\njTGrjDHLjDEDSrflGWOmle77ijGmeSpzisjhK+7anZKTTyH40vSsejBazub3CE6fRsmZrYn2u8jr\nOJWKDL4c1+8nOGWS11FEDillBYgx5pfAI0CodNPdwC3W2u6AAwwyxhwJ/AToCvQF/myMCQLXAxtK\n950A3JKqnCJSQxyH8NhxOJEIoUnZMwch/56/4bhu2qx6Whm3WTOivS8kd/3aOvugQMkMqewB+QAY\nUu59e+CN0tevAhcAHYEl1tqItXYX8D5wFtANmFVhXxFJc+Hho3CDwcTKqFkwGTXng80En59Cyeln\nEr1ogNdxkla2JkhIS7NLGvOn6sDW2mnGmBPKbXKstWW/kfYAjYCGQPmFAw60vWxbpQoK8vH7cw4n\n9ncEglX79iS7f/PmDaoTp0bp2qq+f6ZdmyfX1bwBDBuGf+JEmr+9Cs4/P6lmafsz+8V9EI/jv/13\nNG+R1K+h/+LJtY0aCj/7MfnTp5D/9zshp+Z+L5aXtj+3GpDN15YuUlaAHED5BxQ0AHYCu0tfH2p7\n2bZK7dhRePgpy4lGSpLeNxD0J73/tm17qhupxujaErL12ry8Lv/wMRRMnEj43vvZ0/qcpNqk48/M\n99GHNHnqKWKnnc6OHn2gmsfy6trqXzKEvImPs3P6yyl7Zk06/txqSqZcWyYXNLV5F8waY0yv0tf9\ngUXACqC7MSZkjGkEnA68DSwBLqqwr4hkgJJzOlJy+pkEX5mB8/XXXseptvy/34kTi1H4s1+CL/Nu\nGCxbE0TDMJKuavNP1c+B240xbwIBYKq1dgtwH4kC43Xgt9baMPAgcKYxZjFwDXB7LeYUkcPhOBSN\nHYdTUkLesxO9TlMtvk8+JjT5WUpO/R6RgYO9jlMtJZ06Ezv+BIIzX4K9e72OI/JfUjoEY639GOhc\n+vo9oOcB9nkYeLjCtkJgaCqziUjqRIYOp/7vbyM08QkKf3xjyuYgpEr+vXf9p/cjw7Lv5ziEh42g\n3p1/IfjKDCKlE1NF0kXm9SuKSNpzGzQkfNlQcj77lMD8eV7HqRLfp58Qeu5pSk4+hcjgy7yOc1jC\nlw8HNAwj6UkFiIikRHjMVQCEnnzM4yRVk3/fPTglJRTe+IvM7f0oFT/pZIrP6UTuwgX4vvrS6zgi\n36ECRERSoqRNO4rbnU1g7mx8n3/mdZyk+D7/jNCzEyk58SQiQ7JjFDg8bCSO62b1k4olM6kAEZGU\nCY8djxOPE3rqSa+jJCX/vrtxiosTvR/+2lylIHUigy7FDQQITXk2KxaHk+yhAkREUiY8aAjxho0I\nPT0Biou9jnNIvi+/IPTMRGItTyBSOnciG7iNC4j26Y9/00b8b6/3Oo7IfipARCR16tUjPGwEOV9v\nITD7Va/THFL+P+7BiUazqvejTNmaIMHJmowq6UMFiIikVHjMOADynnzU4yQH59vyFaGnniR2fMv9\nf1lnk2jvC4k3aUJo2mQoSX6FT5FUUgEiIikVO+10op3PJfDGfHwffuB1nAPKu//vOJEIhTf8HHJz\nvY5T8wIBIpdeju+bbQQWvOZ1GhFABYiI1ILw2NJekDScjOr7egt5Ex4nduxxhIeP8jpOyuwfhtGa\nIHWGMcZnjHnIGPOmMWaBMeaUA+yTb4xZYow5rcL2TsaYBanMpwJERFIuMmAQ8aZNCT07ESIRr+N8\nR9799+KEw4nej0DA6zgpU9KuPSWnnErw1Zdxdu+qvIFkg8FAyFrbBbgZuKv8h8aYDsBC4OQK238J\nPAKEUhlOBYiIpF4wSHjEaHzbtxN8+SWv0+znbN1K3oTHiB19DOER3/c6Tmo5DpFhI3HCYYIzXvQ6\njdSObsAsAGvtMqBDhc+DwKXApgrbPwCGpDqcChARqRVFV1wJpNfKqPkP3IdTVEThT34GwaDXcVIu\nfNkwQMMwdUhDoHx3V8wYs/8WL2vtEmvtf60SaK2dBqT8vnkVICJSK+InnUy053kE3lxCjq34D67a\n52zbRt4TjxA76mjC3x/jdZxaET/ueKJduxNYuhjfp594HUdSbzfQoNx7n7U2bW6DUgEiIrWmaOx4\nAEITvO8FyX/wHziFhRT++Kd1ovejTKR0MmpompZmrwOWABcBGGM6Axu8jfNdKkBEpNZE+/YndkQL\nQpOehcJCz3I427eT99jDxFocSXj0lZ7l8EJk4CDcUIjgZC3NXgdMB8LGmKXAPcCNxphRxphrPM4F\nQHYt9yci6S03l/DoMdS7+28EX3yeyMjRnsTIf+h+nMJ9FP3mVgildKJ/2nEbNCRy0QBCz0/F/9Yq\nStqf43UkSRFrbRy4rsLm/xr/tNb2OsC2j4HOKQlWSj0gIlKrwqOvxPX5PFsZ1fl2O6FH/kW8+REU\nXXGVJxm8tn8YRpNRxUMqQESkVsWPPY7oBX3IfWs1/vVra/38ef9+AN++vRT+6KeQl1fr508H0Z7n\nE29+BMHpUyEa9TqO1FEqQESk1pWtjBp68vFaPa+zcwd5D/+LeLPmFJVmqJP8fsKXDcO3YweBeXO8\nTiN1lAoQEal10fMvJHbscYSmTcbZs7vWzpv3rwfw7dlN4f/8BPLza+286SisYRjxmAoQEal9OTmE\nr7gSp3Afwam1czuos2sneQ8/RLxpU4qu+kGtnDOdxVq1puT0MwnMeRVnx7dex5E6SAWIiHgiPOoK\nXL+fvCcerZXbQfMefgjf7l0UXv8TqFcv5edLe45DeOgInOJigi9O9zqN1EEqQETEE/EWRxLtPwD/\nxnc47qN3UnouZ/cu8v71APGCAsLj1PtRJnL5MFyfj9DkZ72OInWQChAR8UzZRNCOi1P7cLS8R/+N\nb9dOiq7/MW79BpU3qCPiRx5FcY9e5K5aQc6H73sdR+oYFSAi4pnibj0oOelkWq1+nbx9qZmM6uzd\nQ95D9xNv3Jii8WmxAGRaKZuMGpwyyeMkUteoABER7/h8hMeMI7ckSrvlr6bkFKFH/41vxw6KrvsR\nboOGKTlHJotcNBA3v17ibph43Os4UoeoABERT4WHj6LYH+CcRS/W/GTUvXvJf/AfxBs1pugH19bs\nsbNFvXpEBg4i59NPyF2xzOs0UoeoABERT7lNm/JOu1403/oZJ25eU6PHznv8EXzffkvRNdfjNmxU\no8fOJv8ZhtGaIFJ7VICIiOdWdB8EQMdFNTcZNTdSRP6D9xFv0JCia66vseNmo+Ku3YkdfUzidtxw\n2Os4UkeoABERz316Umu2HH0Sp69bSL3dNbMoVsdFL+L75huKrr4Ot1HjGjlm1srJIXLZMHy7dxGY\nk5q5OCIVqQAREe85Diu7DcIfK6H9slcO+3C50TDd5z1DvH4Diq79nxoImP32L82uNUGklqgAEZG0\nsLZjH6KBEOcsfgnnMO/GOGfxi9Tfs4Oiq6/FLWhSQwmzW+y00ylu047Aa3Nxtm3zOo7UASpARCQt\nRPLqs77DBRRs/4pTNq6s9nH80Qjd5z5DJJhH0bU/rMGE2S8ydDhOLEbohaleR5E6QAWIiKSNFd0S\nk1HPWfxCtY/RYckMGuz+lmU9h+A2aVpT0eqE8KVDcXNyCE7W3TBJq4XnGGUrFSAikja+bHkaXxxv\nOG3DUhru2Frl9v7iCD3mPk0kkMeS3iNSkDC7uc2bEz3/AnLXrSHHbvI6TtprsvVzfvr7UfR64d9e\nR8lIKkBEJK2s6DYInxunw9KZVW7bfslMGu76hhU9BlNYX3e+VEdk2EiAxMqoclCNt3/FuPtuoNnW\nz9nR/Biv42QkFSAiklY2tO9NOFSPDktm4IuVJN0upzhKj7lPEw2EWHzByBQmzG6RPv2JN2xEcOok\nLc1+EI12fM34e2+g8Y6tzBp8Heu6Xux1pIykAkRE0ko0lM/ajn1puOsbzNtLk27X/s2XabRzGyu6\nD2Zfg4IUJsxyeXlELhlMzpdfkLtkkddp0k79Xd9w1b0/pWD7V7x28TgWX/h9ryNlLBUgIpJ2Vna7\nBCDxfJgk5BRH6THnKYpzAyzW3I/Dtn8YRmuCfEe9PTsYd99Pabbtcxb0vYL5/a/0OlJGUwEiImnn\n62NO5pOTWvO9jSso+ObLSvdvt3wWjXdsZUW3QextpDtfDldxx87Ejj+B4IwXYd8+r+Okhby9u7jq\nvp9yxJZPWNx7BPMGXg2O43WsjKYCRETSUtnzYc5Z/NIh98spKabn7IkU+wMsunBUbUTLfj4f4cuH\n4RTuI/hq1ScDZ5tQ4R6uuv9GjvzyQ97sOYRZl/6Pio8aoAJERNLSO+16UVivIWe/+TI5xdGD7td2\n+SwKvt3Cqq4D2duoWS0mzG6RYVqaHSBYtI+x9/+coz/bzMquA3nl8htUfNQQFSAikpZKcoO81fki\n6u/dyRnrFh5wH1+shJ6zJ1Liz2VhH00GrEmxk06huP055C5cgG/LV17H8UQgXMiYB37BcZ9s5K1O\n/XhpxE24Pv21WVP0nRSRtLWy60AAOi4+8GTUtitm02T7V6w6dyB7GjevzWh1QnjYSJx4nOC0KV5H\nqXW50TCjH7qZlh9uYF2HC5g++mYVHzVM300RSVvbWxzPB6Y9J25eS/MtH3/nM1+shJ6zJlKS42dR\nH839SIXI4CG4ubl1bxgmHOb7//oNJ21ew9ttezJtzG9xfTlep8o6KkBEJK395/kw352MetaqeTT9\n5gtWd7mYXQUtvIiW9dyCJkQv7Id/4zvkvL3B6zi1Ixql4fgrOGXTSja27sqUq35HPMfvdaqspO+q\niKS1jW26s6dhE9ote5W5l1wDwfr4YiX0mvUkJTl+Fva9wuuIWS08bCTBV2YQmvws+1q19jpOahUX\n0/CaqwjOnc17p3fkufG/J+bP9TpVtRljfMADQBsgAvzAWvt+hX3ygbnAeGvtpmTa1JRaL0CMMW8B\nu0vffgT8EXgCcIG3gR9aa+PGmKuBa4ES4A5rre4FE6mD4jl+Vp87gF6zJtBq9eu80+sSWq9+jWZb\nP2dl14HsaqLej1SKXtCHeEEBoWmT2Xfb78Gfpf9uLSmhwQ+vJvjKDKLde/LMkFuJ5Qa8TnW4BgMh\na20XY0xn4C5gUNmHxpgOwEPAscm2qUm1OgRjjAkBjrX/3955h0lZXn34XhYWpAdFiQWxzRFRwYa9\ndxQbYsFY0FhCVGyJ/VNjjS2KaOyiRmM3BmOLXbEXFDX+EHuUWKkidff74zwD47rg7rK7M+9y7uvi\nYuYtM+fszLzveU7VFunfYOBS4DRJmwJlwK5m1g04GtgY2B4438xaN6WsQRCUDq9t1J/KsjL6Pv8A\nZYiM4mEAACAASURBVJVz2OKRW5jTopxnwvvR+FRUMGO3AbT45mtaPftUsaVpHObMocPQIbT5x33M\n3GAjJt1yB7MrmsUtZxPgEQBJLwHrVtvfGtgdeL8O5zQYTZ0D0htoa2aPmdmTybpaB3gm7X8Y2Abo\nC4ySNEPSJGAcsGYTyxoEQYkwcfFujO21Act98h5b3Xc1Xb/6jDc32IGJi/+62KItEkxvzq3ZKytp\nf8JQ2tx9B7PWWY/Jt98N7doVW6qGoiMwqeD5HDOb68KSNErS53U5pyFpagNkGnAx7tU4ArgN94hU\npf1TgE78/A+Q3x4EwSLKqykZdeNH/pa8HwcUWaJFh9lrr8vsFVei9UMPUjZl8i+fkBWqqmh/0vEs\ndtstzOq9FpPuuJeq9h2KLVVDMhkoVKiFpF8aMV2fc+pFUwfzxgLjksEx1sy+wz0geToAE/n5HyC/\nfYH86ldtadmy4UqlKlrX7c9T2+O7di3+Fzx0q/vxWdOtuen1ydqbMKnLUnT6/ivGbLADPyzTnV+K\n0GdFt/oc3+S6DT4ITj+dJZ55DAYPBjKuW1UVHHccjLgB1lyTVk8+zhJduszdnWnd5jEK6A/clSIO\ntSllqs859aKpDZCDgTWAIWa2NO7peMzMtpD0NLAj8BTwCnBuyhlpDfTEE1QXyIQJ0xpU2Jkzam/0\nVbRuWevjv/lmSn1FajBCN6e56tZc9Xpm2/3Y6uERPLnd/rU6J0u6QWl/bi123I3FTz+dmTfcxKSd\n9wQyrFtVFe3OPYu2wy5jtq3KxDv+QdWcVlDwvlnR7RcMmvuBbc3sBTzHcrCZDQLaS7q2tuc0pLyF\nNLUBcgMwwsyex6teDga+Ba4zswrgP8A9kuaY2TDgOTxMdKqk6U0saxAEJcYrm+3O6G0H1unmEDQM\nld2XZ+aGG1Mx6jlafP4Zlct1L7ZI9abtRefTdtilzF5pZSbeM5KqJZrnDCFJlXi6QyHv13DcFr9w\nTqPQpAaIpJlATS0LN6/h2OuA6xpdqCAIgqBWzNhrXypeHEWbe+9i2jEnFFucerHY5ZfQ7uILmLN8\nDybdO5KqpaKMu1hEJ9QgCIKgVszovytVbdrQ+q6/ew5Fxljsr8Npf+5ZzFl2OSbe9yCVSy9TbJEW\nacIACYIgCGpFVcdOzNihHy3HfUDL0W8UW5w60eaGa2l/xinM+fXSTLx3ZKZDSM2FMECCIAiCWjMj\ngz1B2tw6gg4nn0Bl1yWZdO9IKldYsdgiBYQBEgRBENSBmVtsTeUSXWl9/z2Uz55VbHF+kdZ33Eb7\nE4ZSufjiTLx3JHNWXqXYIgWJMECCIAiC2tOyJdMHDKTF99+zynsvF1uaBdL6/nvocMzvqerUiYl3\n/5M5q/YstkhBAWGABEEQBHUiH4bp8/IjRZZk/lQ8+E86DDmUqnbtmXTXP5jT3Cf5ZpAwQIIgCII6\nMXv1NZm9ak9WfecF2kwrfrO36lQ89jAdDx9MVZvFmHTnfczus3axRQpqIAyQIAiCoG6UlTF94L60\nnD2LNV5/stjS/IRWTz5Ox4P3h1atmPz3e5i9bt9iixTMhzBAgiAIgjozY8BAKsvK6PNK6YRhWj33\nDJ0OGgQtWjDpljuYtcFGxRYpWABhgARBEAR1pnLpZfjI1mH5j96hyzdfFFscWr30Ap323xsqK5k0\n4jZmbbZFsUUKfoEwQIIgCIJ6Mbrv9gD0eeXRosrR8rVX6LjvnjBzJpNvuJVZW21bVHmC2hEGSBAE\nQVAv3uu9GTMr2rgBUqTW7C3fepNO+wygbPqPTL7mJmZuv2NR5AjqThggQRAEQb2Y2aYt7/bZnC7f\nfkn3j8Y0+fuXvzOGTnvtRtmUyUy58lpm9t+1yWUI6k8YIEEQBEG9Gb3+DgCs9XLThmHK9T6dB+5C\niwkTmHL5VczYY2CTvn+w8IQBEgRBENSbj3JrMbnTEqz+xpO0nDWjSd6z/MMP6DSgPy2++44pF1/O\njH32a5L3DRqWMECCIAiCelPVopzRfbdjsR+nYmNeaPT3a/HJx3Taoz/lX3/FlPMvYvoBgxv9PYPG\nIQyQIAiCYKFoqmqYFp9/RucB/Skf/yVTzzyX6Ycc3qjvFzQuYYAEQRAEC8XXS6/Il8utQu7dl2g7\nZUKjvEeL8V/SeY+dKf/8M344+XR+HHJUo7xP0HSEARIEQRAsNG/23YHyyjms+foTDf7aZV99Rac9\ndqb800/44bg/Mu3YPzT4ewRNTxggQRAEwULz9rrbMKdFOX0auBqm7Ntv6bxnf1p+OI5pRx7DtBNP\nbdDXD4pHGCBBEATBQvNDxy6M67key372Pl3/90mDvGbZhO/pPHBXWup9ph32O344/SwoK2uQ1w6K\nTxggQRAEQYPwZuoJ0hBekLLJk+i09+60fHcMPx54CD+cfUEYH82MMECCIAiCBuH9NTZhept29H71\nMcoqK+v9OmVTp9Bp7z1oNfpNfhy0P1P/fEkYH82QMECCIAiCBmF2RWveWXsLOk/4mh7jRtfrNVrN\n+JGOgwbS6vVXmb7n3ky9ZBi0iFtVcyQ+1SAIgqDByPcEWevlR+p8bsuZM/jN1SdR8dILTN9ld6YM\n+yuUlze0iEGJEAZIEARB0GB8ulJvJnTpRq83n6bVzOm1Pq981kwGXXcqK419gxk77MSUv14PLVs2\noqRBsQkDJAiCIGgwqlq0YHTf7Wk940d6vvVcrc4pnz2LfW44g9x7L6NeGzD5uhHQqlXjChoUnTBA\ngiAIggZldN/tAOhTizBMizmzGXjTWfQc8zzjVl2Xvx96DrRu3dgiBiVAGCBBEARBg/LdUt35vMdq\nrPz+a7Sf9O18jyurnMOAW85j9dHP8PEqfbjt8POZ3SqMj0WFCLAFQRAEDc6b6+/Acp+8R+9XH2fU\nNvv8bH9ZZSW73/Zner/2bz5dcXVuPeLPzKpoUwRJmy9m1gK4CugNzAB+K2lcwf7+wP8Bs4EbJV1n\nZq2Bm4AVgcnA7yV90BjyhQckCIIgaHDGrL0Vs8tb1jwht6qK/ndeytovPcx/l+/JLUMuYmabtk0v\nZPNnN6CNpA2Bk4BL8jvMrBXwF2A7YHPgMDNbCjgUmCppA+AoYHhjCRcGSBAEQdDg/Ni+E2NX35Bf\nfzGObv8dN29HVRU73X05fZ9/gC+XXYURR17CjMXaF0/Q5s0mwCMAkl4C1i3Y1xMYJ2mCpJnA88Bm\nwGrAw+kcpeMahTBAgiAIgkYh3xNkrhekqort77+KDZ+5l/8tvSIjjrqU6W07FFHCZk9HYFLB8zlm\n1nI++6YAnYDRwM5mVmZmGwDLmFmjNGMJAyQIgiBoFNRrQ6a17eCt2efMZusHb2DTJ+7gm6W6c9NR\nf2Fa+87FFrG5MxkotPBaSJo9n30dgInAjWnfc8DuwOuS5jSGcGGABEEQBI3CnFYVjFlnazpM/p69\n/noKWz5yM98tsQw3Hn0ZP3TsUmzxFgVGAf0AkjdjTMG+/wCrmFkXM6vAwy8vAusBT0jaBLgb+Kix\nhAsDJAiCIGg08mEYG/0cExb/NTcOvZwpnbsWWapFhvuB6Wb2Ap5weqyZDTKzwyTNAo4DHsUNjxsl\nfQF8ABxjZi8CZ6djGoUoww2CIAgajc9X6MV/l+9J+6kTuPHoy5jUZalii7TIIKkSOKLa5vcL9o8E\nRlY751tgm8aXLgyQIAiCoDEpK+P6Y66gvG1rZsz+5cODRYcIwQRBEASNyuyK1lSVx3o3+ClhgARB\nEARB0OSEARIEQRAEQZMTBkgQBEEQBE1OGCBBEARBEDQ5YYAEQRAEQdDkhAESBEEQBEGTEwZIEARB\nEARNThggQRAEQRA0OWGABEEQBEHQ5JRsazozawFcBfQGZgC/lTSuuFIFQRAEQdAQlLIHZDegjaQN\ngZOAS4osTxAEQRAEDUQpGyCbAI8ASHoJWLe44gRBEARB0FCUVVVVFVuGGjGz64F7JT2cnn8GrCgp\n5ikGQRAEQcYpZQ/IZKBDwfMWYXwEQRAEQfOglA2QUUA/ADPbABhTXHGCIAiCIGgoSrYKBrgf2NbM\nXgDKgMFFlicIgiAIggaiZHNAgiAIgiBovpRyCCYIgiAIgmZKGCBBEARBEDQ5YYCUEGbWqtgyNBVm\ntoGZdSu2HEEQBFnDzDqbWVmx5VhYwgApEczsAOAaM1u52LI0JmZWZmanASOAQ5rDj6gmzGxtM9u1\n2HI0Jma2mJldb2bNLkE8dMsmzVm3PGZ2DHAbsHmxZVlYwgApAcysPbAW0BXYwsx+VWSRGgUzW15S\nFTASOAToCOxZXKkanmRE3gpcaGa9ii1PI9IPaA8camarFVuYBiZ0yybNVjcza2lm+crVd4G+ZrZi\nMWVaWMIAKSJmtqWZnQKsIulY4DhgC2C9gi9as8DMzgZuMbMzgJUkjQIEbGxmaxZXuobBzJY0s7WA\njyX1AoYDx5tZxyKL1qCY2VFmthHwhqR9gHuA84ssVoMQumWT5qwbgJntjQ9n3Rq4DrgI6AZsZWaL\nFVO2hSEMkCJhZgcDFwNTgdPM7Gjgc+AJoD9gRRSvQTGzgUAvXK8XgcFm1g/4O/ANMMDM2hZRxIUm\n6fgUcCxwR1qZXIl38z2oOYSazKybmd0HrIO7f+9Iuy4DppvZ6UUTbiEJ3bJJc9Ytj5n9HjgCeAzY\nFjgPmA08DKyGL1ozSRggxWNZ4HBJw4BhwJLAIDw3Yg6wg5ktVTzxGpSWwDOSJgOPA1fj3p7WwL+A\ndsA+xRNv4UjJw5sAe0g6AO/aewiwHHAasDuwYfEkbDCWAuZIOkjS+cBHZna9pErgbPw7u1lxRaw3\noVs2ac665akAzpR0D37tnAAMlfRv4DNgg6yGesMAKR6L4zdhJD0DvAbk8Jvx9UAfYH0za1M0CReS\nglX/98CeZtYmXRiewPXtB7wDvOKH28bFkXThkDQLWBvYKG26Hl+h9JMk4F7gGDNbskgi1osavDaz\nga/MrE96fgCwqZntLukd4CbgDDNr3ZRy1ofQLXTLEEsCvwOQNA74J7BEyjX7B76Q2yyLuYNhgDQB\nZtai4HG+1PZkoJuZ7ZWeP4+HKZZJP5y7gL2A1ZtS1oXFzNY0s5UAUsIpkh4FxuFuUSRNB6YAP6YB\ng4/ixlf/rJUiF8h7Hr7aWlbSl8DLwG4AkobjoaYjzKy8OJLWnfznV8D49P+aZtY1GV4nAzubWQtJ\n1wOj8dBiSRO6hW6liJm1zef/5a8tkk7G9TswHfYubnS0k/QJboRsQgZDMWGANDJm9lu8vPY0M1tM\n0iwzK5M0DU+SOt3MuuNutSrcMwLwHZ5w1K4ogtcRM2tlZnfibs8bzWyfaiGkw4GVzOzUlO+yMzAz\n7Vsa+B9wdbqAlCxmtpaZDUrJphTI+xruzTkxbX8I+KEgS/0h4HTcyCxpzKydmZ1tZicWunYlfY+v\nvjYEtk+blwLGSapMq9OlgR6l6rkL3UK3UsU8Qf964GozW7zatfBQ4Kzk6ekBrADkPR4V+HXliyYU\nt0EIA6QRMbM9gT2AS4FlgGtTGKLKzMpTDO9O3BB5DXhe0gvp9PFA3xSeKVkKXKJrAhMl7QqcCqwH\n7JRPLpU0E0+kGgP0BI6Q9K907geSfpes+ZIlZaL/FegOXGBmRxbs/ha/eJiZXWtmz+JJxZ+k/a2A\ngZLebkKR64yZdQYeAWakTYeY2WH5/ZIewz1W65jZg/j3+8G0uwVwlaT+yctVUoRuoVsTil0r8tdP\nM9sZ93YfhIesz7ZURpzuFaOAC4H9gJuBmyQ9nV5mLLCJpFeaVvoGoKqqKv410r9cLndkLpe7pOD5\n/blc7sgajivP5XJLFzwvK7bsddBxifT/Jrlc7t2C7QNzudz5uVyuT3q+QS6XW6vauWUZ0/XiXC43\nKD3ulcvlPs/lcj2rHdMm/S12LLa89dRxyVwuNzw9rsjlclvlcrm/5XK5DQuOqUj/F24r+c8xdAvd\nSvVfLpcbksvlbsrLncvlLsvlcsfncrl2aVvHXC7XKj3ukEUda/oX03AbGDM7FK9iuRMvDesH3CZp\njJn1xhtU7SzpMzM7Fnhd0rPp3DKoMc5Zcpj37vgTXkb8GnA/8FtgiqQLU9LXNcBTkm42s/7At5Je\nTOeXZUjP6ZLGptBRFXCrpIlmdhywp6SNUrx2f+A5SR8UnN8iJd6WLCmctBrwFvAh8AYwSNKbZtYV\nX1Eujq/AdgeWSnkt+fPLJc1pesl/mdAtdCtFzKwdcBLwA3Af7qkZCvxN0qgUZhqGe4o/MLMTgZfz\nXo8sXFdqQxggDYSZdcCTgd4DFgO+BCYCbYCvgfskfW9mFwFv4610u0j6tkgi15uUSHk/nij7IjAA\n2AC4BDdC7pL0cMp/WVLSeUUTtp4kg+Jv+GfZGU9i64HHl++T9Ho67gG8KZCAFSW9XBSB64mZ7QMc\ng393t8Gbp1UB50haIx3TH9hY0knJHT69FN3Z1Ukhs2MJ3UK3EiEtMjvjeSuPArOAJfCF3KfASsAw\nSd+Y2cXAe5JuNLPWkmbM73WzSuSANBydgI8kHQUcDXyUtrXAb1onmJdN9SUlSGXN+LCCah7gv8CT\nkj7E8yK+A7bDb9rDzexM4A/Ac00tZwOxBjBJ0i54L48N8dVKZ2BzM1s3HTcd+EzSN1kyPszb/wP0\nxi94F+AX/WHAKOCVlMtSjl8Ul05ercmSplf7LpQq6xK6ZVG39Wi+ugGUAW9JOgf4C56g3h1vWjgF\nuMLMNgU2A/4D0ByNDwgPyEJhZsviN913cVfhf/BEw9fMzPDwywTgVeAwPBv7MUnXFUnkemPeuXU9\nPDn2POAG4E1Jl6Uf/trAkUD+uGWB1yR9WiSR64WZrYIbj2sBN8tbquc9BT2A99P/m+DGyBh5G/3M\nYGa/wZPZ9gMOxEOGN0ualFy9W+Lf3cvxeT2/wpvmjZ/PS5YMycj/KFVAHJ02Nxfd+uCl6zIfSFZJ\n89FtQaHOTOsGYGZ98blXLwD/xsPW+0oabd4fqD++UL0S90guCTwq6Z9FErlJyIrFWHKY2W54GKU7\n3t9id7wE82wAScKztleT9C5uyR+YNz4sQ625k/GxDx5vXRy4AF+VDDGzNVIs8lO8rLZK0suS7pX0\nqWWk74WZrWFmD+F5LXcA04B/mtkJ6ZD78Zh0G0mX4fHb4/LGRxY+T/NSxkvwTPsc7vb9HC/pWxFA\n0p9xg2St5M07StIuksaX+grTzJbGBx32TpvG43plWjczqzCzu/FquVvNbBt8sbMK2dctX75/DnC9\nme2SdnXDvRyZ1S2Pme2AXy8fw/s8/YCHbW8FkPQ13ieos6R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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "IV: 0.039354549526\n" ] }, { "data": { "text/html": [ "
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% responders% non-respondersWOEDG-DBIV
(3800.0, 6000.0]0.0988370.138898-0.340264-0.0400610.013631
(2100.0, 3800.0]0.2087210.1940900.0726740.0146300.001063
(200.0, 900.0]0.1540700.1439150.0681810.0101550.000692
(6000.0, 75600.0]0.0860470.101864-0.168747-0.0158170.002669
(900.0, 1200.0]0.1209300.1185090.0202240.0024210.000049
(1200.0, 1700.0]0.1180230.1037750.1286560.0142480.001833
(1700.0, 2100.0]0.0947670.117952-0.218848-0.0231840.005074
(0.0, 200.0]0.1186050.0809970.3813820.0376080.014343
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" ], "text/plain": [ " % responders % non-responders WOE DG-DB \\\n", "(3800.0, 6000.0] 0.098837 0.138898 -0.340264 -0.040061 \n", "(2100.0, 3800.0] 0.208721 0.194090 0.072674 0.014630 \n", "(200.0, 900.0] 0.154070 0.143915 0.068181 0.010155 \n", "(6000.0, 75600.0] 0.086047 0.101864 -0.168747 -0.015817 \n", "(900.0, 1200.0] 0.120930 0.118509 0.020224 0.002421 \n", "(1200.0, 1700.0] 0.118023 0.103775 0.128656 0.014248 \n", "(1700.0, 2100.0] 0.094767 0.117952 -0.218848 -0.023184 \n", "(0.0, 200.0] 0.118605 0.080997 0.381382 0.037608 \n", "\n", " IV \n", "(3800.0, 6000.0] 0.013631 \n", "(2100.0, 3800.0] 0.001063 \n", "(200.0, 900.0] 0.000692 \n", "(6000.0, 75600.0] 0.002669 \n", "(900.0, 1200.0] 0.000049 \n", "(1200.0, 1700.0] 0.001833 \n", "(1700.0, 2100.0] 0.005074 \n", "(0.0, 200.0] 0.014343 " ] }, "execution_count": 42, "metadata": {}, "output_type": "execute_result" } ], "source": [ "functions.feature_stat(data, 'FST_PAYMENT', 'TARGET')\n", "functions.calc_iv(data, 'TARGET', 'FST_PAYMENT')[0]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### FACT_LIVING_TERM\n", "How long the person lives in the fact place, months." ] }, { "cell_type": "code", "execution_count": 43, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 43, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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+oMb4KoBBr6nw/5ZuImIm8F7gBxHxW7Rm9h+iFfQ/orVW/R9t/RcC/5OZO7tQ2xeGawHu\nzMwXO3XKzGcj4hrgS8DWqvkntMJ+uM+fVvWuobWGXsdhl24i4s+BU2j9cRr2AvAYrWsFl9BaujHo\nj1Iu3Wg6uBB4NDN/PTPPz8wzgRMj4jRaF2c/Vl3kJCJOqNrmdqmWLwO/TesiaKdlm1dk5l1AAu+v\n9n9C64/VK4EaEfOAX6F1UbZbVtGa1Y8M8q8AH6F1nWHPq87SUcMZvaaD5cCtI9puBT6UmVdExOeB\n+yPiJVp33VyXmd+rOda6iNhdbefwBV1+2jAYETuA+Zn5/TG83jXAb7Ttvw+4MSIeBg4Ax9G6q+Zr\nNesduXSTmbliRM0HI+Jy4MGIaL+7559o/cfxgZpjqxA+pliSCueMXkWJiDcCt3U4tGV4zXwqTNe6\ndHRwRi9JhfNirCQVzqCXpMIZ9JJUOINekgpn0EtS4f4P557ZkBdj5voAAAAASUVORK5CYII=\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "data['FACT_LIVING_TERM'].plot(kind='box')" ] }, { "cell_type": "code", "execution_count": 44, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(38.5, 85.5] 0.188218\n", "(238.5, 1000.0] 0.187237\n", "(85.5, 131.5] 0.157677\n", "(0.0, 18.5] 0.122373\n", "(18.5, 38.5] 0.122233\n", "(171.5, 238.5] 0.119081\n", "(131.5, 171.5] 0.101919\n", "NaN 0.001261\n", "Name: FACT_LIVING_TERM, dtype: float64\n", "IV: 0.0508487769524\n" ] } ], "source": [ "data['FACT_LIVING_TERM'] = functions.split_best_iv(data, 'FACT_LIVING_TERM', 'TARGET')\n", "data['FACT_LIVING_TERM'].fillna(data['FACT_LIVING_TERM'].cat.categories[0], inplace=True)" ] }, { "cell_type": "code", "execution_count": 45, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Counts:\n", "FACT_LIVING_TERM\n", "(0.0, 18.5] 1765\n", "(18.5, 38.5] 1745\n", "(38.5, 85.5] 2687\n", "(85.5, 131.5] 2251\n", "(131.5, 171.5] 1455\n", "(171.5, 238.5] 1700\n", "(238.5, 1000.0] 2673\n", "Name: TARGET, dtype: int64\n", "Frequencies:\n", "(38.5, 85.5] 0.188218\n", "(238.5, 1000.0] 0.187237\n", "(85.5, 131.5] 0.157677\n", "(0.0, 18.5] 0.123634\n", "(18.5, 38.5] 0.122233\n", "(171.5, 238.5] 0.119081\n", "(131.5, 171.5] 0.101919\n", "Name: FACT_LIVING_TERM, dtype: float64\n" ] }, { "data": { "image/png": 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gGRARx6G8zSHktzmE/HG3kTx3Fr9PnEqrLz6i5X++oP+rk/m8bTc+6dSf78yhuL4ErxOL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\nyuWFdFvgxlIRWZ3f9vfD5EgDpAeR9DVgBLCr7Qc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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "IV: 0.0503735245857\n" ] }, { "data": { "text/html": [ "
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% responders% non-respondersWOEDG-DBIV
(131.5, 171.5]0.1017440.101943-0.001955-0.0001993.892631e-07
(238.5, 1000.0]0.1273260.195444-0.428529-0.0681192.919086e-02
(18.5, 38.5]0.1453490.1190670.1994540.0262825.242095e-03
(38.5, 85.5]0.2151160.1845330.1533490.0305834.689865e-03
(85.5, 131.5]0.1453490.159366-0.092067-0.0140171.290523e-03
(171.5, 238.5]0.1104650.120261-0.084967-0.0097968.323416e-04
(0.0, 18.5]0.1546510.1193850.2588170.0352669.127448e-03
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" ], "text/plain": [ " % responders % non-responders WOE DG-DB \\\n", "(131.5, 171.5] 0.101744 0.101943 -0.001955 -0.000199 \n", "(238.5, 1000.0] 0.127326 0.195444 -0.428529 -0.068119 \n", "(18.5, 38.5] 0.145349 0.119067 0.199454 0.026282 \n", "(38.5, 85.5] 0.215116 0.184533 0.153349 0.030583 \n", "(85.5, 131.5] 0.145349 0.159366 -0.092067 -0.014017 \n", "(171.5, 238.5] 0.110465 0.120261 -0.084967 -0.009796 \n", "(0.0, 18.5] 0.154651 0.119385 0.258817 0.035266 \n", "\n", " IV \n", "(131.5, 171.5] 3.892631e-07 \n", "(238.5, 1000.0] 2.919086e-02 \n", "(18.5, 38.5] 5.242095e-03 \n", "(38.5, 85.5] 4.689865e-03 \n", "(85.5, 131.5] 1.290523e-03 \n", "(171.5, 238.5] 8.323416e-04 \n", "(0.0, 18.5] 9.127448e-03 " ] }, "execution_count": 45, "metadata": {}, "output_type": "execute_result" } ], "source": [ "functions.feature_stat(data, 'FACT_LIVING_TERM', 'TARGET')\n", "functions.calc_iv(data, 'TARGET', 'FACT_LIVING_TERM')[0]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### LOAN_NUM_PAYM\n", "\n", "Number of payments by the client" ] }, { "cell_type": "code", "execution_count": 46, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 46, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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ASUVORK5CYII=\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "data['LOAN_NUM_PAYM'].plot(kind='box')" ] }, { "cell_type": "code", "execution_count": 47, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(3.5, 4.5] 0.264290\n", "(11.5, 110.0] 0.191580\n", "(4.5, 5.5] 0.154245\n", "(0.0, 3.5] 0.150252\n", "(5.5, 6.5] 0.134351\n", "(6.5, 11.5] 0.105282\n", "Name: LOAN_NUM_PAYM, dtype: float64\n", "IV: 0.0295041530193\n" ] } ], "source": [ "data['LOAN_NUM_PAYM'] = functions.split_best_iv(data, 'LOAN_NUM_PAYM', 'TARGET')" ] }, { "cell_type": "code", "execution_count": 48, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Counts:\n", "LOAN_NUM_PAYM\n", "(0.0, 3.5] 2145\n", "(3.5, 4.5] 3773\n", "(4.5, 5.5] 2202\n", "(5.5, 6.5] 1918\n", "(6.5, 11.5] 1503\n", "(11.5, 110.0] 2735\n", "Name: TARGET, dtype: int64\n", "Frequencies:\n", "(3.5, 4.5] 0.264290\n", "(11.5, 110.0] 0.191580\n", "(4.5, 5.5] 0.154245\n", "(0.0, 3.5] 0.150252\n", "(5.5, 6.5] 0.134351\n", "(6.5, 11.5] 0.105282\n", "Name: LOAN_NUM_PAYM, dtype: float64\n" ] }, { "data": { "image/png": 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bT+jQwwj07W91GpECAqf1pfiV2WCzkXXpeJwt/S9j06TVLX/EM2cmge49KJk6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\nMzNbBU/H7X1OQMzMzFbhr2O35ZVha3pV1F7kBMT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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "IV: 0.0295041530193\n" ] }, { "data": { "text/html": [ "
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% responders% non-respondersWOEDG-DBIV
(5.5, 6.5]0.1325580.134597-0.015264-0.0020390.000031
(6.5, 11.5]0.1151160.1039340.1021830.0111820.001143
(11.5, 110.0]0.1453490.197913-0.308693-0.0525650.016226
(0.0, 3.5]0.1331400.152596-0.136399-0.0194570.002654
(3.5, 4.5]0.3058140.2586010.1676890.0472120.007917
(4.5, 5.5]0.1680230.1523570.0978730.0156660.001533
\n", "
" ], "text/plain": [ " % responders % non-responders WOE DG-DB IV\n", "(5.5, 6.5] 0.132558 0.134597 -0.015264 -0.002039 0.000031\n", "(6.5, 11.5] 0.115116 0.103934 0.102183 0.011182 0.001143\n", "(11.5, 110.0] 0.145349 0.197913 -0.308693 -0.052565 0.016226\n", "(0.0, 3.5] 0.133140 0.152596 -0.136399 -0.019457 0.002654\n", "(3.5, 4.5] 0.305814 0.258601 0.167689 0.047212 0.007917\n", "(4.5, 5.5] 0.168023 0.152357 0.097873 0.015666 0.001533" ] }, "execution_count": 48, "metadata": {}, "output_type": "execute_result" } ], "source": [ "functions.feature_stat(data, 'LOAN_NUM_PAYM', 'TARGET')\n", "functions.calc_iv(data, 'TARGET', 'LOAN_NUM_PAYM')[0]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### LOAN_AVG_DLQ_AMT\n", "\n", "Average deliquency amount" ] }, { "cell_type": "code", "execution_count": 49, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 49, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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/6bbPVmtk8oVKU8Q7AE1XzWZj4FDLegqAfjAANJ0ZAJquugWAPwSTpEIZAJJUKANAkgpl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\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "data['LOAN_AVG_DLQ_AMT'].plot(kind='box')" ] }, { "cell_type": "code", "execution_count": 50, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "NaN 0.871603\n", "(500.0, 15000.0] 0.123074\n", "(0.0, 500.0] 0.005324\n", "Name: LOAN_AVG_DLQ_AMT, dtype: float64\n", "IV: 0.0437967491802\n" ] } ], "source": [ "data['LOAN_AVG_DLQ_AMT'] = functions.split_best_iv(data, 'LOAN_AVG_DLQ_AMT', 'TARGET')\n", "data['LOAN_AVG_DLQ_AMT'].fillna(data['LOAN_AVG_DLQ_AMT'].cat.categories[0], inplace=True)" ] }, { "cell_type": "code", "execution_count": 51, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Counts:\n", "LOAN_AVG_DLQ_AMT\n", "(0.0, 500.0] 12519\n", "(500.0, 15000.0] 1757\n", "Name: TARGET, dtype: int64\n", "Frequencies:\n", "(0.0, 500.0] 0.876926\n", "(500.0, 15000.0] 0.123074\n", "Name: LOAN_AVG_DLQ_AMT, dtype: float64\n" ] }, { "data": { "image/png": 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SmhsUEYkGVVXkDBtA6isvUdntBIqnzUzoUiKJS8VERMRr1dXkDB9M2vPPUnXMcRQ/PAdS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\nUrLzLhQtWU7dDjt6HUlEJGZ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% responders% non-respondersWOEDG-DBIV
(500.0, 15000.0]0.1941860.1133320.5384930.0808540.043539
(0.0, 500.0]0.8058140.886668-0.095617-0.0808540.007731
\n", "
" ], "text/plain": [ " % responders % non-responders WOE DG-DB IV\n", "(500.0, 15000.0] 0.194186 0.113332 0.538493 0.080854 0.043539\n", "(0.0, 500.0] 0.805814 0.886668 -0.095617 -0.080854 0.007731" ] }, "execution_count": 51, "metadata": {}, "output_type": "execute_result" } ], "source": [ "functions.feature_stat(data, 'LOAN_AVG_DLQ_AMT', 'TARGET')\n", "functions.calc_iv(data, 'TARGET', 'LOAN_AVG_DLQ_AMT')[0]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### LOAN_MAX_DLQ_AMT" ] }, { "cell_type": "code", "execution_count": 52, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 52, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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emQ9322arNTL5QqUp4ghA01Wz2RjY07KeAqAfDABNZwaApqtuAeAPwSSpUAaAJBXKAJCk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\nkKRC/R+5SV8J/ivjLwAAAABJRU5ErkJggg==\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "data['LOAN_MAX_DLQ_AMT'].plot(kind='box')" ] }, { "cell_type": "code", "execution_count": 53, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "NaN 0.871603\n", "(500.0, 15000.0] 0.123354\n", "(0.0, 500.0] 0.005043\n", "Name: LOAN_MAX_DLQ_AMT, dtype: float64\n", "IV: 0.0435641041626\n" ] } ], "source": [ "data['LOAN_MAX_DLQ_AMT'] = functions.split_best_iv(data, 'LOAN_MAX_DLQ_AMT', 'TARGET')\n", "data['LOAN_MAX_DLQ_AMT'].fillna(data['LOAN_MAX_DLQ_AMT'].cat.categories[0], inplace=True)" ] }, { "cell_type": "code", "execution_count": 54, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Counts:\n", "LOAN_MAX_DLQ_AMT\n", "(0.0, 500.0] 12515\n", "(500.0, 15000.0] 1761\n", "Name: TARGET, dtype: int64\n", "Frequencies:\n", "(0.0, 500.0] 0.876646\n", "(500.0, 15000.0] 0.123354\n", "Name: LOAN_MAX_DLQ_AMT, dtype: float64\n" ] }, { "data": { "image/png": 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l8vNz4vYMac0NiojEgupqcocPJO3lF6nqfiwlU6YndSmR5KViIiLitZoackcMIf25Z6g+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\nk2GMCQB7EjoxdjlwQqN9RWJ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% responders% non-respondersWOEDG-DBIV
(500.0, 15000.0]0.1941860.1136510.5356860.0805350.043142
(0.0, 500.0]0.8058140.886349-0.095258-0.0805350.007672
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" ], "text/plain": [ " % responders % non-responders WOE DG-DB IV\n", "(500.0, 15000.0] 0.194186 0.113651 0.535686 0.080535 0.043142\n", "(0.0, 500.0] 0.805814 0.886349 -0.095258 -0.080535 0.007672" ] }, "execution_count": 54, "metadata": {}, "output_type": "execute_result" } ], "source": [ "functions.feature_stat(data, 'LOAN_MAX_DLQ_AMT', 'TARGET')\n", "functions.calc_iv(data, 'TARGET', 'LOAN_MAX_DLQ_AMT')[0]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Income_to_limit" ] }, { "cell_type": "code", "execution_count": 55, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 55, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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9cWb+V0Ssp/083xpwdWb+Q0RcAJxG++HsmzLzsxHxV8BbgN8A3ghcRTss3wr8aedRkecA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\nXZIK9H9mPhIFhPpihgAAAABJRU5ErkJggg==\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "data['Income_to_limit'].plot(kind='box')" ] }, { "cell_type": "code", "execution_count": 56, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(0.515, 0.783] 0.189619\n", "(0.783, 1.108] 0.181704\n", "(0.0, 0.515] 0.174629\n", "(1.428, 1.962] 0.129728\n", "(1.108, 1.428] 0.121393\n", "(2.556, 16.706] 0.114388\n", "(1.962, 2.556] 0.088540\n", "Name: Income_to_limit, dtype: float64\n", "IV: 0.0317804169333\n" ] } ], "source": [ "data['Income_to_limit'] = functions.split_best_iv(data, 'Income_to_limit', 'TARGET')" ] }, { "cell_type": "code", "execution_count": 57, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Counts:\n", "Income_to_limit\n", "(0.0, 0.515] 2493\n", "(0.515, 0.783] 2707\n", "(0.783, 1.108] 2594\n", "(1.108, 1.428] 1733\n", "(1.428, 1.962] 1852\n", "(1.962, 2.556] 1264\n", "(2.556, 16.706] 1633\n", "Name: TARGET, dtype: int64\n", "Frequencies:\n", "(0.515, 0.783] 0.189619\n", "(0.783, 1.108] 0.181704\n", "(0.0, 0.515] 0.174629\n", "(1.428, 1.962] 0.129728\n", "(1.108, 1.428] 0.121393\n", "(2.556, 16.706] 0.114388\n", "(1.962, 2.556] 0.088540\n", "Name: Income_to_limit, dtype: float64\n" ] }, { "data": { "image/png": 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DUHhqf/yL3vc4UWKtnX7pN9DjJLIh5acNIrrppuQ8PBqnpNjrOGlBBYhIBsu7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\nH/Y2mgoQSaK277/EsRPuIFRRxof7Hc4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% responders% non-respondersWOEDG-DBIV
(0.515, 0.783]0.1686050.192498-0.132527-0.0238930.003166
(0.0, 0.515]0.1366280.179834-0.274775-0.0432060.011872
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(0.783, 1.108]0.1773260.182303-0.027684-0.0049780.000138
\n", "
" ], "text/plain": [ " % responders % non-responders WOE DG-DB IV\n", "(0.515, 0.783] 0.168605 0.192498 -0.132527 -0.023893 0.003166\n", "(0.0, 0.515] 0.136628 0.179834 -0.274775 -0.043206 0.011872\n", "(2.556, 16.706] 0.125581 0.112854 0.106856 0.012727 0.001360\n", "(1.428, 1.962] 0.133140 0.129261 0.029565 0.003879 0.000115\n", "(1.962, 2.556] 0.118605 0.084422 0.339970 0.034183 0.011621\n", "(1.108, 1.428] 0.140116 0.118828 0.164799 0.021289 0.003508\n", "(0.783, 1.108] 0.177326 0.182303 -0.027684 -0.004978 0.000138" ] }, "execution_count": 57, "metadata": {}, "output_type": "execute_result" } ], "source": [ "functions.feature_stat(data, 'Income_to_limit', 'TARGET')\n", "functions.calc_iv(data, 'TARGET', 'Income_to_limit')[0]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Categorical\n", "\n", "Now categorical variables are different. Usually the main problem is that some categories have too little values. Again I'll try to do so that there are no categories with less than 5%. Most of the time it is necessary to combine categories based on the common or business case. I convert variables into type \"category\" for easier processing. Missing values are treated as a separate category." ] }, { "cell_type": "code", "execution_count": 58, "metadata": { "collapsed": true }, "outputs": [], "source": [ "for col in ['GENDER', 'CHILD_TOTAL', 'DEPENDANTS', 'EDUCATION', 'MARITAL_STATUS', 'GEN_INDUSTRY', 'OWN_AUTO',\n", " 'FAMILY_INCOME', 'LOAN_NUM_TOTAL', 'LOAN_NUM_CLOSED', 'LOAN_DLQ_NUM', 'LOAN_MAX_DLQ']:\n", " data[col] = data[col].astype('category')\n", " if (data[col].isnull() == True).any():\n", " data[col].cat.add_categories(['Unknown'], inplace=True)\n", " data[col].fillna('Unknown', inplace=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### OWN_AUTO\n", "\n", "Number of cars owned." ] }, { "cell_type": "code", "execution_count": 59, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0 0.885262\n", "1 0.114668\n", "2 0.000070\n", "Name: OWN_AUTO, dtype: float64" ] }, "execution_count": 59, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data['OWN_AUTO'].value_counts(dropna=False, normalize=True)" ] }, { "cell_type": "code", "execution_count": 60, "metadata": { "collapsed": true }, "outputs": [], "source": [ "data.loc[data['OWN_AUTO'] == 2, 'OWN_AUTO'] = 1\n", "data['OWN_AUTO'] = data['OWN_AUTO'].cat.remove_unused_categories()" ] }, { "cell_type": "code", "execution_count": 61, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Counts:\n", "OWN_AUTO\n", "0 12638\n", "1 1638\n", "Name: TARGET, dtype: int64\n", "Frequencies:\n", "0 0.885262\n", "1 0.114738\n", "Name: OWN_AUTO, dtype: float64\n" ] }, { "data": { "image/png": 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QaPWzDURE1iZ/3h8pHncqYY8elM9ZSMMO/xd1JBHpWKsIw+kEwVbAchLLq19p\nTcfWjsTMBD4F/hWGvJg8+W3rn1NE5L/yFi6g+LQxhCUx4nMepOHHP4k6koh0vGqCYAPAgT0Jw5BW\nDpS0toh5FNg0DBmYfN8HeHG9Y4qIJOU9tIiSsaMJC7sTnz2f+p12jjqSiETjRmA2sAgYSRC8Dbzc\nmo7rvJ0UBOxD4lbS7cAJQUDQpN+tgG5ci8h6y3vsYUrGHE9Y0I34/fOp/+luUUcSkeisAn5JGIYE\nwa4kaos3WtOxpTkxB5J4muSmwG+atNej20ki0ga5TzxOyehjITeXinvnUL/7HlFHEpFoXU8YPgRA\nGK4EXmttx3UWMWHIrwGCgGPDkFnfIaCICLlPP0nsuOGQlUV81mzq9ton6kgiEr33CYI7SUxTWfVN\naxjObKlja1cnPRME3ABsAN/cUiIMGb1+OUUkU+U+/xyxkcMgDInPvJ+6PvtFHUlEOoevSNQWezZp\nC0ksKlqn1hYxfwSeTX6F65tORDJbzgt/JTZ8CNTXUzHjXup+vn/UkUSkswjDUW3t2toiJjcMObet\n30REMlfOyy8RGz4YamuouGMWtQccFHUkEekiWrvE+rkgoH8QkNeuaUSkS8l5/VViw44kWFVFxW3T\nqT3ksKgjiUgX0toiZjDwIFAdBDQmvxraMZeIpLnsN/9ObOgRBCsqqfzDVGr7D4g6koh0Ma26nRSG\nbNbeQUSk68h+5216DDmcIB6ncvKt1AwaEnUkEemsguAg4GqgJ4kJvgEQEoZbt9S1tU+xvmxN7WH4\nrb1jRETI9qX0GNyfrK+/pvKmP1Az9OioI4lI5zYZ+BXwFuu5eKi1E3uDJq9zgYPRYwdEpJns998j\ndmR/sr78ksobbqJ6+LFRRxKRzu9LwnBxWzq29nbSFU3fBwFXAo+15RuKSNeU9c8PiA3qT/YXn1N5\nzfVUH6dtpESkVZ4lCCYAjwDV37SG4TMtdWztSExzRcAP2thXRLqYrA//TY8j+5P96SesuOIaqk88\nOepIIpI+dk/+d5cmbSHwi5Y6tnZOzD/5732qLKAHcMN6BBSRLirr4//QY1A/sv/zESvGX8GqU06P\nOpKIpJMw/Hlbu7Z2JKZv028HlIchFW39piLSNWR9+gk9Bh5G9of/ZuUFl7DqzLOjjiQi6SYIegPn\nkbjLEwDZwJaE4VYtdW1tEfMhcDKwf7LPE0HAzWFIY5sCi0jaCz7/nNigfmT/65+s/NV5VJ1zQdSR\nRKQVzCxJUHABAAAgAElEQVQLmAL0AmqAE919WbNjCoHHgRPcfamZ5QJ3AlsB+cBV7r7QzO4HNkl2\n2wp4wd2HmdlEoDdQmfxsgLvH1xLpduA64HhgEnAI8GprrqW1Rcz1wHbJCwiAUcDWwFmt7C8iXUhQ\nVkaPwf3JeX8ZVWecTdUF46OOJCKtdwRQ4O57mdmewI3AN7tRmtluwK3A5k36jAC+cvdjzWwD4HVg\nobsPS/bpCTwJrB6O3RU4yN2/bEWeVYThdIJgK2A5cBLwSmsupLU79v4SGBSGLAxDHiSxg68egCKS\ngYKvv6LH4MPJ8aVUjT2NleN/DUHQYj8R6TR6k1gJhLu/AOzW7PN8YCCwtEnbHODS5OsAqG/W5wpg\nsrt/mhzp2Q6YamZLzKylpYrVBMEGgAN7EoYh0L01F9LaIiaHb4/a5IAeOyCSaYLy5cSGHEHOu2+z\navRJrPzNNSpgRNJPCdD01k6DmX3zN97dl7j7R007uPsKd680s2JgLvDN8KuZbURiusldyabuJDaw\nG0FiX7lTzWyndeSZAMwGFgEjCYK3gZdbcyGtvZ10D/BUEHBf8v3RwL2t7CsiXUBQESd21EBy33yD\nVceOYsU1N6iAEUlPFUBxk/dZ7t58ZOV/mNkWwAJgirs3rQEGA/e6++rBjSpgortXJfs9QWL+zd/X\neOIwnEMQzCUMQ4JgV2B74I3WXEiLIzFBQE9gGnAlib1hjgduCUOuac03EJH0F6yoJHbUIHJfe5VV\nR49gxQ2/h6zWDuSKSCezBDgUIDkn5s2WOpjZxiQ2ub3A3e9s9vEBwMNN3m8PLDGz7OSE4N6sa6Ju\nEPQEphIETwAFwBlArDUXss6RmCBgF+BPwKgw5GHg4SDgGuC3QcAbYbiWqmodzOwi4HAgj8Ts6KdJ\nDEGFJJ6bcJq7N5rZScBYEvfdrnL3xWbWDbgb2IjEjOfj3L1sfTOIyHpYuZLY0YPJfeVvVA8+ihUT\nJquAEUlvC4ADzex5kot1zGw4UOTuU9fS52ISD2i81MxWz405xN1XAQZ8sPpAd3/XzGYBLwB1wEx3\nf3sdeaaRKJB2J/G3/VMSf+sPa+lCgsT8mbV8GPAX4Mow5Klm7QcB54UhB7T0DZoys77AOSRmQRcC\n5wI/BSa4+1NmdivwKPBXEku7diNRlT2XfH0aUOLuvzazYcBe7j5uXd+zrKxyvR4m1RoT57RqlCvt\n5eXnUFvT4ghj2hs3pFfUETqvqipiI4aS99wzVB8xiMopt0NOWzf6Flk/paXFlJVVtnyg/I/S0uL0\nudcbBK8QhrsSBK8Rhrsk294gDFv85dzSP6d6Ni9gAMKQR4EN2xD1IBLDVgtITOBZTGIZ1tPJzx8m\nMSy1O7DE3WuS68qXATvRZEZ1k2NFpD1UVxM77mjynnuGmn4DqPzDNBUwItIe6gmCGKufDBAE20Hr\n9qFr6TdSbhCQ1XxTuyAgi8TtoPW1IbAl0A/4IbCQxISi1aMllSTugzWfOb2m9tVtIpJqNTWUjDqG\nvKefpObgQ6m49Q7IzY06lYh0TZcDTwE/IAgeAPYCWvUE2ZaKmKeTJ7+8Wft4Wrn8qZmvgKXuXgu4\nmVUDWzT5vBgo539nTq+pfXXbOvXsWUhOTnYboq5dXn7m/Gs0E661tLS45YMySW0tDD4G/vI4HHII\n+QvmU5qfH3UqyVD6+cwAYfgIQfAysAeJRw6MJQw/b03Xlv5CXQT8KQg4BvgbiQlAPwW+IDE5d309\nB4wzswnApiTWkv/FzPq6+1Mkthp+EngJuNrMCkhsurMDiUm/q2dUv5Q89tmWvuHy5VVtiLlumTBP\nBDJnTozuuTdRV0fJmFHkP7SI2v1+Tvy2GVBRC9RGnUwykObEtF1aFH9BMHItnxxEEEAYzmzpFOss\nYsKQyiBgX+DnJB6R3Qj8IQxbLh7WJLnCaF8SRUgWiYm6/wSmmVke8C4w190bzGwSiSIlC7jE3avN\n7BZghpk9R+K36vC25BCRNaivp/i0k8h/aCG1vfclPuM+KCiIOpWIdF13kRgU+TOJv+lNJyOHQItF\nzDpXJ3UFWp3UdpkyEqPVSUBDA8VnnEzB3NnU7rk38fvmQfdW7fot0m40EtN2abE6KQh2Bo4CDiSx\nud1s4M+EYasfLq3NHkQyXWMjRb86g4K5s6nbbXcq7p2jAkZE2l8Yvk4YXkQY7gbcQqKYeYkguJUg\n6NuaU3T9WZsisnaNjRSddzbd7rubul1+Svz+eYRFaXAvXUS6ljB8GXiZIOgD/JbEc5eKWuqmIkYk\nU4UhRRefR7dZ06n7SS/isxcQlmjXAhHpQEEQAPsCQ0gs2HmdxMMjF7Wmu4oYkUwUhnS/7CK63TmN\n+h12JD7nAcIePaNOJSKZJAhuIfGU69eAPwIXEIYr1+cUKmJEMk0Y0v3Kyym8bQr19iPK5y4k3OB7\nUacSkcwzlsT+cbskv64haDIfOQy3bukEKmJEMkzhdVdRePNN1G+7HeVzFxGWlkYdSUQy0w+/6wlU\nxIhkkMIbr6P7hBuo/+HWxOcvJtx446gjiUimCsN/f9dTaIm1SIboNmkC3a+7moYfbEV8/mIaN9k0\n6kgiIt+JihiRDNDtlpspuurXNHx/c8rnL6Lx+5tHHUlE5DtTESPSxRXcfitFl19Mw6abUT5/MY0/\n2DLqSCIiKaEiRqQLK7jrDoovPp+GjTYmPn8RjT9scbK/iEjaUBEj0kUV3DuL4vPPpnHDDYnPX0zD\nNttFHUlEJKVUxIh0Qfl/vI+is0+ncYMNKJ+7iIbtLepIIiIppyJGpIvJnz+H4jNPIYzFKJ+zkIb/\n2zHqSCIi7UJFjEgXkrfoAYpPG0NYVEx8zoM0/GSnqCOJiLQbFTEiXUTeww9RMnY0YbdC4rPnU99r\nl6gjiYi0KxUxIl1A3uOPUHLiSMjLJ37fPOp3/VnUkURE2p2KGJE0l/vEnykZNQJycojfO4f6PfaM\nOpKISIdQESOSxnKffZrY8cMhK4v4rNnU7d076kgiIh1GD4AUSVO5f11C7NijoLGR+Mz7qNu3b9SR\nREQ6lIoYkTSU89KLxI4eDHV1VEy/m7pfHBh1JBGRDqciRiTN5Lz6MrFhg6C2horbZ1L7y0OijiQi\nEgkVMSJpJOfvrxMbOpCgaiUVU6dTe2i/qCOJiERGRYxImsh+601iQwYQVFZQOWUatYcPjDqSiKQh\nM8sCpgC9gBrgRHdf1uyYQuBx4AR3X2pmucCdwFZAPnCVuy80s12AxcB7ya63uPtsMzsJGAvUJ49d\n3B7XotVJImkg+9136DHkcILycionTqHmyKFRRxKR9HUEUODuewEXAjc2/dDMdgOeAbZp0jwC+Mrd\n+wAHAzcn23cFJrh73+TXbDPbBDgT2Ac4CLjWzPLb40I0EiPSyWW/9w96HNmfrK++onLCZGqGHRN1\nJBFJb72BRwDc/YVk0dJUPjAQmNWkbQ4wN/k6IDHCAokixsxsAInRmLOA3YEl7l4D1JjZMmAn4G+p\nvhCNxIh0YtkfLCM2qB9ZX5ZRed0EqkccF3UkEUl/JUC8yfsGM/tmUMPdl7j7R007uPsKd680s2IS\nxcz45EcvAee5+77AB8Dlazh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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "IV: 0.00335633471077\n" ] }, { "data": { "text/html": [ "
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% responders% non-respondersWOEDG-DBIV
00.8686050.887544-0.021570-0.0189390.000409
10.1313950.1124560.1556470.0189390.002948
\n", "
" ], "text/plain": [ " % responders % non-responders WOE DG-DB IV\n", "0 0.868605 0.887544 -0.021570 -0.018939 0.000409\n", "1 0.131395 0.112456 0.155647 0.018939 0.002948" ] }, "execution_count": 61, "metadata": {}, "output_type": "execute_result" } ], "source": [ "functions.feature_stat(data, 'OWN_AUTO', 'TARGET')\n", "functions.calc_iv(data, 'TARGET', 'OWN_AUTO')[0]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## GENDER" ] }, { "cell_type": "code", "execution_count": 62, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Counts:\n", "GENDER\n", "0 4936\n", "1 9340\n", "Name: TARGET, dtype: int64\n", "Frequencies:\n", "1 0.654245\n", "0 0.345755\n", "Name: GENDER, dtype: float64\n" ] }, { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "IV: 0.00857138118803\n" ] }, { "data": { "text/html": [ "
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% responders% non-respondersWOEDG-DBIV
10.6151160.659605-0.069830-0.0444890.003107
00.3848840.3403950.1228340.0444890.005465
\n", "
" ], "text/plain": [ " % responders % non-responders WOE DG-DB IV\n", "1 0.615116 0.659605 -0.069830 -0.044489 0.003107\n", "0 0.384884 0.340395 0.122834 0.044489 0.005465" ] }, "execution_count": 62, "metadata": {}, "output_type": "execute_result" } ], "source": [ "functions.feature_stat(data, 'GENDER', 'TARGET')\n", "functions.calc_iv(data, 'TARGET', 'GENDER')[0]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## CHILD_TOTAL" ] }, { "cell_type": "code", "execution_count": 63, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "1 0.333217\n", "0 0.327473\n", "2 0.272065\n", "3 0.053026\n", "4 0.008826\n", "5 0.003993\n", "6 0.000841\n", "7 0.000350\n", "10 0.000140\n", "8 0.000070\n", "Name: CHILD_TOTAL, dtype: float64" ] }, "execution_count": 63, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data['CHILD_TOTAL'].value_counts(dropna=False, normalize=True)" ] }, { "cell_type": "code", "execution_count": 64, "metadata": { "collapsed": true }, "outputs": [], "source": [ "data['CHILD_TOTAL'].cat.add_categories(['3 or more'], inplace=True)\n", "data.loc[data['CHILD_TOTAL'].isin([1.0, 0.0, 2.0]) == False, 'CHILD_TOTAL'] = '3 or more'\n", "data['CHILD_TOTAL'] = data['CHILD_TOTAL'].cat.remove_unused_categories()" ] }, { "cell_type": "code", "execution_count": 65, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Counts:\n", "CHILD_TOTAL\n", "0 4675\n", "1 4757\n", "2 3884\n", "3 or more 960\n", "Name: TARGET, dtype: int64\n", "Frequencies:\n", "1 0.333217\n", "0 0.327473\n", "2 0.272065\n", "3 or more 0.067246\n", "Name: CHILD_TOTAL, dtype: float64\n" ] }, { "data": { "image/png": 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sSdWEi72uRrKUgomIiDQI/+wnKfjmG6ouvxqCQa/LkSylYCIiIpmX1CJcqRZh+RYKJiIi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\nkvmBCcADSfueAGYmth2gZqn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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "IV: 0.00436821503813\n" ] }, { "data": { "text/html": [ "
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% responders% non-respondersWOEDG-DBIV
3 or more0.0819770.0652280.2285500.0167493.827978e-03
10.3226740.334661-0.036473-0.0119864.371814e-04
00.3279070.3274130.0015070.0004947.441446e-07
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" ], "text/plain": [ " % responders % non-responders WOE DG-DB IV\n", "3 or more 0.081977 0.065228 0.228550 0.016749 3.827978e-03\n", "1 0.322674 0.334661 -0.036473 -0.011986 4.371814e-04\n", "0 0.327907 0.327413 0.001507 0.000494 7.441446e-07\n", "2 0.267442 0.272698 -0.019464 -0.005256 1.023110e-04" ] }, "execution_count": 65, "metadata": {}, "output_type": "execute_result" } ], "source": [ "functions.feature_stat(data, 'CHILD_TOTAL', 'TARGET')\n", "functions.calc_iv(data, 'TARGET', 'CHILD_TOTAL')[0]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## DEPENDANTS" ] }, { "cell_type": "code", "execution_count": 66, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0 0.538386\n", "1 0.297772\n", "2 0.144088\n", "3 0.016251\n", "4 0.002802\n", "5 0.000350\n", "6 0.000280\n", "7 0.000070\n", "Name: DEPENDANTS, dtype: float64" ] }, "execution_count": 66, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data['DEPENDANTS'].value_counts(dropna=False, normalize=True)" ] }, { "cell_type": "code", "execution_count": 67, "metadata": { "collapsed": true }, "outputs": [], "source": [ "data['DEPENDANTS'].cat.add_categories(['2 or more'], inplace=True)\n", "data.loc[data['DEPENDANTS'].isin([1.0, 2.0]) == False, 'DEPENDANTS'] = '2 or more'\n", "data['DEPENDANTS'] = data['DEPENDANTS'].cat.remove_unused_categories()" ] }, { "cell_type": "code", "execution_count": 68, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Counts:\n", "DEPENDANTS\n", "1 4251\n", "2 2057\n", "2 or more 7968\n", "Name: TARGET, dtype: int64\n", "Frequencies:\n", "2 or more 0.558140\n", "1 0.297772\n", "2 0.144088\n", "Name: DEPENDANTS, dtype: float64\n" ] }, { "data": { "image/png": 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r5xtj0oBngeYE7wDubq3NjWReaRiJ//k32Rd1xsnLI3/iY5RcdqXXkUR2WuGQ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\ngbnW2nXAJILFYykwxFpbDEwBjjTGrAB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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "IV: 0.00743729564542\n" ] }, { "data": { "text/html": [ "
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% responders% non-respondersWOEDG-DBIV
2 or more0.525000.562679-0.069311-0.0376790.002612
10.308140.2963520.0390040.0117870.000460
20.166860.1409680.1686220.0258920.004366
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" ], "text/plain": [ " % responders % non-responders WOE DG-DB IV\n", "2 or more 0.52500 0.562679 -0.069311 -0.037679 0.002612\n", "1 0.30814 0.296352 0.039004 0.011787 0.000460\n", "2 0.16686 0.140968 0.168622 0.025892 0.004366" ] }, "execution_count": 68, "metadata": {}, "output_type": "execute_result" } ], "source": [ "functions.feature_stat(data, 'DEPENDANTS', 'TARGET')\n", "functions.calc_iv(data, 'TARGET', 'DEPENDANTS')[0]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## EDUCATION" ] }, { "cell_type": "code", "execution_count": 69, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Professional School 0.432544\n", "Some High School 0.308700\n", "Undergraduate Degree 0.200406\n", "No Formal Education 0.034744\n", "Some Primary School 0.022345\n", "Post-Graduate Work 0.001191\n", "Graduate Degree 0.000070\n", "Name: EDUCATION, dtype: float64" ] }, "execution_count": 69, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data['EDUCATION'].value_counts(dropna=False, normalize=True)" ] }, { "cell_type": "code", "execution_count": 70, "metadata": { "collapsed": true }, "outputs": [], "source": [ "data.loc[data['EDUCATION'].isin(['Undergraduate Degree', 'Post-Graduate Work', 'Graduate Degree']),\n", " 'EDUCATION'] = 'Undergraduate Degree'\n", "data.loc[data['EDUCATION'].isin(['Some High School', 'No Formal Education', 'Some Primary School']),\n", " 'EDUCATION'] = 'Some High School'\n", "data['EDUCATION'] = data['EDUCATION'].cat.remove_unused_categories()" ] }, { "cell_type": "code", "execution_count": 71, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Counts:\n", "EDUCATION\n", "Professional School 6175\n", "Some High School 5222\n", "Undergraduate Degree 2879\n", "Name: TARGET, dtype: int64\n", "Frequencies:\n", "Professional School 0.432544\n", "Some High School 0.365789\n", "Undergraduate Degree 0.201667\n", "Name: EDUCATION, dtype: float64\n" ] }, { "data": { "image/png": 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abnuKr7uRwG+/kXP/vX7HEZFGoL/tRCSuFV92FZW77Er2E+MIfPO133FEJMZUTEQkvmVn\nUzR0JE55OXkjh/qdRkRiTMVEROJe6cmnUX5YRzLf+A/pc9/3O46IxJCKiYjEP8ehcPSdAOQNuQ0q\nK30OJCKxomIiIgmh4qBDCJ11DsHPl5D1r+f8jiMiMaLhwiIJ7MGXFvsdAYCMzCBlpRUNb7iV8g88\nk+unTsEZNpTHq/aiNDt3g/UDzmwX8wwiEls6YyIiCWP9Ni14/9jzyFu/miPefNbvOCISAyomIpJQ\n5vb8O2uataLzey/S7Lef/I4jIlGmYiIiCaUiI5M3T72CYEU5x00d53ccEYkyFRMRSThL2vdkxe77\ns/+i2bT+6lO/44hIFKmYiEjicRxeP+MaAHq//DBOlYYPiyQLFRMRSUg/tt6PTw87jh2//4qDF8zw\nO46IRImKiYgkrLdPvpSyjCyOee0JMkLFfscRkSiI6TwmxphPgHXht98CY4CnARf4DLjKWltljLkE\nuAyoAEZba6cZY7KB54BWwHqgr7V2VSzzikhiWdesFXOOOYeer0/0hg+f38nvSCKylWJ2xsQYkwU4\n1toe4a9+wFhgsLW2G+AApxhjtgeuBboAxwF3GGMygSuAJeFtnwEGxyqriCSuuUefzdptWtF55osE\nvlvudxwR2UqxvJTTDsgxxrxljJlpjOkItAdmh9dPB44GDgMKrLWl1tq1wDLgQKArMKPOtiIiGyjP\nyOLNUy8nvaKM3FHD/I4jIlsplpdyioF7gSeBvfDKhWOtdcPr1wNNgSbA2lr71be8etkmNWuWQzCY\nFpXw4E2zHS/iIUvLlvl+R5A64uG4qOZnlqVdjueH96ew82tTyVp6PXTr5lsWiV/6GZYYYvmT5H/A\nsnAR+Z8x5ne8MybV8oE1ePeg5DewvHrZJq1eHd2b3xrj2R+RaKznkDRk1ar1fkeQOuLhuID4OEan\n9bmay++9nPKrr2XNW7MgoHv75U8tW+Yn5c+wZCxbsfyTexFwH4AxZke8MyBvGWN6hNf3AuYAC4Fu\nxpgsY0xTYF+8G2MLgN51thURqdcPu7cldPrfSP/vIjJf/JffcURkC8WymEwAtjHGzAX+jVdUBgAj\njDHzgQxgsrV2JfAQXvGYCQyy1oaAx4C24f0vBUbEMKuIJIGiwcNxs7PJHT0cCgv9jiMiWyBml3Ks\ntWXAOfWsOqKebccD4+ssKwbOjE06EUlGVTvtTPFVA8i9905yHh5L8W1D/Y4kIptJF2FFJKkUXzWA\nyh12JGfcwwS+X+F3HBHZTComIpJccnMpGjwcp7SU3FE6YyKSaFRMRCTplJ7+N8oPaU/WK1MIfrDA\n7zgishlUTEQk+QQCFI66E4C8IbdAVZXPgUQkUiomIpKUKg49nFCfM0hf9CmZL73gdxwRiZCKiYgk\nraLBI3CzssgdM0LDh0UShIqJiCStqp13ofjKa0lb+TM5jzzgdxwRiYCKiYgkteKrr6Ny+x3IGfcQ\ngR++9zuOiDRAxUREklteHkWDhuGEQuSO1tOHReKdiomIJL3SM/9O+UEHkzVlMsGFH/gdR0Q2QcVE\nRJJfIEDhqLsAyBt6q4YPi8QxFRMRSQkVh3ckdGof0j/5mMyXX/Q7johshIqJiKSMoiEjcTMzvacP\nFxX5HUdE6qFiIiIpo2qXXSm+8hrSfv6JnEcf9DuOiNRDxUREUkrxNTdQ2Wo7ch59kMCPP/gdR0Tq\nUDERkdSSl+c9fbikxLukIyJxRcVERFJO6d/OpvzAg8h6+UWCHy30O46I1BL0O4CISKMLBCgafSfb\nnHw8eUNuZc0b74Lj+J1KJKqMMQFgHNAOKAUuttYuq7NNDvA20N9au9QYkw5MBFoDmcBoa+1rxpg2\nwNOAC3wGXGWtjcm4e50xEZGUVN6xM6GTTyP944/InPKS33FEYuFUIMta2wm4Fbiv9kpjTAfgfWDP\nWovPA3631nYDjgceCS8fCwwOL3eAU2IVWsVERFJW0ZAR3vDhUcOguNjvOCLR1hWYAWCtXQB0qLM+\nEzgNWFpr2UvAkPBrB6gIv24PzA6/ng4cHYO8gIqJiKSwqt1aU3L51aT99CM54x7yO45ItDUB1tZ6\nX2mMqbmFw1pbYK3d4MmW1tpCa+16Y0w+MBkYHF7lWGvd8Ov1QNNYhVYxEZGUVjzgBqpatiLnkQcI\n/PSj33FEomkdkF/rfcBaW7GxjasZY3YB3gOetdY+H15c+36SfGBN1FLWoWIiIinNzcv3nj5cXEzu\nmBF+xxGJpgKgN4AxpiOwpKEdjDHbAW8Bt1hrJ9Za9akxpkf4dS9gTnSj/knFRERSXuiscyg/oB1Z\nL71A8JOP/I4jEi1TgZAxZh5wP3C9MeYcY8ylm9hnINAMGGKMmRX+ygZuBEYYY+YDGXiXeWJCw4VF\nRNLSKBp1B9uc2pu8wbey5vW3NXxYEl54OO/ldRYvrWe7HrVeDwAG1PNx/wOOiGa+jdEZExERoLxz\nV0pPPIX0jxaS+crLfscRSVkqJiIiYYVDR+JmZJA7ciiUlPgdRyQlqZiIiIRVtd6dksuuIu3HH8h5\n7GG/44ikJBUTEZFaiq+7kaoWLcl5aCyBlT/7HUck5aiYiIjU4uY3oWjgUA0fFvGJiomISB2hs8+j\nou0BZP37eYKLPvE7jkhKUTEREakrLY3CUXcAkDf4VnDdBnYQkWhRMRERqUd51+6U9j6J9IULyHxt\nqt9xRFKGiomIyEYUDhuFm56u4cMijUjFRERkI6p234OSS68k7fsV5PzzUb/jiKQEFRMRkU0ovv4f\nVLVoQc4D9xH4ZaXfcUSSnoqJiMgmuE2aUnTrEJziInJuH+l3HJGkp2IiItKA0LkXULHf/mS98H8E\nF3/qdxyRpKZiIiLSkPDwYcd1yR1ym4YPi8SQiomISATKux1B6fEnkLFgHhnTXvU7jkjSUjEREYlQ\n0XBv+HDeiCEQCvkdRyQpqZiIiESoco82lFx8OWkrviP7iXF+xxFJSiomIiKbofiGm6jadlty7r8X\n55df/I4jknSCsfxwY0wr4GPgGKACeBpwgc+Aq6y1VcaYS4DLwutHW2unGWOygeeAVsB6oK+1dlUs\ns4qIRMJtug1Ftwwm/+bryb1zFIX3P+J3JJGkErMzJsaYdOCfQPU8zmOBwdbaboADnGKM2R64FugC\nHAfcYYzJBK4AloS3fQYYHKucIiKbK3ReXyr23Y+s558luGSx33FEkkosz5jcCzwO3BZ+3x6YHX49\nHTgWqAQKrLWlQKkxZhlwINAVuLvWtkMi+YbNmuUQDKZFJz2QkRnTE0qbJR6ytGyZ73cEqSMejotq\n8ZClUY/RBx+AY4+l2cjB8N574DiN971li+hnWGKIyU8SY8yFwCpr7ZvGmOpi4lhrqwf/rweaAk2A\ntbV2rW959bIGrV5dvJXJN1RWWhHVz9tSGZnBuMiyatV6vyNIHfFwXECKHqMHdaTJcb3IfHM6ayf9\ni7ITTmq87y2brWXL/KT8GZaMZStWl3IuAo4xxswCDsK7HNOq1vp8YA2wLvx6U8url4mIxJWi4aNx\ng0Hyhg+C0lK/44gkhZgUE2ttd2vtEdbaHsAi4AJgujGmR3iTXsAcYCHQzRiTZYxpCuyLd2NsAdC7\nzrYiInGlcs+9KOl/GWnfLSd7/ON+xxFJCo05XPhGYIQxZj6QAUy21q4EHsIrHjOBQdbaEPAY0NYY\nMxe4FBjRiDlFRCJWfOPNVDVvTs7Yu3F+/dXvOCIJL+Z3q4XPmlQ7op7144HxdZYVA2fGNpmIyNZz\nt2lG0c2DyL/1RnLvGkPhfQ/6HUkkoWmCNRGRrRS6oB8VZh+y/m8SaZ8t8TuOSEJTMRER2VrBIIUj\n78CpqiJvqJ4+LLI1VExERKKg/MielB5zHBlz3ydjxht+xxFJWComIiJRUjR8jDd8eNhADR8W2UIq\nJiIiUVK5196UXHQJacu/JXvCE37HEUlIKiYiIlFUfOMtVDVrRs59d+H89pvfcUQSjoqJiEgUuc2a\nU3TTbQTWryP3rjF+xxFJOComIiJRFurbn4q99ibr2adI++Jzv+OIJBQVExGRaEtPp2jk7d7w4SEa\nPiyyOVRMRERioKznsZQddTQZc2aR8dYMv+OINC7HycVxDsRxHBwnd3N2VTEREYmRwhG346alkTts\nIJSV+R1HpHE4Tk9gMfAqsD2wHMc5NtLdVUxERGKk0uxD6ML+BL/5muyJGj4sKeN2oCuwBtf9Ge85\nefdEurOKiYhIDBXddBtVTbch5967cH7/3e84Io0hgOuurHnnul9s3s4iIhIzbvNtKb7pVgLr1pJ7\nt4YPS0r4Acc5EXBxnG1wnEHAikh3VjEREYmxkn6XUNFmL7ImTSTty836x6NIIroMOBfYBfgaOAi4\nJNKdVUxERGItPZ2iEWP09GFJFe1w3bNx3Za47ra47plA50h3DsYwmIiIhJUdfRxlPY4iY9ZMMt55\nk7Jjjvc7kkh0Oc5ZQCYwEscZWmtNEBgITInkY3TGRESkMTgOhSPvwA0EyB06EMrL/U4kEm1NgCOB\n/PB/q786AYMi/ZCIz5g4Dju4Lj87Dt2AA4GnXZeizYosIpLCKvfZl1Dfi8h+6kmynxpPyaVX+h1J\nJHpcdzwwHsfpieu+u6UfE9EZE8fhMWCw47Af8DxwCPDMln5TEZFUVXTzIKqaNCXnnjtx/tDwYUlK\npTjOqzjOuzjOTBxnNo6zPNKdI72UcxhwNfA3YILr0h/YdfOzioikNnfbbSn+xy0E1q4h9547/I4j\nEgtPAq/gXZV5FPgKmBrpzpEWk7TwtqcA0x2HHGCz5r4XERFPyUWXUrHHnmQ9PYE0u9TvOCLRVoLr\nPgXMAlbjDRU+ItKdIy0mzwA/A8tdlw+Aj4F/bl5OEREBICODohG341RWkjdsoN9pRKIthOM0ByzQ\nEdd12YyTGZEWkzeBHVyX08LvuwEfbFZMERGpUXbs8ZR1P5KMme+Q8e5bfscRiab7gH8D/wEuwHE+\nBz6KdOdNFhPHoYvj0B3v2lBnx6F7+P2B6OZXEZEt5zgUjrxdw4clGZUAx+K664H2wHnA+ZHu3NBw\n4WPwrgvtAIystbwCXcoREdkqlfu1JXR+P7InTSBr0gRCF1/udySRaLgb130dANctAj7dnJ03WUxc\nl+EAjsP5rsuzWxhQREQ2ouiWQWROeYncu2+n9PS/4TZr7nckka31NY4zEe+Wj5Kapa4b0ZWWSO8x\ned9xuMdxmOA4TKz+2vysIiJSm9uiBcU33kJgzRpy7r3T7zgi0fA74AAd+XP21x6R7hzpzK8vAnPC\nX3r6lIhIFJVcfBlZkyaQPXE8ob79qdzb+B1JZMu5br+t2T3SYpLuuvxja76RiIhsREYGRcPH0LTv\n2eQOH8S65yf7nUjEN5FeypnrOJzkOGTENI2ISIoqO743Zd2OIPOdt0if+bbfcUR8E2kxOQN4FQg5\nDlXhr8oY5hIRSS21nj6cN3QgVFT4nUjEFxEVE9dlR9clUOcrLdbhRERSSWXb/Qmd25fg/yxZkzS+\nQBKU4xyH43yE43yN43yD43yL43wT6e4R3WPiOAytb7nrbjC3iYiIbKWiWweTOXUyuXePofT0M3G3\naeZ3JJHN9TBwA/AZWzBgJtJLOU6trwzgZGC7zf1mIiKyaW7LlhTfcDOB1avJue8uv+OIbInfcN1p\nuO5yXPe7mq8IRXTGxHUZUfu94zAK0MMdRERioOSSy8meNIHsCU94w4fb7OV3JJHNMQfHGQvMAEI1\nS133/Uh2jnS4cF15wK5buK+IiGxKZiaFw8fQtN+53vDh5170O5EkIGNMABgHtANKgYuttcvqbJMD\nvA30t9YurbX8cOAua22P8PuDgWnAV+FNHrPW/nsj3/qw8H8PrrXMBY6KJHek95h8y5/XiQLANsA9\nkewrIiKbr6z3iZR16UbmWzNIf+9dyo/s6XckSTynAlnW2k7GmI54T/09pXqlMaYD8Diwc+2djDE3\n4z10r6jW4vbAWGvtfQ1+V9c9cmtCR3qPSQ/+nFa2O7Cr6zJma76xiIhsQvXwYcchb5iGD8sW6Yp3\nOQVr7QKgQ531mcBpwNI6y78G+tRZ1h44wRjzvjFmgjEmf6Pf1XG64jiv4jjv4jgzcZzZOM7ySENH\nWkxWAL0CRstjAAAgAElEQVTx2tZDwIWOE/G+IiKyBSoPOJDQuRcQXPolWc8+7XccSTxNgLW13lca\nY2qulFhrC6y139fdyVr7MlBeZ/FC4CZrbXfgG2DYJr7vk8AreFdlHsW7/DM10tCRlou7geOAZ4Cn\n8K4TjY30m4iIyJYpunUIVXn55N41GmftGr/jSGJZB9Q+sxGw1m7pqbep1tqPq1+z4f0jdZXguk8B\ns4DVwCXAEZF+o0iLybFAH9flNdflVbyZYI+L9JuIiMiWcVu1ovi6fxD44w9y7rvb7ziSWArwrnYQ\nvsdkyVZ81pvGmOqbWnsCH29i2xCO0xywQEdc1wVyI/1GkY7KCYa/ymq93+SU9MaYNGA8YPBunL0c\nb9jQ0+H3nwFXWWurjDGXAJcBFcBoa+00Y0w28BzQClgP9LXWror0FyYikixKLr2C7GeeIvvJxwn1\n7Uflnho+LBGZChxjjJmHNw9ZP2PMOUCetfaJzfysK4CHjTHlwErg0k1sOxb4N959Kh/iOOcCH0X6\njSItJv8HzHIc/hV+fzbwfAP7nARgre1ijOkBjMH7jRlsrZ1ljHkcOMUYMx+4Fu+mnCxgrjHmbbzf\nhCXW2uHGmL8Dg4EBkf7CRESSRlYWhcNG0bT/+eSOGMK6Z17wO5EkAGttFd5Jgdrq3uhK9ZDgOsuW\nAx1rvf8E6BLRN3bdl3Ccybiui+O0B/YGFkeau8FLOY5DM7wzH6Pw5i65EHjMdbl9U/tZa1/hz0a1\nG7AG767e2eFl04Gj8cY7F1hrS621a4FlwIHUupu41rYiIimp7MSTKevUhcwZb5A++z2/44hsnOM0\nA57AcWbinXC4Bmga6e6bPGPiOBwMvAH0c12mA9Mdh9uBOx2Hxa7Lfze1v7W2whgzCW840hnAMdba\n6vlQ1oeD1r1ruL7l1cs2qVmzHILB6D1bMCNzS+efi754yNKy5cZHh4k/4uG4qBYPWZL+GH3kIejQ\ngW1GDIJPP4Wg/7/niSTpj4/4MR5vdvjD8P7+/hnv1owTItm5oaP6XuBs12VW9QLXZaDjMBvvGlKD\nZzGstX2NMbcAHwDZtVbl451FqXvXcH3Lq5dt0urVxQ1tslnKSuNj3oCMzGBcZFm1ar3fEaSOeDgu\nQMdoo9llL/LOPo/s559l/QOPEup7kd+JEkbLlvlJeXzEadnaHdd9Ase5AtctAwbhOFG7lNOsdimp\n5rq8CbTY1I7GmPONMbeF3xYDVcBH4ftNAHoBc/DGRnczxmQZY5oC++LdGFtzN3GtbUVEUlrxbUOo\nys0j985ROOvWNryDSOOrwHGaUj1jvOPshdcBItJQMUmvbyK18LKMBvadAhxsjHkfeBO4DrgKGBG+\n4TUDmGytXYk3adscYCYwyFobAh4D2hpj5uLdqzKinu8hIpJSqrbbnuLrbiTw++/kjNWTQSQuDcOb\nw2Q3HOcVYC7eAJaION7w4o2sdHgE+N11N5zhzXEYCrRxXS7YksSxsmrV+o3/YrbAgy9FfOYppuLl\nNPmAM9v5HUHq0DG6oZQ5RkMhmnfpQGDlz/wxZyFVe+zpd6K4l8SXchy/M9TLcVoAhwNpwAe47i+R\n7trQPSa3AW84DucCH+IN9z0E+BU4ecvSiojIVqkePnxxX/JGDGHdpIZmbxBpBI6zsZMVx+E44LrP\nRPIxmywmrst6x6E73sP7Dsa7RvSo6+p+DxERP5WddCrlh3cic/o00ufMprxbxDN+Nyqd1dtQkp/V\nexrvxMU7eBOy1j6b4+I91qZBDY41c11cvHs/Zm52RBERiQ3HoXD0nWxzbA/yhtzG6nfnQFr0pksQ\n2QKHAGcBx+BNqPYC8A6uG/GNrxD5s3JERCTOVLQ7mNKzziH4xWdkPf+s33Ek1bnuIlz3Nly3A94A\nlmOAhTjO4zhOj0g/RsVERCSBFQ0cipuTS+4dIzV8WOKH636E694EXA8cAEyLdFcVExGRBFa1/Q4U\nD7iBwG+/kfPAfX7HkVTnOA6OcwSO8wiO8zXeVCEPA9tF+hEqJiIiCa748qup3HkXsp8YR+Dbb/yO\nI6nKcR4DvsF74O5c4EBc93Rc9wVctyjSj1ExERFJdNnZFA0diVNWRt7IoX6nkdR1GZCHN4r3DmAJ\njvNNzVeE9AQoEZEkUHpKH8rHP07m66+RXjCH8i7d/I4kqWf3aHyIzpiIiCSD8PBhgNwht0Flpc+B\nJOW47neb/IqQiomISJKoOLg9ob+dTfpn/yXrhf/zO47IFlExERFJIkWDhuHm5JB7+0ic9ev8jiOy\n2VRMRESSSNUOO1J8zfUEVv1KzoNj/Y4jstlUTEREkkzxFddQudPOZD/+CIHvlvsdR2SzqJiIiCSb\nnByKhozQ8GFJSComIiJJqPS0MyhvfyiZ/3mF9PkFfscRiZiKiYhIMqo9fHjwrRo+LAlDxUREJElV\ntD+U0Blnkb5kMZkv/svvOCIRUTEREUliRYOH42ZnkztmBE7her/jiDRIxUREJIlV7bgTxVdfR9qv\nv5D90P1+xxFpkIqJiEiSK75qAJU77kTOYw8TWBHxzOAivlAxERFJdjk5FA0ejlNaSu6oYX6nEdkk\nFRMRkRRQ2udMytt3IOvVKQQXzPc7jshGqZiIiKSCQIDCUd7w4bwht0JVlc+BROqnYiIikiIqOhxG\nqM+ZpC/+VMOHJW6pmIiIpJCiISO84cO3j4TCQr/jiPyFiomISAqp2mlniq+8lrSVP5PzyAN+xxH5\nCxUTEZEUU3z1dVRuvwM54x4i8MP3fscR2YCKiYhIqsnN9YYPh0LkjtbwYYkvKiYiIimo9IyzKD/4\nELKmTCa48AO/44jUUDEREUlFgQCFo+4CIG+ohg9L/FAxERFJURWHHU7otNNJ/+RjMl9+0e84IoCK\niYhISisaPAI3K4vc0cOhqMjvOCIqJiIiqaxql10pvvIa0n7+iZxHH/Q7joiKiYhIqiu++noqt9ue\nnEcfJPDjD37HkRSnYiIikury8igaNAynpMS7pCPiIxUTERGh9G9nU97uYLJefpHgRwv9jiMpTMVE\nRET++vRh1/U5kKQqFRMREQGgomMnQqf0If3jj8ic8pLfcSRFqZiIiEiNoiEjcDMzyR01DIqL/Y4j\nKUjFREREalTtuhvFV1xD2k8/kjPuIb/jSApSMRERkQ2UXHs9la22I+eRBwj89KPfcSTFBGPxocaY\ndGAi0BrIBEYDXwBPAy7wGXCVtbbKGHMJcBlQAYy21k4zxmQDzwGtgPVAX2vtqlhkFRGRDbl5+RQN\nGkaTAVeSO2YE6x99wu9IkkJidcbkPOB3a2034HjgEWAsMDi8zAFOMcZsD1wLdAGOA+4wxmQCVwBL\nwts+AwyOUU4REalH6VnnUH5AO7JeeoHgJx/5HUdSSKyKyUvAkPBrB+9sSHtgdnjZdOBo4DCgwFpb\naq1dCywDDgS6AjPqbCsiIo0lEKBodHj48GANH5bGE5NLOdbaQgBjTD4wGe+Mx73W2uojez3QFGgC\nrK21a33Lq5c1qFmzHILBtK3OXy0jMya/PVskHrK0bJnvdwSpIx6Oi2rxkEXHaJSdfDyccQbpkyfT\ncuYb8Pe/b/ZHxMNxUS0esugYbVjM/i8ZY3YBpgLjrLXPG2PurrU6H1gDrAu/3tTy6mUNWr06ukPb\nykorovp5WyojMxgXWVatWu93BKkjHo4L0DGazAK3DKX5a69R9Y+b+KPzUZCdvVn7x8NxAcl7jCZj\n0YnJpRxjzHbAW8At1tqJ4cWfGmN6hF/3AuYAC4FuxpgsY0xTYF+8G2MLgN51thURkUZWtVtrSi6/\nmrQffyDnsYf9jiMpIFb3mAwEmgFDjDGzjDGz8C7njDDGzAcygMnW2pXAQ3jFYyYwyFobAh4D2hpj\n5gKXAiNilFNERBpQPOAGqlq2IuehsQRW/ux3HElysbrHZAAwoJ5VR9Sz7XhgfJ1lxcCZscgmIiKb\nx81vQtHAoeRff7U3fPjhx/2OJElME6yJiEiDQn8/l/L9DyTr388TXPSJ33EkiamYiIhIw9LSKBp1\nB6DhwxJbKiYiIhKR8i7dKD3hZNIXLiDztal+x5EkpWIiIiIRKxw6Ejcjg9yRQ6GkxO84koRUTERE\nJGJVu+9ByaVXkvb9CnL++ajfcSQJqZiIiMhmKb7+H1S1aEnOA/cR+GWl33Ekyfg/P6+IiCQUN78J\nRbcNIf/Ga8m5fSSFD47zO5LUwxgTAMYB7YBS4GJr7bI62+QAbwP9rbVLay0/HLjLWtsj/L4N8DTg\n4k2EepW1tioWuXXGRERENlvonPOp2G9/sl74P4KLP/U7jtTvVCDLWtsJuBW4r/ZKY0wH4H1gzzrL\nbwaeBLJqLR4LDLbWdsN7OO8psQqtYiIiIpsvLY3C0XfiuC65Q27T8OH41BWYAWCtXQB0qLM+EzgN\nWFpn+ddAnzrL2gOzw6+nA0dHNWktKiYiIrJFyrt2p7TXiWQsmEfGtFf9jiN/1QRYW+t9pTGm5hYO\na22Btfb7ujtZa18Gyussdqy11e1zPdA02mGrqZiIiMgWKxw2Cjc9nbwRQyAU8juObGgdUPvxwwFr\n7ZY+Yrn2/ST5wJotTtUAFRMREdliVXvsScklV5C24juyn9BNsHGmAOgNYIzpCCzZis/61BjTI/y6\nF97Dd2NCxURERLZK8Q03UdWiBTn334vzyy9+x5E/TQVCxph5wP3A9caYc4wxl27BZ90IjDDGzAcy\ngMlRzLkBDRcWEZGt4jZpStEtg8m/6Tpy7xxF4f2P+B1JgPBw3svrLK57oyvVQ4LrLFsOdKz1/n/A\nEdFNWD+dMRERka0WOvcCKvZtS9bzzxJcstjvOJLAVExERGTrBYMUjrpDw4dlq6mYiIhIVJR370Hp\n8b3JmDeXjNf/43ccSVAqJiIiEjVFw0d7w4eHD4bSUr/jSAJSMRERkaip3KMNJf0vI23FcrKfeMzv\nOJKAVExERCSqim+8marmzcm5/x5y1/3hdxxJMComIiISVW7TbSi6ZTCBwvUcPe1Jv+NIglExERGR\nqAudfyEV++xL+3nT2P77r/yOIwlExURERKIvGKRw5B0EXJfeLz+s4cMSMRUTERGJifIeR7F0/87s\n8dWn7PvfmD1aRZKMiomIiMTMjD5XURlI4/gp40grL/M7jiQAFRMREYmZ37bblQVH9GHb336k4+yX\n/Y4jCUDFREREYuq93v0ozm3CkdMnkbt+td9xJM6pmIiISEyFcvJ594T+ZIWK6Dltgt9xJM6pmIiI\nSMx92PVkftm+NR0K/sN2Py7zO47EMRUTERGJuaq0INNPv5qAW0Xvlx/R8GHZKBUTERFpFMv2Oxzb\ntiN72o/ZZ0mB33EkTqmYiIhIo5ne5+rw8OFHSaso9zuOxCEVExERaTS/bb8bH3Q/jRarfuBwDR+W\neqiYiIhIo3qvdz+Kc/I5cvokcjR8WOpQMRERkUZVktuEmSdcRHZJIT1fn+h3HIkzKiYiItLoFnY7\nlV+3241D575Gq5++8TuOxBEVExERaXRVaUFm9LkqPHxYTx+WP6mYiIiIL/7XtiP/2/cw2iz9CPPZ\nPL/jSJxQMREREX84DjM0fFjqUDERERHf/Lrj7nzY9RRa/vo9h70/1e84EgdUTERExFczT+hHSXYe\nR73xFDmFa/yOIz5TMREREV8V523DzN79yC4p5KjXn/I7jvhMxURERHy3sPtprGq1C4fOfZVWP33r\ndxzxUTCWH26MORy4y1rbwxjTBngacIHPgKustVXGmEuAy4AKYLS1dpoxJht4DmgFrAf6WmtXxTKr\niIj4pzKYzow+V3H+47fSa8rDTLrqPnAcv2OJD2J2xsQYczPwJJAVXjQWGGyt7QY4wCnGmO2Ba4Eu\nwHHAHcaYTOAKYEl422eAwbHKKSIi8cHu35ll+xzKXl9+yN6fL/A7jvgklmdMvgb6AM+G37cHZodf\nTweOBSqBAmttKVBqjFkGHAh0Be6ute2QSL5hs2Y5BINp0UkPZGTG9ITSZomHLC1b5vsdQeqIh+Oi\nWjxk0TEafzb3uHj77AHsMfwCek99hBXtOlEVjN5xpWM0McTs/5K19mVjTOtaixxrbfXUfuuBpkAT\nYG2tbepbXr2sQatXF29N5L8oK62I6udtqYzMYFxkWbVqvd8RpI54OC5Ax6hs3OYeFz+22I0Pu57M\n4XNe4aB3JrPgyDOikiNZj9FkLDqNefNrVa3X+cAaYF349aaWVy8TEZEU8O4JF4WHD08ku3BtwztI\nUmnMYvKpMaZH+HUvYA6wEOhmjMkyxjQF9sW7MbYA6F1nWxERSQHF+c14r1dfcorXc9QbGj6cahqz\nmNwIjDDGzAcygMnW2pXAQ3jFYyYwyFobAh4D2hpj5gKXAiMaMaeIiPjsgyNO57eWO3PYnFdouXK5\n33GkEcX0TiBr7XKgY/j1/4Aj6tlmPDC+zrJi4MxYZhMRkfhVPXz4vH/exvEvP8qzV93jdyRpJJpg\nTURE4tLSA7rwtWmP+WIBe2n4cMpQMRERkfjkOLxx+tVUOQF6TXmEQKX/o2ok9lRMREQkbv2yUxs+\n6nISrVZ+x6FzXvU7jjQCFRMREYlr757Yn1BWLj3fmEhWseaqSXYqJiIiEteKqocPF63jSA0fTnoq\nJiIiEvcWHHE6v7fYiY6zp9DilxV+x5EYUjEREZG4V5mewYw+V5JWVcnxUx71O47EkIqJiIgkhC8P\n7MY3ex/CPp/No82XC/2OIzGiYiIiIonBcXjj9Guochx6vazhw8lKxURERBLGyp3b8HHnE9nu52/p\nUPAfv+NIDKiYiIhIQnnnxIsJZeXQc9oEDR9OQiomIiKSUIqaNGfW8ReQW7SWHtMn+R1HokzFRERE\nEs78HmfyR4sd6TRrMttq+HBSUTEREZGEU5mewYzTwsOHpz7mdxyJIhUTERFJSF+06863ex3Evkvm\nsufSj/yOI1GiYiIiIolpg+HDD2v4cJJQMRERkYT18y5780mn3mz/0ze0n/e633EkClRMREQkob1z\n4iWUZmZz9LTxZJYU+h1HtpKKiYiIJLTCptsy+/gLyC3U8OFkoGIiIiIJb96RZ7J62x3oNGsyzX/9\nwe84shVUTEREJOFVpGcy49QrCFZWcPwr4/yOI1tBxURERJLC5wf3YPme7dhv8Rz2sB/7HUe2kIqJ\niIgkB8fhjTP+HD7sVFX6nUi2gIqJiIgkjZ92NXx6eC92+PFr2s/X8OFEpGIiIiJJ5e2TL6E0I5uj\nX9Pw4USkYiIiIkmlsGkL3j/uPPIK13DEm8/6HUc2U9DvACIiItFWcNRZdCj4D53fe4mPupxM4c67\n+R2p0RljAsA4oB1QClxsrV1WZ5sc4G2gv7V26cb2McYcDEwDvgrv+pi19t+xyK0zJiIiknQqMjJ5\n89QrCFaUc9wrKfv04VOBLGttJ+BW4L7aK40xHYD3gT0j2Kc9MNZa2yP8FZNSAiomIiKSpD475Ei+\n2+MA2i6azW5LP/E7jh+6AjMArLULgA511mcCpwFLI9inPXCCMeZ9Y8wEY0x+rEKrmIiISHJyHF4/\n41oAur3+lM9hfNEEWFvrfaUxpuYWDmttgbX2+wj3WQjcZK3tDnwDDItRZt1jIiIiyeun3fbhtbNu\ngOwsv6P4YR1Q+8xGwFpbsSX7GGOmWmvXhJdNBR6OYs4N6IyJiIgktYXdT2NR15P8juGHAqA3gDGm\nI7BkK/Z50xhzWPh1TyBmU+vqjImIiEhymgocY4yZBzhAP2PMOUCetfaJSPcJL78CeNgYUw6sBC6N\nVWgVExERkSRkra0CLq+zeGk92/VoYB+stZ8AXaIcsV66lCMiIiJxQ8VERERE4oaKiYiIiMQNFRMR\nERGJGyomIiIiEjdUTERERCRuqJiIiIhI3FAxERERkbgRtxOsGWMCwDigHVAKXGytXeZvKhEREYml\neD5jciqQZa3tBNwK3OdzHhEREYmxeC4mXYEZANbaBUAHf+OIiIhIrDmu6/qdoV7GmCeBl62108Pv\nVwB7RPDIZhEREUlQ8XzGZB2QX+t9QKVEREQkucVzMSkAegMYYzoCS/yNIyIiIrEWt6NygKnAMcaY\neYAD9PM5j4iIiMRY3N5jIiIiIqknni/liIiISIpRMREREZG4oWIiDTLGZBljcv3OIVKXMaZl+L/6\nWSaSJPSHWTbJGNMCeBA4whgTzzdLS4oxxhwALDLGNLXWVvmdR0SiQ8VENsoY41hrfwOWAT2BvX2O\nJIIxxgkfm0uAacCjfmcS2RLGmDS/M8QjjcqRvzDGOADWWrfW+weA5cDz1tpf/EsnqcwYE6h7dsQY\n8wEw0Vr7z/rWi8QzY0wrYDfgF2vtCh3DKiayCcaYw4FewEJgDXAx3vwyb1trQ35mk9RmjLkGaAF8\nCiwG3gWOstYuD59N0Q82iXvGmBOBEcCLQH/gJGut9TeV/3QpRzZQfbbEGHMB8AjeWZLrgX0BCxwL\nHORXPkk9tY7JQPhG7CeBTsA7wD3AnsC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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "IV: 0.00586098683881\n" ] }, { "data": { "text/html": [ "
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% responders% non-respondersWOEDG-DBIV
Some High School0.3825580.3634920.0511250.0190670.000975
Undergraduate Degree0.1755810.205241-0.156080-0.0296590.004629
Professional School0.4418600.4312680.0242650.0105930.000257
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" ], "text/plain": [ " % responders % non-responders WOE DG-DB \\\n", "Some High School 0.382558 0.363492 0.051125 0.019067 \n", "Undergraduate Degree 0.175581 0.205241 -0.156080 -0.029659 \n", "Professional School 0.441860 0.431268 0.024265 0.010593 \n", "\n", " IV \n", "Some High School 0.000975 \n", "Undergraduate Degree 0.004629 \n", "Professional School 0.000257 " ] }, "execution_count": 71, "metadata": {}, "output_type": "execute_result" } ], "source": [ "functions.feature_stat(data, 'EDUCATION', 'TARGET')\n", "functions.calc_iv(data, 'TARGET', 'EDUCATION')[0]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## MARITAL_STATUS" ] }, { "cell_type": "code", "execution_count": 72, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "Married 0.617750\n", "Single 0.238652\n", "Separated 0.081816\n", "Widowed 0.038806\n", "Partner 0.022976\n", "Name: MARITAL_STATUS, dtype: float64" ] }, "execution_count": 72, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data['MARITAL_STATUS'].value_counts(dropna=False, normalize=True)" ] }, { "cell_type": "code", "execution_count": 73, "metadata": { "collapsed": true }, "outputs": [], "source": [ "data.loc[data['MARITAL_STATUS'].isin(['Married', 'Partner']), 'MARITAL_STATUS'] = 'Married'\n", "data.loc[data['MARITAL_STATUS'].isin(['Single', 'Separated', 'Widowed']), 'MARITAL_STATUS'] = 'Single'\n", "data['MARITAL_STATUS'] = data['MARITAL_STATUS'].cat.remove_unused_categories()" ] }, { "cell_type": "code", "execution_count": 74, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Counts:\n", "MARITAL_STATUS\n", "Married 9147\n", "Single 5129\n", "Name: TARGET, dtype: int64\n", "Frequencies:\n", "Married 0.640726\n", "Single 0.359274\n", "Name: MARITAL_STATUS, dtype: float64\n" ] }, { "data": { "image/png": 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ZfPOtKiUi8aMbkGmtPR64Fri3/ovGmLbAq8CB9YYvADZaa9sTugJ3Snh8MnCZ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\nfO+9i79fT6iqpPjhOVSd1dH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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "IV: 0.00380333908871\n" ] }, { "data": { "text/html": [ "
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% responders% non-respondersWOEDG-DBIV
Married0.6145350.644313-0.047320-0.0297790.001409
Single0.3854650.3556870.0804010.0297790.002394
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" ], "text/plain": [ " % responders % non-responders WOE DG-DB IV\n", "Married 0.614535 0.644313 -0.047320 -0.029779 0.001409\n", "Single 0.385465 0.355687 0.080401 0.029779 0.002394" ] }, "execution_count": 74, "metadata": {}, "output_type": "execute_result" } ], "source": [ "functions.feature_stat(data, 'MARITAL_STATUS', 'TARGET')\n", "functions.calc_iv(data, 'TARGET', 'MARITAL_STATUS')[0]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## GEN_INDUSTRY" ] }, { "cell_type": "code", "execution_count": 75, "metadata": { "scrolled": false }, "outputs": [ { "data": { "text/plain": [ "Market, real estate 0.157957\n", "Others fields 0.113477\n", "Iron & Steel 0.089451\n", "Unknown 0.086579\n", "Public & municipal administ. 0.084477\n", "Healthcare 0.077683\n", "Schools 0.064164\n", "Transportation 0.051695\n", "Agriculture 0.046372\n", "Construction - Raw Materials 0.037896\n", "Municipal economy/Road service 0.035724\n", "Restaurant & Catering 0.027249\n", "Scientific & Technical Instr. 0.026898\n", "Oil & Gas Operations 0.014780\n", "Assembly production 0.011418\n", "Regional Banks 0.010857\n", "Recreational Activities 0.009526\n", "Detective 0.009316\n", "Oil Well Services & Equipment 0.009316\n", "Information service 0.006795\n", "Beauty shop 0.006514\n", "Software & Programming 0.005534\n", "Chemistry/Perfumery/Pharmaceut 0.004273\n", "Mass media 0.003362\n", "Personal Services 0.002732\n", "Insurance (Accident & Health) 0.001821\n", "Hotels & Motels 0.001121\n", "Real Estate Operations 0.000771\n", "Business Services 0.000771\n", "Trucking 0.000700\n", "Staff recruitment 0.000560\n", "Marketing 0.000210\n", "Name: GEN_INDUSTRY, dtype: float64" ] }, "execution_count": 75, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data['GEN_INDUSTRY'].value_counts(dropna=False, normalize=True)" ] }, { "cell_type": "code", "execution_count": 76, "metadata": { "collapsed": true }, "outputs": [], "source": [ "data['GEN_INDUSTRY'].cat.add_categories(['others'], inplace=True)\n", "data.loc[data['GEN_INDUSTRY'].isin(['Market, real estate', 'Others fields', 'Iron & Steel', 'Unknown', 'Transportation',\n", " 'Public & municipal administ.', 'Healthcare', 'Schools']) == False,\n", " 'GEN_INDUSTRY'] = 'others'\n", "data['GEN_INDUSTRY'] = data['GEN_INDUSTRY'].cat.remove_unused_categories()" ] }, { "cell_type": "code", "execution_count": 77, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Counts:\n", "GEN_INDUSTRY\n", "Healthcare 1109\n", "Iron & Steel 1277\n", "Market, real estate 2255\n", "Others fields 1620\n", "Public & municipal administ. 1206\n", "Schools 916\n", "Transportation 738\n", "Unknown 1236\n", "others 3919\n", "Name: TARGET, dtype: int64\n", "Frequencies:\n", "others 0.274517\n", "Market, real estate 0.157957\n", "Others fields 0.113477\n", "Iron & Steel 0.089451\n", "Unknown 0.086579\n", "Public & municipal administ. 0.084477\n", "Healthcare 0.077683\n", "Schools 0.064164\n", "Transportation 0.051695\n", "Name: GEN_INDUSTRY, dtype: float64\n" ] }, { "data": { "image/png": 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RMG0Kls9H+OIJTqcRSVbPnlQ9ORuz/GA63fUnCv9yp9OR2jXvSk1g0QKio8aQ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\nvsLtig/aehbvgXARPtV3FC40/R6lTHOkVFCun8grSRC5RErBTMKbvB2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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "IV: 0.113378430613\n" ] }, { "data": { "text/html": [ "
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% responders% non-respondersWOEDG-DBIV
Market, real estate0.2110470.1506850.3368870.0603620.020335
others0.2779070.2740520.0139680.0038550.000054
Schools0.0587210.064909-0.100193-0.0061880.000620
Public & municipal administ.0.0796510.085139-0.066624-0.0054870.000366
Others fields0.1261630.1117390.1214040.0144230.001751
Iron & Steel0.0819770.090475-0.098634-0.0084980.000838
Unknown0.0279070.094616-1.220951-0.0667090.081449
Transportation0.0686050.0493790.3288390.0192260.006322
Healthcare0.0680230.079006-0.149675-0.0109830.001644
\n", "
" ], "text/plain": [ " % responders % non-responders WOE \\\n", "Market, real estate 0.211047 0.150685 0.336887 \n", "others 0.277907 0.274052 0.013968 \n", "Schools 0.058721 0.064909 -0.100193 \n", "Public & municipal administ. 0.079651 0.085139 -0.066624 \n", "Others fields 0.126163 0.111739 0.121404 \n", "Iron & Steel 0.081977 0.090475 -0.098634 \n", "Unknown 0.027907 0.094616 -1.220951 \n", "Transportation 0.068605 0.049379 0.328839 \n", "Healthcare 0.068023 0.079006 -0.149675 \n", "\n", " DG-DB IV \n", "Market, real estate 0.060362 0.020335 \n", "others 0.003855 0.000054 \n", "Schools -0.006188 0.000620 \n", "Public & municipal administ. -0.005487 0.000366 \n", "Others fields 0.014423 0.001751 \n", "Iron & Steel -0.008498 0.000838 \n", "Unknown -0.066709 0.081449 \n", "Transportation 0.019226 0.006322 \n", "Healthcare -0.010983 0.001644 " ] }, "execution_count": 77, "metadata": {}, "output_type": "execute_result" } ], "source": [ "functions.feature_stat(data, 'GEN_INDUSTRY', 'TARGET')\n", "functions.calc_iv(data, 'TARGET', 'GEN_INDUSTRY')[0]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## FAMILY_INCOME" ] }, { "cell_type": "code", "execution_count": 78, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "10000-20000 0.471070\n", "20000-50000 0.405015\n", "5000-10000 0.096526\n", "50000+ 0.025077\n", "up to 5000 0.002312\n", "Name: FAMILY_INCOME, dtype: float64" ] }, "execution_count": 78, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data['FAMILY_INCOME'].value_counts(dropna=False, normalize=True)" ] }, { "cell_type": "code", "execution_count": 79, "metadata": { "collapsed": true }, "outputs": [], "source": [ "data['FAMILY_INCOME'].cat.add_categories(['up to 10000', '20000+'], inplace=True)\n", "data.loc[data['FAMILY_INCOME'].isin(['up to 5000', '5000-10000']), 'FAMILY_INCOME'] = 'up to 10000'\n", "data.loc[data['FAMILY_INCOME'].isin(['20000-50000', '50000+']), 'FAMILY_INCOME'] = '20000+'\n", "data['FAMILY_INCOME'] = data['FAMILY_INCOME'].cat.remove_unused_categories()" ] }, { "cell_type": "code", "execution_count": 80, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Counts:\n", "FAMILY_INCOME\n", "10000-20000 6725\n", "up to 10000 1411\n", "20000+ 6140\n", "Name: TARGET, dtype: int64\n", "Frequencies:\n", "10000-20000 0.471070\n", "20000+ 0.430092\n", "up to 10000 0.098837\n", "Name: FAMILY_INCOME, dtype: float64\n" ] }, { "data": { "image/png": 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Npk6C5GTip00hvpK/73D47ioVDln0d/I/ofxt5ADTgIeAwwgWEY611i3ZngmkAA2AjDLz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\naEXweszB1tqvQ5FPK/2I1HG5SSk8esM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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "IV: 0.0274921611768\n" ] }, { "data": { "text/html": [ "
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% responders% non-respondersWOEDG-DBIV
10000-200000.4354650.475948-0.088893-0.0404830.003599
20000+0.4941860.4213130.1595370.0728740.011626
up to 100000.0703490.102740-0.378733-0.0323910.012267
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" ], "text/plain": [ " % responders % non-responders WOE DG-DB IV\n", "10000-20000 0.435465 0.475948 -0.088893 -0.040483 0.003599\n", "20000+ 0.494186 0.421313 0.159537 0.072874 0.011626\n", "up to 10000 0.070349 0.102740 -0.378733 -0.032391 0.012267" ] }, "execution_count": 80, "metadata": {}, "output_type": "execute_result" } ], "source": [ "functions.feature_stat(data, 'FAMILY_INCOME', 'TARGET')\n", "functions.calc_iv(data, 'TARGET', 'FAMILY_INCOME')[0]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## LOAN_NUM_TOTAL" ] }, { "cell_type": "code", "execution_count": 81, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "1 0.738232\n", "2 0.174489\n", "3 0.058350\n", "4 0.018282\n", "5 0.007005\n", "6 0.002452\n", "7 0.000981\n", "8 0.000140\n", "11 0.000070\n", "Name: LOAN_NUM_TOTAL, dtype: float64" ] }, "execution_count": 81, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data['LOAN_NUM_TOTAL'].value_counts(dropna=False, normalize=True)" ] }, { "cell_type": "code", "execution_count": 82, "metadata": { "collapsed": true }, "outputs": [], "source": [ "data['LOAN_NUM_TOTAL'].cat.add_categories(['3 or more'], inplace=True)\n", "data.loc[data['LOAN_NUM_TOTAL'].isin([1, 2]) == False, 'LOAN_NUM_TOTAL'] = '3 or more'\n", "data['LOAN_NUM_TOTAL'] = data['LOAN_NUM_TOTAL'].cat.remove_unused_categories()" ] }, { "cell_type": "code", "execution_count": 83, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Counts:\n", "LOAN_NUM_TOTAL\n", "1 10539\n", "2 2491\n", "3 or more 1246\n", "Name: TARGET, dtype: int64\n", "Frequencies:\n", "1 0.738232\n", "2 0.174489\n", "3 or more 0.087279\n", "Name: LOAN_NUM_TOTAL, dtype: float64\n" ] }, { "data": { "image/png": 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ZdQUUFvpckIiIb8pwnCZUrpzvOB2AilAPri2wpO5sgbjAtrQ6FCmSsMoOPYzC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\nbMj3uwTZCT/Pjc8PPZ5OX3x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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "IV: 0.0121634889441\n" ] }, { "data": { "text/html": [ "
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% responders% non-respondersWOEDG-DBIV
10.7796510.7325580.0623040.0470930.002934
20.1488370.178003-0.178945-0.0291650.005219
3 or more0.0715120.089439-0.223700-0.0179280.004010
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" ], "text/plain": [ " % responders % non-responders WOE DG-DB IV\n", "1 0.779651 0.732558 0.062304 0.047093 0.002934\n", "2 0.148837 0.178003 -0.178945 -0.029165 0.005219\n", "3 or more 0.071512 0.089439 -0.223700 -0.017928 0.004010" ] }, "execution_count": 83, "metadata": {}, "output_type": "execute_result" } ], "source": [ "functions.feature_stat(data, 'LOAN_NUM_TOTAL', 'TARGET')\n", "functions.calc_iv(data, 'TARGET', 'LOAN_NUM_TOTAL')[0]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## LOAN_NUM_TOTAL" ] }, { "cell_type": "code", "execution_count": 84, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "0 0.522275\n", "1 0.302045\n", "2 0.115999\n", "3 0.039857\n", "4 0.013379\n", "5 0.004133\n", "6 0.001821\n", "7 0.000280\n", "8 0.000140\n", "11 0.000070\n", "Name: LOAN_NUM_CLOSED, dtype: float64" ] }, "execution_count": 84, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data['LOAN_NUM_CLOSED'].value_counts(dropna=False, normalize=True)" ] }, { "cell_type": "code", "execution_count": 85, "metadata": { "collapsed": true }, "outputs": [], "source": [ "data['LOAN_NUM_CLOSED'].cat.add_categories(['3 or more'], inplace=True)\n", "data.loc[data['LOAN_NUM_CLOSED'].isin([0, 1, 2]) == False, 'LOAN_NUM_CLOSED'] = '3 or more'\n", "data['LOAN_NUM_CLOSED'] = data['LOAN_NUM_CLOSED'].cat.remove_unused_categories()" ] }, { "cell_type": "code", "execution_count": 86, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Counts:\n", "LOAN_NUM_CLOSED\n", "0 7456\n", "1 4312\n", "2 1656\n", "3 or more 852\n", "Name: TARGET, dtype: int64\n", "Frequencies:\n", "0 0.522275\n", "1 0.302045\n", "2 0.115999\n", "3 or more 0.059681\n", "Name: LOAN_NUM_CLOSED, dtype: float64\n" ] }, { "data": { "image/png": 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0c9a078X+v2zi+AVv+R1HRCRSvQBMJ3hG5Wnga2BaKANDnQn1MeBoYALBO2Eu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\nNiJ55CM4WVv8jigiYUAFREQ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3 or more0.0412790.062201-0.410021-0.0209220.008579
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" ], "text/plain": [ " % responders % non-responders WOE DG-DB IV\n", "1 0.260465 0.307741 -0.166791 -0.047276 0.007885\n", "2 0.089535 0.119624 -0.289726 -0.030089 0.008718\n", "0 0.608721 0.510433 0.176100 0.098288 0.017308\n", "3 or more 0.041279 0.062201 -0.410021 -0.020922 0.008579" ] }, "execution_count": 86, "metadata": {}, "output_type": "execute_result" } ], "source": [ "functions.feature_stat(data, 'LOAN_NUM_CLOSED', 'TARGET')\n", "functions.calc_iv(data, 'TARGET', 'LOAN_NUM_CLOSED')[0]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## LOAN_DLQ_NUM" ] }, { "cell_type": "code", "execution_count": 87, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "0 0.871603\n", "1 0.094284\n", "2 0.018633\n", "3 0.006514\n", "4 0.003362\n", "5 0.002662\n", "6 0.001121\n", "7 0.000911\n", "9 0.000280\n", "8 0.000210\n", "13 0.000140\n", "10 0.000140\n", "12 0.000070\n", "11 0.000070\n", "Name: LOAN_DLQ_NUM, dtype: float64" ] }, "execution_count": 87, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data['LOAN_DLQ_NUM'].value_counts(dropna=False, normalize=True)" ] }, { "cell_type": "code", "execution_count": 88, "metadata": { "collapsed": true }, "outputs": [], "source": [ "data['LOAN_DLQ_NUM'].cat.add_categories(['1 or more'], inplace=True)\n", "data.loc[data['LOAN_DLQ_NUM'].isin([0]) == False, 'LOAN_DLQ_NUM'] = '1 or more'\n", "data['LOAN_DLQ_NUM'] = data['LOAN_DLQ_NUM'].cat.remove_unused_categories()" ] }, { "cell_type": "code", "execution_count": 89, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Counts:\n", "LOAN_DLQ_NUM\n", "0 12443\n", "1 or more 1833\n", "Name: TARGET, dtype: int64\n", "Frequencies:\n", "0 0.871603\n", "1 or more 0.128397\n", "Name: LOAN_DLQ_NUM, dtype: float64\n" ] }, { "data": { "image/png": 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YY9KAyUAnoBo4y1q7ttE2ucDzwAhr7RpjzDBgWOjpbOBAoKO1tsm1ns2lK7+KiMSJzKVL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\nInEl/bNP8fftSdrPxZTdfjd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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "IV: 0.0512098860054\n" ] }, { "data": { "text/html": [ "
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% responders% non-respondersWOEDG-DBIV
1 or more0.2005810.1185090.5262310.0820720.043189
00.7994190.881491-0.097730-0.0820720.008021
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" ], "text/plain": [ " % responders % non-responders WOE DG-DB IV\n", "1 or more 0.200581 0.118509 0.526231 0.082072 0.043189\n", "0 0.799419 0.881491 -0.097730 -0.082072 0.008021" ] }, "execution_count": 89, "metadata": {}, "output_type": "execute_result" } ], "source": [ "functions.feature_stat(data, 'LOAN_DLQ_NUM', 'TARGET')\n", "functions.calc_iv(data, 'TARGET', 'LOAN_DLQ_NUM')[0]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## LOAN_MAX_DLQ" ] }, { "cell_type": "code", "execution_count": 90, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0 0.871603\n", "1 0.125525\n", "2 0.002171\n", "3 0.000490\n", "8 0.000070\n", "6 0.000070\n", "4 0.000070\n", "Name: LOAN_MAX_DLQ, dtype: float64" ] }, "execution_count": 90, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data['LOAN_MAX_DLQ'].value_counts(dropna=False, normalize=True)" ] }, { "cell_type": "code", "execution_count": 91, "metadata": { "collapsed": true }, "outputs": [], "source": [ "data['LOAN_MAX_DLQ'].cat.add_categories(['1 or more'], inplace=True)\n", "data.loc[data['LOAN_MAX_DLQ'].isin([0]) == False, 'LOAN_MAX_DLQ'] = '1 or more'\n", "data['LOAN_MAX_DLQ'] = data['LOAN_MAX_DLQ'].cat.remove_unused_categories()" ] }, { "cell_type": "code", "execution_count": 92, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Counts:\n", "LOAN_MAX_DLQ\n", "0 12443\n", "1 or more 1833\n", "Name: TARGET, dtype: int64\n", "Frequencies:\n", "0 0.871603\n", "1 or more 0.128397\n", "Name: LOAN_MAX_DLQ, dtype: float64\n" ] }, { "data": { "image/png": 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YY9KAyUAnoBo4y1q7ttE2ucDzwAhr7RpjzDBgWOjpbOBAoKO1tsm1ns2lK7+KiMSJzKVL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\nInEl/bNP8fftSdrPxZTdfjd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% responders% non-respondersWOEDG-DBIV
1 or more0.2005810.1185090.5262310.0820720.043189
00.7994190.881491-0.097730-0.0820720.008021
\n", "
" ], "text/plain": [ " % responders % non-responders WOE DG-DB IV\n", "1 or more 0.200581 0.118509 0.526231 0.082072 0.043189\n", "0 0.799419 0.881491 -0.097730 -0.082072 0.008021" ] }, "execution_count": 92, "metadata": {}, "output_type": "execute_result" } ], "source": [ "functions.feature_stat(data, 'LOAN_MAX_DLQ', 'TARGET')\n", "functions.calc_iv(data, 'TARGET', 'LOAN_MAX_DLQ')[0]" ] }, { "cell_type": "code", "execution_count": 93, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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AGREEMENT_RKTARGETAGESOCSTATUS_WORK_FLSOCSTATUS_PENS_FLGENDERCHILD_TOTALDEPENDANTSEDUCATIONMARITAL_STATUSGEN_INDUSTRYFAMILY_INCOMEPERSONAL_INCOMEREG_FACT_FLREG_POST_FLREG_FACT_POST_FLREG_FACT_POST_TP_FLFL_PRESENCE_FLOWN_AUTOAUTO_RUS_FLHS_PRESENCE_FLCREDITTERMFST_PAYMENTGPF_DOCUMENT_FLFACT_LIVING_TERMWORK_TIMEFACT_PHONE_FLREG_PHONE_FLGEN_PHONE_FLLOAN_NUM_TOTALLOAN_NUM_CLOSEDLOAN_NUM_PAYMLOAN_DLQ_NUMLOAN_MAX_DLQLOAN_AVG_DLQ_AMTLOAN_MAX_DLQ_AMTIncome_to_limit
1599102300(30.0, 34.0]1013 or more2 or moreSome High SchoolMarriedMarket, real estate10000-20000(11000.0, 14800.0]11100000(18100.0, 22500.0](4.5, 8.5](3800.0, 6000.0]1(131.5, 171.5](85.5, 151.0]10111(5.5, 6.5]1 or more1 or more(500.0, 15000.0](500.0, 15000.0](0.515, 0.783]
2599105250(50.0, 54.0]1013 or more2 or moreSome High SchoolMarriedothers10000-20000(7600.0, 9300.0]11110001(22500.0, 119700.0](11.5, 36.0](3800.0, 6000.0]1(238.5, 1000.0](53.5, 85.5]00121(6.5, 11.5]00(0.0, 500.0](0.0, 500.0](0.0, 0.515]
3599108030(38.0, 42.0]10111Undergraduate DegreeMarriedSchools20000+(20800.0, 44000.0]00011000(7200.0, 9400.0](4.5, 8.5](3800.0, 6000.0]0(18.5, 38.5](151.0, 600.0]11111(5.5, 6.5]1 or more1 or more(500.0, 15000.0](500.0, 15000.0](2.556, 16.706]
4599117810(26.0, 30.0]10002 or moreSome High SchoolMarriedPublic & municipal administ.10000-20000(11000.0, 14800.0]11100001(18100.0, 22500.0](11.5, 36.0](3800.0, 6000.0]1(38.5, 85.5](85.5, 151.0]10121(11.5, 110.0]1 or more1 or more(500.0, 15000.0](500.0, 15000.0](0.515, 0.783]
5599117840(26.0, 30.0]10002 or moreSome High SchoolMarriedMarket, real estate20000+(11000.0, 14800.0]11110100(22500.0, 119700.0](11.5, 36.0](2100.0, 3800.0]0(85.5, 131.5](35.5, 53.5]10121(6.5, 11.5]00(0.0, 500.0](0.0, 500.0](0.0, 0.515]
7599120340(38.0, 42.0]10102 or moreSome High SchoolMarriedothers10000-20000(0.0, 7600.0]11111000(14100.0, 18100.0](8.5, 11.5](200.0, 900.0]1(171.5, 238.5](0.0, 6.5]11121(6.5, 11.5]00(0.0, 500.0](0.0, 500.0](0.0, 0.515]
9599126590(42.0, 50.0]10102 or moreProfessional SchoolMarriedSchools10000-20000(0.0, 7600.0]11111000(0.0, 5400.0](4.5, 8.5](200.0, 900.0]0(238.5, 1000.0](53.5, 85.5]00111(5.5, 6.5]00(0.0, 500.0](0.0, 500.0](1.428, 1.962]
10599126920(50.0, 54.0]10122 or moreProfessional SchoolSingleSchools20000+(15300.0, 20800.0]11111000(0.0, 5400.0](0.0, 4.5](200.0, 900.0]0(238.5, 1000.0](85.5, 151.0]10111(0.0, 3.5]00(0.0, 500.0](0.0, 500.0](2.556, 16.706]
11599131081(0.0, 26.0]10002 or moreProfessional SchoolSingleOthers fields10000-20000(15300.0, 20800.0]11110000(9400.0, 14100.0](0.0, 4.5](6000.0, 75600.0]1(38.5, 85.5](21.5, 35.5]00121(11.5, 110.0]00(0.0, 500.0](0.0, 500.0](1.428, 1.962]
12599131341(54.0, 67.0]0103 or more2 or moreSome High SchoolMarriedIron & Steel10000-20000(0.0, 7600.0]11110110(9400.0, 14100.0](0.0, 4.5](2100.0, 3800.0]1(238.5, 1000.0](35.5, 53.5]00111(0.0, 3.5]00(0.0, 500.0](0.0, 500.0](0.515, 0.783]
\n", "
" ], "text/plain": [ " AGREEMENT_RK TARGET AGE SOCSTATUS_WORK_FL SOCSTATUS_PENS_FL \\\n", "1 59910230 0 (30.0, 34.0] 1 0 \n", "2 59910525 0 (50.0, 54.0] 1 0 \n", "3 59910803 0 (38.0, 42.0] 1 0 \n", "4 59911781 0 (26.0, 30.0] 1 0 \n", "5 59911784 0 (26.0, 30.0] 1 0 \n", "7 59912034 0 (38.0, 42.0] 1 0 \n", "9 59912659 0 (42.0, 50.0] 1 0 \n", "10 59912692 0 (50.0, 54.0] 1 0 \n", "11 59913108 1 (0.0, 26.0] 1 0 \n", "12 59913134 1 (54.0, 67.0] 0 1 \n", "\n", " GENDER CHILD_TOTAL DEPENDANTS EDUCATION MARITAL_STATUS \\\n", "1 1 3 or more 2 or more Some High School Married \n", "2 1 3 or more 2 or more Some High School Married \n", "3 1 1 1 Undergraduate Degree Married \n", "4 0 0 2 or more Some High School Married \n", "5 0 0 2 or more Some High School Married \n", "7 1 0 2 or more Some High School Married \n", "9 1 0 2 or more Professional School Married \n", "10 1 2 2 or more Professional School Single \n", "11 0 0 2 or more Professional School Single \n", "12 0 3 or more 2 or more Some High School Married \n", "\n", " GEN_INDUSTRY FAMILY_INCOME PERSONAL_INCOME \\\n", "1 Market, real estate 10000-20000 (11000.0, 14800.0] \n", "2 others 10000-20000 (7600.0, 9300.0] \n", "3 Schools 20000+ (20800.0, 44000.0] \n", "4 Public & municipal administ. 10000-20000 (11000.0, 14800.0] \n", "5 Market, real estate 20000+ (11000.0, 14800.0] \n", "7 others 10000-20000 (0.0, 7600.0] \n", "9 Schools 10000-20000 (0.0, 7600.0] \n", "10 Schools 20000+ (15300.0, 20800.0] \n", "11 Others fields 10000-20000 (15300.0, 20800.0] \n", "12 Iron & Steel 10000-20000 (0.0, 7600.0] \n", "\n", " REG_FACT_FL REG_POST_FL REG_FACT_POST_FL REG_FACT_POST_TP_FL \\\n", "1 1 1 1 0 \n", "2 1 1 1 1 \n", "3 0 0 0 1 \n", "4 1 1 1 0 \n", "5 1 1 1 1 \n", "7 1 1 1 1 \n", "9 1 1 1 1 \n", "10 1 1 1 1 \n", "11 1 1 1 1 \n", "12 1 1 1 1 \n", "\n", " FL_PRESENCE_FL OWN_AUTO AUTO_RUS_FL HS_PRESENCE_FL CREDIT \\\n", "1 0 0 0 0 (18100.0, 22500.0] \n", "2 0 0 0 1 (22500.0, 119700.0] \n", "3 1 0 0 0 (7200.0, 9400.0] \n", "4 0 0 0 1 (18100.0, 22500.0] \n", "5 0 1 0 0 (22500.0, 119700.0] \n", "7 1 0 0 0 (14100.0, 18100.0] \n", "9 1 0 0 0 (0.0, 5400.0] \n", "10 1 0 0 0 (0.0, 5400.0] \n", "11 0 0 0 0 (9400.0, 14100.0] \n", "12 0 1 1 0 (9400.0, 14100.0] \n", "\n", " TERM FST_PAYMENT GPF_DOCUMENT_FL FACT_LIVING_TERM \\\n", "1 (4.5, 8.5] (3800.0, 6000.0] 1 (131.5, 171.5] \n", "2 (11.5, 36.0] (3800.0, 6000.0] 1 (238.5, 1000.0] \n", "3 (4.5, 8.5] (3800.0, 6000.0] 0 (18.5, 38.5] \n", "4 (11.5, 36.0] (3800.0, 6000.0] 1 (38.5, 85.5] \n", "5 (11.5, 36.0] (2100.0, 3800.0] 0 (85.5, 131.5] \n", "7 (8.5, 11.5] (200.0, 900.0] 1 (171.5, 238.5] \n", "9 (4.5, 8.5] (200.0, 900.0] 0 (238.5, 1000.0] \n", "10 (0.0, 4.5] (200.0, 900.0] 0 (238.5, 1000.0] \n", "11 (0.0, 4.5] (6000.0, 75600.0] 1 (38.5, 85.5] \n", "12 (0.0, 4.5] (2100.0, 3800.0] 1 (238.5, 1000.0] \n", "\n", " WORK_TIME FACT_PHONE_FL REG_PHONE_FL GEN_PHONE_FL LOAN_NUM_TOTAL \\\n", "1 (85.5, 151.0] 1 0 1 1 \n", "2 (53.5, 85.5] 0 0 1 2 \n", "3 (151.0, 600.0] 1 1 1 1 \n", "4 (85.5, 151.0] 1 0 1 2 \n", "5 (35.5, 53.5] 1 0 1 2 \n", "7 (0.0, 6.5] 1 1 1 2 \n", "9 (53.5, 85.5] 0 0 1 1 \n", "10 (85.5, 151.0] 1 0 1 1 \n", "11 (21.5, 35.5] 0 0 1 2 \n", "12 (35.5, 53.5] 0 0 1 1 \n", "\n", " LOAN_NUM_CLOSED LOAN_NUM_PAYM LOAN_DLQ_NUM LOAN_MAX_DLQ LOAN_AVG_DLQ_AMT \\\n", "1 1 (5.5, 6.5] 1 or more 1 or more (500.0, 15000.0] \n", "2 1 (6.5, 11.5] 0 0 (0.0, 500.0] \n", "3 1 (5.5, 6.5] 1 or more 1 or more (500.0, 15000.0] \n", "4 1 (11.5, 110.0] 1 or more 1 or more (500.0, 15000.0] \n", "5 1 (6.5, 11.5] 0 0 (0.0, 500.0] \n", "7 1 (6.5, 11.5] 0 0 (0.0, 500.0] \n", "9 1 (5.5, 6.5] 0 0 (0.0, 500.0] \n", "10 1 (0.0, 3.5] 0 0 (0.0, 500.0] \n", "11 1 (11.5, 110.0] 0 0 (0.0, 500.0] \n", "12 1 (0.0, 3.5] 0 0 (0.0, 500.0] \n", "\n", " LOAN_MAX_DLQ_AMT Income_to_limit \n", "1 (500.0, 15000.0] (0.515, 0.783] \n", "2 (0.0, 500.0] (0.0, 0.515] \n", "3 (500.0, 15000.0] (2.556, 16.706] \n", "4 (500.0, 15000.0] (0.515, 0.783] \n", "5 (0.0, 500.0] (0.0, 0.515] \n", "7 (0.0, 500.0] (0.0, 0.515] \n", "9 (0.0, 500.0] (1.428, 1.962] \n", "10 (0.0, 500.0] (2.556, 16.706] \n", "11 (0.0, 500.0] (1.428, 1.962] \n", "12 (0.0, 500.0] (0.515, 0.783] " ] }, "execution_count": 93, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data.head(10)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This is it, all the variables are transformed. I didn't do anything to several variables which are flags, but they are good as they are." ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "# Feature selection based on IV\n", "\n", "Now it is time to choose columns. It could be done before, while processing separate columns, but I prefer to do this for all columns at once. I calculate IV for all columns and use only those which have value higher that threshold (0.02 in this case)." ] }, { "cell_type": "code", "execution_count": 94, "metadata": { "collapsed": true }, "outputs": [], "source": [ "columns_to_try = [col for col in list(data.columns) if col not in ('AGREEMENT_RK', 'CARD_ID_SB8', 'CARD_NUM', 'TARGET')]" ] }, { "cell_type": "code", "execution_count": 95, "metadata": { "collapsed": true }, "outputs": [], "source": [ "ivs = []\n", "for col in columns_to_try:\n", " data[col] = data[col].astype('category')\n", " if data[col].isnull().any():\n", " print(col)\n", " if 'Unknown' not in data[col].cat.categories:\n", " data[col].cat.add_categories(['Unknown'], inplace=True)\n", " data[col].fillna('Unknown', inplace=True)\n", " data[col] = data[col].cat.remove_unused_categories()\n", " _, iv = functions.calc_iv(data, 'TARGET', col)\n", " ivs.append((col, np.round(iv, 4)))" ] }, { "cell_type": "code", "execution_count": 96, "metadata": { "collapsed": true }, "outputs": [], "source": [ "good_cols = [i[0] for i in sorted(ivs, key=lambda tup: tup[1], reverse=True) if i[1] > 0.02]\n", "for i in ['TARGET', 'AGREEMENT_RK']:\n", " good_cols.append(i)" ] }, { "cell_type": "code", "execution_count": 97, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['AGE',\n", " 'GEN_INDUSTRY',\n", " 'WORK_TIME',\n", " 'PERSONAL_INCOME',\n", " 'GEN_PHONE_FL',\n", " 'SOCSTATUS_PENS_FL',\n", " 'SOCSTATUS_WORK_FL',\n", " 'LOAN_AVG_DLQ_AMT',\n", " 'LOAN_DLQ_NUM',\n", " 'LOAN_MAX_DLQ',\n", " 'LOAN_MAX_DLQ_AMT',\n", " 'FACT_LIVING_TERM',\n", " 'LOAN_NUM_CLOSED',\n", " 'FST_PAYMENT',\n", " 'TERM',\n", " 'Income_to_limit',\n", " 'LOAN_NUM_PAYM',\n", " 'FAMILY_INCOME',\n", " 'REG_FACT_POST_TP_FL',\n", " 'TARGET',\n", " 'AGREEMENT_RK']" ] }, "execution_count": 97, "metadata": {}, "output_type": "execute_result" } ], "source": [ "good_cols" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Some additional visualization\n", "\n", "Plotting variables by themselves is useful, but visualizing their interactions can unveil interesting things. There are some examples below.\n", "\n", "Pointplots show mean target rate for pairs of variables. I show only several plots as there are too many possible combinations." ] }, { "cell_type": "code", "execution_count": 98, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 98, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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EpIs4eKyI+VmW/UcKA+LDByZz25zRpPVNDFNmIu1PzayIiIiISCdXUenln6v3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\nDptKMcZ8zTs3xphS3Eb5cism/wB33mx4I93h/fNbuE9/v3GVexYREbl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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "data_viz = data[good_cols]\n", "fig, ax = plt.subplots(1, 2, figsize = (16, 6))\n", "sns.pointplot(x='SOCSTATUS_WORK_FL', y=\"TARGET\", hue='SOCSTATUS_PENS_FL', data=data_viz, ax=ax[0])\n", "sns.pointplot(x='LOAN_MAX_DLQ', y=\"TARGET\", hue='SOCSTATUS_PENS_FL', data=data_viz, ax=ax[1])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "SOCSTATUS_PENS_FL 1 means that person is on pension, 0 otherwise.\n", "SOCSTATUS_WORK_FL 1 means that person works, 0 otherwise.\n", "\n", "Three features on the plots above show clear distinctions between mean target rates. It could be a good idea to create new variables showing these interactions." ] }, { "cell_type": "code", "execution_count": 99, "metadata": { "collapsed": true }, "outputs": [], "source": [ "data['work_pens'] = 0\n", "data.loc[data['SOCSTATUS_WORK_FL'] == 0, 'work_pens'] = 1\n", "data.loc[(data['SOCSTATUS_WORK_FL'] == 1) & (data['SOCSTATUS_PENS_FL'] == 1), 'work_pens'] = 2\n", "data.loc[(data['SOCSTATUS_WORK_FL'] == 1) & (data['SOCSTATUS_PENS_FL'] == 0), 'work_pens'] = 3" ] }, { "cell_type": "code", "execution_count": 100, "metadata": { "collapsed": true }, "outputs": [], "source": [ "data['pens_dlq'] = 0\n", "data.loc[(data['LOAN_MAX_DLQ'] == 0) & (data['SOCSTATUS_PENS_FL'] == 0), 'pens_dlq'] = 1\n", "data.loc[(data['LOAN_MAX_DLQ'] == '1 or more') & (data['SOCSTATUS_PENS_FL'] == 1), 'pens_dlq'] = 2\n", "data.loc[(data['LOAN_MAX_DLQ'] == 0) & (data['SOCSTATUS_PENS_FL'] == 0), 'pens_dlq'] = 3\n", "data.loc[(data['LOAN_MAX_DLQ'] == '1 or more') & (data['SOCSTATUS_PENS_FL'] == 1), 'pens_dlq'] = 4" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For the next graphs I'll need data, where continuous variables aren't binned. Also it is necessary to do label encoding for categorical variables, as sns.pairplot doesn't work well with them." ] }, { "cell_type": "code", "execution_count": 101, "metadata": { "collapsed": true }, "outputs": [], "source": [ "le = preprocessing.LabelEncoder()\n", "for col in ['GENDER', 'CHILD_TOTAL', 'DEPENDANTS', 'EDUCATION', 'MARITAL_STATUS', 'GEN_INDUSTRY', 'OWN_AUTO',\n", " 'FAMILY_INCOME', 'LOAN_NUM_TOTAL', 'LOAN_NUM_CLOSED', 'LOAN_DLQ_NUM', 'LOAN_MAX_DLQ']:\n", " initial_data[col] = initial_data[col].astype('category')\n", " if (initial_data[col].isnull() == True).any():\n", " initial_data[col].cat.add_categories(['Unknown'], inplace=True)\n", " initial_data[col].fillna('Unknown', inplace=True)\n", " initial_data[col] = le.fit_transform(initial_data[col]) " ] }, { "cell_type": "code", "execution_count": 102, "metadata": { "collapsed": true }, "outputs": [], "source": [ "data_viz1 = initial_data[good_cols].drop(['AGREEMENT_RK'], axis=1)" ] }, { "cell_type": "code", "execution_count": 103, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 103, "metadata": {}, "output_type": "execute_result" }, { "data": { "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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rzE/5B/d0dGn0Ev9+5rl530Onu5r5zG4r9Xn1cS7d8sV7iZxN9fDMpe+HfZ9/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\nBALB9kQulzK7mkglk8+gkN6uIzu+cJOOJidyiZwnO44QWYmue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x3ecL0QSNQ8\nrtGo0GhU9RRt27Adv7d6UKtihzfmo6Vl+2Xm3lB3N+iLwaDBUMevdKO29Xo1er26fm1vsMavtx7P\nPqJtb2ca0eD9AHgZ+NvVGN7LG10ciSRrntsoI3M4HGaoZXDbue80gkvRTpJxKyeHjXT3k7AV300j\n6AQ0hpyNrrtOQxujQU/NLMt31radWLhFb3P3ums3ytLsMrZx3ncFoOY1DqONs77LdDW57vm+tWSp\ndf5+7lW69nr41ob9euv6+zhWn1sb3d9htJV3uu+sD1ztWGQlylBb9Zq/d/v83cbFoW3nhx/dLPbB\npufKzRDD/W01dXi76q3Lpq9ah9dl02/7OWOzaIT5cbNwGmuvDxtNdzda69b7+3yQcdy0trey3w3W\n9qNgCDeiwfsa8Jzb7f4QkAC/8aA3GrT1V83IPGDtQy7ffm/wBILN4Hd/8j/f92f+88l/WwdJBI8i\nAy37uOC/gsNgq1pDdm1tW41cQyKdRC1Xrbs2ncugVWiqfn6vtY/zviukc5mKz5WucehtXPSN8kLv\n01zwjd7TfWvJslbuRDq57vP3e206l6k5LnutfcUYXmvfXe/v0Ns47b1YlrtaX+50eavVbrXP38t3\nVLp2wLyPd6eKMbx97WbOXZvn+IBtQx3ZjuzvtXDu2nxFDO/+XssWSiWoF4PW/nVzA9xeHzYaG611\n6972Fo7jlvZ7K9u27K3edmt/3dvezjRc0qr75V6yNI/Oj+ON+XAZ7QxY+7Z1zG4jvGHdSTJu1yQU\na7nfpFWaoz+8b1k+qcHbCDoBjSFno+uuxWLgh9c+whMew2myMh2dwxvz0dnkxKZr5ezcCO0mB206\nK/FUglg6gS8e4KB9kIVkhFuLM3Q2uWjTWdEp1Ywv3CrP3z3NHeUszdeC15mL+zlk31f+nMtop8Pk\nYDbqp6u5nY+9Z3nMNcyN8FQ5K79RqUen1BJKhlfbctKmsyKXypiKzjIX93PYsZ/5pQWmFr1luc/N\nXcZusGFU6rHqW7keniy3X8wSXZTbqmvh/Oq1BqUOg0pHYGmBmegsw479BJci2A2W8ri4jHb2Wnev\nZmnuJ5KMYjO0Mh2dq5ClOAadfDh9BpveikGpQ6/UlfviMtrpb+1hObPCVHQWb8xHd1M7LVozF/1X\n1mVpLj0Pl1JJJqPe1e+oKP+5uREchmJG6EwuUx6X4lgvcisyUyNL8xJH+20Nl6W5xOnxECPXg2uy\nNFs42tf6sMTbchphftxM7nd9uJ11dyvXuqLtLWg7fJHR0Njttlv7RZbmR93gLdEoE3kjyLmTZNzO\nD7AS92vwPgifNLNzI+gENIacja67a+U3GpXEYunyv2uPbeb5h9FWqV/bQZbNHLd77dfdzt9rHd7t\nqrdraYR5oh6Ift/1OqG7G7B2XnjYiH7f9bodb/A2YpZmgUAgEOwASouAtYuBav//pOcfRlv32v7D\nkGUzx22z+lKv8iMCgaAxeBRqVVfjUe33dkMYvAKBQCAQCAQCgUAg2JEIg1cgEAgEAoFAIBAIBDuS\nRszSLBAIHjL3Gyf8SWN+BQKBQCAQCASCzUAYvAKBYNMRpY8EAoFAIBAIBNuBHZ+lWSAQCAQCgUAg\nEAgEjyYihlcgEAgEAoFAIBAIBDsSYfAKBAKBQCAQCAQCgWBHIgxegUAgEAgEAoFAIBDsSITBKxAI\nBAKBQCAQCASCHYkweAUCgUAgEAgEAoFAsCMRBq9AIBAIBAKBQCAQCHYkwuAVCAQCgUAgEAgEAsGO\nRBi8AoFAIBAIBAKBQCDYkQiDVyAQCAQCgUAgEAgEOxJh8AoEAoFAIBAIBAKBYEciDF6BQCAQCAQC\ngUAgEOxIhMErEAgEAoFAIBAIBIIdiTB4BQKBQCAQCAQCgUCwIxEGr0AgEAgEAoFAIBAIdiTC4BUI\nBAKBQCAQCAQCwY5EGLwCgUAgEAgEAoFAINiRCINXIBAIBAKBQCAQCAQ7EvlWC7ARbrf714FfX/1T\nDQwBTwL/ASgAV4Df9Xg8+a2QTyAQCAQCgUAgEAgE2xdJoVDYahnuCbfb/Z+BS8BLwJ94PJ533G73\n/wO86fF4Xtta6QQCgUAgEAgEAoFAsN3Y1ju8Jdxu9zAw4PF4ftftdv8R8O7qqb8DngdqGrzBYPye\nLHqzWUskkvzEstabRpBzJ8losRgkD0Gcquwk3W0EGaEx5Gx03W2EMX4QRL82h+2qt2vZqd/13RD9\n3hihu9sX0e+N2UrdfVg0SgzvHwL/2+r/JR6Pp/TDjgOmzWhALpdtxm3qTiPIKWR8uDRCXxpBRmgM\nORtBxo1odPlrIfr16PCojonod+Ozk/pyP4h+C7a9S7Pb7W4CPvB4PAOrf3s9Ho9r9f+vAM95PJ5/\nXOvz2WyuIL5wwSdgy956Cd0VfEKE7goaEaG3gkZF6K6gUdnxO7yN4NL8FPDjNX9fcLvdT3s8nneA\nzwA/3ejD9+rCYLEYCAbjDyrjQ6MR5NxJMloshocgTXV2ku42gozQGHI2uu42whg/CKJfm9feVrGT\n5tx6IPp99+u2CqG7GyP6fffrdjqNYPC6gZtr/v594L+63W4lcA345pZIJRAIBAKBQCAQCASCbc22\nN3g9Hs//dcff48CJLRJHIBAIBAKBQCAQCAQNQqMkrRIIBAKBQCAQCAQCgeC+2PY7vPVmej7BR6N+\nxqYX6e9o4vhAGx1W/VaLJRDcFaG7AoFA8PAQc66gURG6K3jUeaQN3un5BP/ma+dIZXIATPlivHN+\nlj/4ymExEQi2NUJ3BQKB4OEh5lxBoyJ0VyB4xF2aPxr1k8rkaDGpOHHQSYtJRSqT46PRwD3fQyrd\n8Zm8BduQT6q7Qm8FAoHg3tmM9YJAsBWUdHctW6G7er3yobYnEKzlkd3hlUol3PTG+PXP7mV8JsKt\nuRjuzmb62s18eGkOqVRCPl+7RnHZPWRqkf5O4R4ieHh8Et0VeisQCAT3xyddLwgEW4VUKmFsarHq\nOc905KHo7unxECPXg3gDCVw2Pft7LRzta61rmyXOjIe4tKbtA70WjjyktgXbi0fW4M3nC5wYdvG1\nN66V33xNB+KcuzbP33txz12N3XXuIX7hHiJ4eDyo7gq9FQgEgvvnk6wXBIKtJJ8v0O00MuWPVZzr\ndhgfirH756+PVvxueHmg7kbvmfEQf1ajbWH0Pno80i7NV28tkMrk2NNp4n/8pYPs6TSRyuQYvbVQ\nvqbk+rnWBfTMWACzUYVKIQNApZBhNqo4MzZ/X+0Lt1LBg3I33b0XvS0xOhm57/aF7goEgkeJ0py7\nv8fMH/7aEfb3mCvWCwLBdqTVpEalkPH0kJ3/+E9P8PSQHZVCRotJXfe2R64HSWVyqBQy2lq0qBQy\nUpkcI9eDdW/70mrbvU4D/+jVffQ6DaQyOS49hLYF249HdofXaFQSW1zmd/6ei9HIZb4ffB/H/nae\nObGP995NIJFL+ZsfjQNS4sk0M/Nx9nSaGdjVQiiWQimXsb+3hU67iVtzUYKRZYLRFaaDCTosG++W\nCbdSwSfhbrrriyzz/iVfTb0d7GlBoyr+9JdTWT4e9RNbSnN8wHZXPRS6KxAIHjWMRiWJ6DK/9flB\nRq4v8Bc/HMNl0/Nbnx/kw3Pe4pwcS2+1mIJNxrvs5bTvPBPnbrHb3M1R+yFcGtdWi3VfKJUyZmZC\n/OaX7YxGLvOfLr2Jo72d39y/j3MfhVA+2U06nbv7jR4AvV7J3PwST+x3sJLOEowsM9jTglopZ3Y+\ngV6vJJGoz+9Go1GwGFnmH31ukMs3Fvi7jyZx2fQ8e7ST98960WgULC9n6tK2YHsiKRR2titOMBiv\n2cGzc+P85cRXSeduK71SpuBXdv8a3ltyFqJpzl4LrAv2VylkDO+x8cHIHE/sd1Q9v5GL6J1upffy\nmbVYLAaCwfhdr9tKdpKMFothy7YyH1R3Y/M6vvHj6zX1FuCpIQcfjz483W0EnYDGkLPRdbcRxvhB\nEP3atPa2pd6eHp/nz1+/VjH//cbLezjaZ30o8m01O1XHq+Fd9vLvTv+Ximfs7x/97ZpG73bV3Y3W\nC8OOvrrK9cOzM3z33ZsVv5tXTuziheH2urYtfrONsV54WDzSLs2jkcvrJgCAdC7DaOQyLouWXC5f\nNbNdJlfM1LiSzlY9f+pqAKlUUtXtc222vJKLR/G4yPQouHdq6e7VyGWCi8tV9TKdydJh02PQKlha\nqa27UN1luaS7d7omCd0VCAQ7nZHrC1WzNI9cFy7NO5HT/vNVn7Fn/Be2SKIHZ6O1br3xh5JV1w3+\nhWTd2y79ZtcifrOPLo+0S/NccqbqubnkDP/gUy6+/2H18/5QkqcPuThztTJmVyqVINVH+LORb+Bb\n9tLT1MXjrmFcGhdSqYRrU8WseMcH7etcPCSrBrJIfiG4Gxvp7mxyhs7c/qrnAuFlWpu09HWYGZ+u\nzNpY0t2vXf02s8lp+pp3lV24/JFlPFOLVV2TJmYWhe4KBIIdi9GoZD60XDVL888uzAmX5h2GVCph\nInyr6rnx8E2kPY3zvLvbWreeuqvXK5n2x/nU4xoyhmlCWR9dcjuKeAfTN+N1dWk2GpV4A4mq57yB\nxI7/zZbDz6YX6e8Q4WfwCBu8sViaLrMLb3yu4ly3uZ2fnJ7G0qRh2l/pCmAxa3jr42n6OsxMB9af\nf+KYmg+WXiMdK75N88bn+GjuDL9/9LcpJJqwNGlotxrWuUJPB+KoFDKO7bE+8gopuDt3091kqHo8\njsWs4cqN4pvNwZ6Wu+ruTGyO92c+5h8O/ib/71/O8nNHO3jjg8kKvf3CM70N8/AXCASC+yUWS/P0\nESdfe2OsIuPrV17cs6MXzo8i+XyBziYn09HZinNdTa6Get7dbb1QT91NJNIcP6bkjcBrpCPFd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Bm3Z8Nxp1lJaqZbddGRzgrX2vM+QfwS35OGDrorumg3PO8zx34vFsSu18ikcbXmOp4jZOyUmd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\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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(32, 32))\n", "sns.pairplot(data_viz1[['LOAN_AVG_DLQ_AMT', 'LOAN_MAX_DLQ_AMT', 'AGE', 'TARGET', 'FAMILY_INCOME']], hue='TARGET')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "I included only several variables in this pairplot, but it shows how variables can interact. Sometimes variables may interact in such a way, that their values cleate visible clusters based on target. New variables can be created based on this.\n", "Another use of the graph is to find correlated features. 'LOAN_AVG_DLQ_AMT' and 'LOAN_MAX_DLQ_AMT' seem to be highly correlated, let's have a look." ] }, { "cell_type": "code", "execution_count": 104, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "with sns.axes_style(\"white\"):\n", " sns.jointplot(x=data_viz1['LOAN_AVG_DLQ_AMT'], y=data_viz1['LOAN_MAX_DLQ_AMT'], kind=\"hex\", color=\"k\");" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Well, it seems that pearson correlation coefficient is 1 which shows very high correlation. I'll drop one of these columns." ] }, { "cell_type": "code", "execution_count": 105, "metadata": { "collapsed": true }, "outputs": [], "source": [ "data.drop(['LOAN_AVG_DLQ_AMT'], axis=1, inplace=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's try selecting variables based on IV again." ] }, { "cell_type": "code", "execution_count": 106, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['AGE',\n", " 'GEN_INDUSTRY',\n", " 'work_pens',\n", " 'WORK_TIME',\n", " 'PERSONAL_INCOME',\n", " 'GEN_PHONE_FL',\n", " 'SOCSTATUS_PENS_FL',\n", " 'SOCSTATUS_WORK_FL',\n", " 'LOAN_DLQ_NUM',\n", " 'LOAN_MAX_DLQ',\n", " 'LOAN_MAX_DLQ_AMT',\n", " 'FACT_LIVING_TERM',\n", " 'LOAN_NUM_CLOSED',\n", " 'FST_PAYMENT',\n", " 'TERM',\n", " 'Income_to_limit',\n", " 'LOAN_NUM_PAYM',\n", " 'FAMILY_INCOME',\n", " 'REG_FACT_POST_TP_FL',\n", " 'TARGET',\n", " 'AGREEMENT_RK']" ] }, "execution_count": 106, "metadata": {}, "output_type": "execute_result" } ], "source": [ "columns_to_try = [col for col in list(data.columns) if col not in ('AGREEMENT_RK', 'CARD_ID_SB8', 'CARD_NUM', 'TARGET')]\n", "ivs = []\n", "for col in columns_to_try:\n", " data[col] = data[col].astype('category')\n", " if data[col].isnull().any():\n", " print(col)\n", " if 'Unknown' not in data[col].cat.categories:\n", " data[col].cat.add_categories(['Unknown'], inplace=True)\n", " data[col].fillna('Unknown', inplace=True)\n", " data[col] = data[col].cat.remove_unused_categories()\n", " _, iv = functions.calc_iv(data, 'TARGET', col)\n", " ivs.append((col, np.round(iv, 4)))\n", "good_cols = [i[0] for i in sorted(ivs, key=lambda tup: tup[1], reverse=True) if i[1] > 0.02]\n", "for i in ['TARGET', 'AGREEMENT_RK']:\n", " good_cols.append(i)\n", "good_cols" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "One of the newly created features proved to be useful! Now it's time to go further. I'll dummify all features." ] }, { "cell_type": "code", "execution_count": 107, "metadata": { "collapsed": true }, "outputs": [], "source": [ "columns_dummify = [col for col in good_cols if col not in ('TARGET', 'AGREEMENT_RK')]\n", "data = data[good_cols]\n", "for col in columns_dummify:\n", " data[col] = data[col].astype('category')\n", " dummies = pd.get_dummies(data[col])\n", " dummies = dummies.add_prefix('{}_:_'.format(col))\n", " data.drop([col], axis=1, inplace=True)\n", " data = data.join(dummies)" ] }, { "cell_type": "code", "execution_count": 108, "metadata": { "collapsed": true }, "outputs": [], "source": [ "X = data.drop(['TARGET', 'AGREEMENT_RK'], axis=1)\n", "Y = data['TARGET']" ] }, { "cell_type": "code", "execution_count": 109, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(14276, 87)" ] }, "execution_count": 109, "metadata": {}, "output_type": "execute_result" } ], "source": [ "X.shape" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "87 variables could be okay, but I think it could be a good idea to reduce the number of them. There are various ways to select features: greedy algorithms, feature importance and so on. As I'm going to use Logistic Regression, I'll use sklearn's RandomizedLogisticRegression for this.\n", "\n", "[RandomizedLogisticRegression](http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.RandomizedLogisticRegression.html) basically runs Logistic Regression several times with various penalties for random coefficients. After the runs high scores are assigned to the most stable features." ] }, { "cell_type": "code", "execution_count": 110, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "array([ True, False, False, False, True, True, False, True, False,\n", " False, True, False, False, False, False, False, False, False,\n", " False, True, False, True, False, True, False, True, True,\n", " True, True, True, False, True, True, True, False, False,\n", " False, False, False, False, True, False, False, False, False,\n", " False, False, False, True, True, False, False, True, True,\n", " False, False, False, True, True, False, False, True, False,\n", " True, True, False, True, False, True, True, True, False,\n", " True, False, True, False, False, True, False, False, False,\n", " True, False, False, False, True, True], dtype=bool)" ] }, "execution_count": 110, "metadata": {}, "output_type": "execute_result" } ], "source": [ "randomized_logistic = linear_model.RandomizedLogisticRegression(C=0.1, selection_threshold=0.5,\n", " n_resampling=50, normalize=False)\n", "X_train_log = randomized_logistic.fit_transform(X=X, y=Y)\n", "randomized_logistic.get_support()" ] }, { "cell_type": "code", "execution_count": 111, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(14276, 36)" ] }, "execution_count": 111, "metadata": {}, "output_type": "execute_result" } ], "source": [ "X_train_log.shape" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "36 from 87 were selected. It's time for the model. I split data into train, test and validation sets. LogisticRegressionCV is used to choose an optimal regularization strength." ] }, { "cell_type": "code", "execution_count": 112, "metadata": { "scrolled": false }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 112, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "X_train, X_test, y_train, y_test = train_test_split(X_train_log, Y, test_size=0.2, stratify = Y)\n", "X_train, X_val, y_train, y_val = train_test_split(X_train, y_train, test_size=0.2, stratify = y_train)\n", "logreg = linear_model.LogisticRegressionCV(class_weight='balanced', n_jobs=-1, fit_intercept=True)\n", "logreg.fit(X_train, y_train)\n", "\n", "y_pred_log_val = logreg.predict_proba(X_val)\n", "y_pred_log_val_1 = [i[1] for i in y_pred_log_val]\n", "fpr_val, tpr_val, thresholds_val = roc_curve(y_val, y_pred_log_val_1)\n", "plt.plot(fpr_val, tpr_val, label='Validation')\n", "scores_val = cross_val_score(logreg, X_val, y_val, cv=5, scoring='roc_auc')\n", "\n", "y_pred_log_test = logreg.predict_proba(X_test)\n", "y_pred_log_test_1 = [i[1] for i in y_pred_log_test]\n", "fpr_test, tpr_test, thresholds_test = roc_curve(y_test, y_pred_log_test_1)\n", "plt.plot(fpr_test, tpr_test, label='Test')\n", "scores_test = cross_val_score(logreg, X_test, y_test, cv=5, scoring='roc_auc')\n", "\n", "plt.title('ROCAUC curve')\n", "plt.legend(loc='lower right')" ] }, { "cell_type": "code", "execution_count": 113, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Validation auc: 0.6906\n", "Cross-validation: mean value is 0.6612 with std 0.023.\n", "Test auc: 0.6728\n", "Cross-validation: mean value is 0.6593 with std 0.0308.\n" ] } ], "source": [ "print('Validation auc: ', np.round(auc(fpr_val, tpr_val), 4))\n", "print('Cross-validation: mean value is {0} with std {1}.'.format(np.round(np.mean(scores_val), 4),\n", " np.round(np.std(scores_val), 4)))\n", "print('Test auc: ', np.round(auc(fpr_test, tpr_test), 4))\n", "print('Cross-validation: mean value is {0} with std {1}.'.format(np.round(np.mean(scores_test), 4),\n", " np.round(np.std(scores_test), 4)))" ] }, { "cell_type": "code", "execution_count": 114, "metadata": {}, "outputs": [ { "data": { "text/html": 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FeatureCoefficient
0AGE_:_(0.0, 26.0]0.058152
1AGE_:_(38.0, 42.0]0.326568
2AGE_:_(42.0, 50.0]-0.049522
3AGE_:_(54.0, 67.0]-0.384079
4GEN_INDUSTRY_:_Market, real estate0.179091
5work_pens_:_30.515434
6WORK_TIME_:_(6.5, 21.5]0.288714
7WORK_TIME_:_(35.5, 53.5]0.168606
8WORK_TIME_:_(85.5, 151.0]-0.168910
9WORK_TIME_:_(151.0, 600.0]-0.381151
10PERSONAL_INCOME_:_(0.0, 7600.0]-0.340836
11PERSONAL_INCOME_:_(7600.0, 9300.0]-0.285362
12PERSONAL_INCOME_:_(9300.0, 11000.0]-0.164639
13PERSONAL_INCOME_:_(14800.0, 15300.0]0.277408
14PERSONAL_INCOME_:_(15300.0, 20800.0]0.056118
15PERSONAL_INCOME_:_(20800.0, 44000.0]0.535057
16LOAN_DLQ_NUM_:_0-0.685242
17FACT_LIVING_TERM_:_(38.5, 85.5]0.154556
18FACT_LIVING_TERM_:_(85.5, 131.5]-0.141100
19FACT_LIVING_TERM_:_(238.5, 1000.0]-0.104258
20LOAN_NUM_CLOSED_:_00.178287
21FST_PAYMENT_:_(0.0, 200.0]0.310499
22FST_PAYMENT_:_(200.0, 900.0]0.216670
23FST_PAYMENT_:_(1700.0, 2100.0]-0.207217
24FST_PAYMENT_:_(3800.0, 6000.0]-0.440642
25FST_PAYMENT_:_(6000.0, 75600.0]-0.405891
26TERM_:_(4.5, 8.5]-0.128436
27TERM_:_(11.5, 36.0]0.347700
28Income_to_limit_:_(0.0, 0.515]-0.014197
29Income_to_limit_:_(0.515, 0.783]-0.027910
30Income_to_limit_:_(1.108, 1.428]0.245322
31Income_to_limit_:_(1.962, 2.556]0.225381
32LOAN_NUM_PAYM_:_(3.5, 4.5]0.141627
33LOAN_NUM_PAYM_:_(11.5, 110.0]-0.174840
34REG_FACT_POST_TP_FL_:_00.220352
35REG_FACT_POST_TP_FL_:_1-0.222208
\n", "
" ], "text/plain": [ " Feature Coefficient\n", "0 AGE_:_(0.0, 26.0] 0.058152\n", "1 AGE_:_(38.0, 42.0] 0.326568\n", "2 AGE_:_(42.0, 50.0] -0.049522\n", "3 AGE_:_(54.0, 67.0] -0.384079\n", "4 GEN_INDUSTRY_:_Market, real estate 0.179091\n", "5 work_pens_:_3 0.515434\n", "6 WORK_TIME_:_(6.5, 21.5] 0.288714\n", "7 WORK_TIME_:_(35.5, 53.5] 0.168606\n", "8 WORK_TIME_:_(85.5, 151.0] -0.168910\n", "9 WORK_TIME_:_(151.0, 600.0] -0.381151\n", "10 PERSONAL_INCOME_:_(0.0, 7600.0] -0.340836\n", "11 PERSONAL_INCOME_:_(7600.0, 9300.0] -0.285362\n", "12 PERSONAL_INCOME_:_(9300.0, 11000.0] -0.164639\n", "13 PERSONAL_INCOME_:_(14800.0, 15300.0] 0.277408\n", "14 PERSONAL_INCOME_:_(15300.0, 20800.0] 0.056118\n", "15 PERSONAL_INCOME_:_(20800.0, 44000.0] 0.535057\n", "16 LOAN_DLQ_NUM_:_0 -0.685242\n", "17 FACT_LIVING_TERM_:_(38.5, 85.5] 0.154556\n", "18 FACT_LIVING_TERM_:_(85.5, 131.5] -0.141100\n", "19 FACT_LIVING_TERM_:_(238.5, 1000.0] -0.104258\n", "20 LOAN_NUM_CLOSED_:_0 0.178287\n", "21 FST_PAYMENT_:_(0.0, 200.0] 0.310499\n", "22 FST_PAYMENT_:_(200.0, 900.0] 0.216670\n", "23 FST_PAYMENT_:_(1700.0, 2100.0] -0.207217\n", "24 FST_PAYMENT_:_(3800.0, 6000.0] -0.440642\n", "25 FST_PAYMENT_:_(6000.0, 75600.0] -0.405891\n", "26 TERM_:_(4.5, 8.5] -0.128436\n", "27 TERM_:_(11.5, 36.0] 0.347700\n", "28 Income_to_limit_:_(0.0, 0.515] -0.014197\n", "29 Income_to_limit_:_(0.515, 0.783] -0.027910\n", "30 Income_to_limit_:_(1.108, 1.428] 0.245322\n", "31 Income_to_limit_:_(1.962, 2.556] 0.225381\n", "32 LOAN_NUM_PAYM_:_(3.5, 4.5] 0.141627\n", "33 LOAN_NUM_PAYM_:_(11.5, 110.0] -0.174840\n", "34 REG_FACT_POST_TP_FL_:_0 0.220352\n", "35 REG_FACT_POST_TP_FL_:_1 -0.222208" ] }, "execution_count": 114, "metadata": {}, "output_type": "execute_result" } ], "source": [ "coefs = pd.DataFrame(list(zip(X[X.columns[randomized_logistic.get_support()]].columns, logreg.coef_[0])),\n", " columns=['Feature', 'Coefficient'])\n", "coefs" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "And here we can see how each category influenced the result." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "So, this is it. The score is quite high, accuracy on real test set should be lower, but hopefully not much. There are many ways to enchance the model, of course:\n", "- Transform variables with more care - maybe change parameters for DecisionTreeClassifier for specific variables to create better bins;\n", "- Fill missing values with something else;\n", "- Treat outliers instead of dropping rows with them;\n", "- Create more variables bases of feature interaction;\n", "- Try different threshold for feature selection);\n", "\n", "And if interpreting variables isn't necessary, then continuous variables can be used without binning. Maybe they can be transformed some way or scaled. More sophisticated algorithms can be used such as a reputable xgboost and so on." ] } ], "metadata": { "kernelspec": { "display_name": "Python [Root]", "language": "python", "name": "Python [Root]" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.1" } }, "nbformat": 4, "nbformat_minor": 2 }