{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Ch. 9 - Feature engineering & data preparation\n", "In this chapter we will prepare the data we got from the bank marketing campaign. We will examine it closely, clean it up and make it ready for our neural network." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Preparing out tools\n", "Before starting out on the actual analysis, it makes sense to prepare the tools of trade. We will use four libraries:" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "# As always, first some libraries\n", "# Numpy handles matrices\n", "import numpy as np\n", "# Pandas handles data \n", "import pandas as pd\n", "# Matplotlib is a plotting library\n", "import matplotlib.pyplot as plt\n", "# Set matplotlib to render imediately\n", "%matplotlib inline\n", "# Seaborn is a plotting library built on top of matplotlib that can handle some more advanced plotting\n", "import seaborn as sns" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To make our charts look nice, we will use the FiveThirtyRight color scheme. [FiveThirtyEight](http://fivethirtyeight.com/) is a quantitative journalism website that has built a very nice graph scheme. So to make this chapter pretty, we will use their scheme." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "# Define colors for seaborn\n", "five_thirty_eight = [\n", " \"#30a2da\",\n", " \"#fc4f30\",\n", " \"#e5ae38\",\n", " \"#6d904f\",\n", " \"#8b8b8b\",\n", "]\n", "# Tell seaborn to use the 538 colors\n", "sns.set(palette=five_thirty_eight)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## The data\n", "The data is taken from [Moro et al., 2014](https://archive.ics.uci.edu/ml/datasets/bank+marketing) via the UCI machine learning repository. The balanced version we are working with is included in the GitHub repository." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "# Load data with pandas\n", "df = pd.read_csv('balanced_bank.csv',index_col=0)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Getting an overview\n", "The first step in data preparation is to check what we are actually working with. After we have surveyed the dataset as a whole we will look at the individual features in it. As a start we can use pandas ```head()```function to get the first few rows of the dataset for a manual overview." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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agejobmaritaleducationdefaulthousingloancontactmonthday_of_week...campaignpdayspreviouspoutcomeemp.var.ratecons.price.idxcons.conf.idxeuribor3mnr.employedy
3457935admin.singleuniversity.degreenoyesnocellularmaythu...19991failure-1.892.893-46.21.2665099.1no
44642technicianmarriedprofessional.coursenononotelephonemaytue...19990nonexistent1.193.994-36.44.8575191.0yes
2017336admin.marrieduniversity.degreenononocellularaugmon...29990nonexistent1.493.444-36.14.9655228.1yes
1817137admin.marriedhigh.schoolnoyesyestelephonejulwed...29990nonexistent1.493.918-42.74.9635228.1yes
3012831managementsingleuniversity.degreenoyesnocellularaprthu...19990nonexistent-1.893.075-47.11.3655099.1no
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5 rows × 21 columns

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" ], "text/plain": [ " age job marital education default housing loan \\\n", "34579 35 admin. single university.degree no yes no \n", "446 42 technician married professional.course no no no \n", "20173 36 admin. married university.degree no no no \n", "18171 37 admin. married high.school no yes yes \n", "30128 31 management single university.degree no yes no \n", "\n", " contact month day_of_week ... campaign pdays previous \\\n", "34579 cellular may thu ... 1 999 1 \n", "446 telephone may tue ... 1 999 0 \n", "20173 cellular aug mon ... 2 999 0 \n", "18171 telephone jul wed ... 2 999 0 \n", "30128 cellular apr thu ... 1 999 0 \n", "\n", " poutcome emp.var.rate cons.price.idx cons.conf.idx euribor3m \\\n", "34579 failure -1.8 92.893 -46.2 1.266 \n", "446 nonexistent 1.1 93.994 -36.4 4.857 \n", "20173 nonexistent 1.4 93.444 -36.1 4.965 \n", "18171 nonexistent 1.4 93.918 -42.7 4.963 \n", "30128 nonexistent -1.8 93.075 -47.1 1.365 \n", "\n", " nr.employed y \n", "34579 5099.1 no \n", "446 5191.0 yes \n", "20173 5228.1 yes \n", "18171 5228.1 yes \n", "30128 5099.1 no \n", "\n", "[5 rows x 21 columns]" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Display first five rows for a rough overview\n", "df.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As we can see, there are 21 columns, 20 features plus the output. We can also see that categorical data is saved in text form. That is, instead of using numbers to indicate a category it uses text such as 'married' to indicate a marital status. We can also see an 'unknown' in the default column. That is, the value is not missing, but it is set to the text 'unknown'. We will have to decide later on how we want to deal with unknowns, first, lets check whether there is any actual data missing:" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "age 0\n", "job 0\n", "marital 0\n", "education 0\n", "default 0\n", "housing 0\n", "loan 0\n", "contact 0\n", "month 0\n", "day_of_week 0\n", "duration 0\n", "campaign 0\n", "pdays 0\n", "previous 0\n", "poutcome 0\n", "emp.var.rate 0\n", "cons.price.idx 0\n", "cons.conf.idx 0\n", "euribor3m 0\n", "nr.employed 0\n", "y 0\n", "dtype: int64" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Count missing values per column\n", "df.isnull().sum()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "There seem to be no missing values, good! But as we noted earlier, some values might be 'unknown'. Let's check the data types we are dealing with. There are three common datatypes we might encounter:\n", "- int: integers (1,2,3,4,...)\n", "- float: floating point numbers with decimals (1.123,1.124,...)\n", "- object: some non numeric data type, often text" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "age int64\n", "job object\n", "marital object\n", "education object\n", "default object\n", "housing object\n", "loan object\n", "contact object\n", "month object\n", "day_of_week object\n", "duration int64\n", "campaign int64\n", "pdays int64\n", "previous int64\n", "poutcome object\n", "emp.var.rate float64\n", "cons.price.idx float64\n", "cons.conf.idx float64\n", "euribor3m float64\n", "nr.employed float64\n", "y object\n", "dtype: object" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Display datatypes\n", "df.dtypes" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Job\n", "Next on our feature list is the job that people have. This is a categorical variable currently in text form. We will search for obvious correlations with the output and see whether we can do any clever feature engineering. Since this is a categorical variable, we need to define a new function to draw the frequencies by outcome." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "# Function to draw frequencies by outcome\n", "def draw_conditional_barplot(feature,df):\n", " # Set matplotlib style\n", " plt.style.use('fivethirtyeight')\n", " # Count the total yes responses in our dataset\n", " n_yes = len(df[df.y == 'yes'])\n", " # Count the total no responses in our dataset\n", " n_no = len(df[df.y == 'no'])\n", " # Count the frequencies of the different jobs for the yes people\n", " yes_cnts = df[df.y == 'yes'][feature].value_counts() / n_yes * 100\n", " # Count frequencies of jobs for the nay sayers\n", " no_cnts = df[df.y == 'no'][feature].value_counts() / n_no * 100\n", " \n", " # A potential problem of creating two different frequency tables is that if one group (perhaps the yes crowd)...\n", " # ... does not include a certain category (like a certain job) then it will not be in the frequency tables at all\n", " # ... So we have to join them in one table to ensure that all categories are included\n", " # ... When merging the frequncy tables, missing categories will be marked with 'NA'\n", " # ... We can then replace all NAs with zeros, which is the correct frequency\n", " \n", " # Create a new dataframe that includes all frequencies\n", " res = pd.concat([yes_cnts,no_cnts],axis=1)\n", " # Name the columns of the new dataframe (yes crowd and nay sayers)\n", " res.columns = ['yes','no']\n", " # Fill empty fields with zeros\n", " res = res.fillna(0)\n", " \n", " # N = number of categories\n", " N = len(res['yes'])\n", " # Create an array for the locations of the group (creates an array [0,1,2,...,N])\n", " ind = np.arange(N) \n", " # Specify width of bars\n", " width = 0.35 \n", " # Create empty matplotlib plot\n", " fig, ax = plt.subplots()\n", " # Add bars of the nay sayers\n", " rects1 = ax.bar(ind, res['no'], width)\n", " # Add bars of the yes crowd\n", " rects2 = ax.bar(ind + width, res['yes'], width)\n", "\n", " # Add label: feature name (e.g. job) in percent\n", " ax.set_ylabel(feature + ' in percent')\n", " # Add title\n", " ax.set_title(feature + ' by conversion')\n", " # Add ticks \n", " ax.set_xticks(ind + width / 2)\n", " # Add categorie names as tick labels\n", " ax.set_xticklabels(res.index.values)\n", " # Rotate labels 90 degrees\n", " labels = ax.get_xticklabels()\n", " plt.setp(labels, rotation=90)\n", "\n", " # Add legend\n", " ax.legend((rects1[0], rects2[0]), ('No', 'Yes'))\n", " \n", " # Render plot\n", " plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we can draw our conditional barplot" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "image/png": 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QiIiIqFCzZs3Czp078ffffwMAUlJScOzYMWzevBmbN29GdHS0tM5ZSWJBISIiokKZmJhg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\nIvlxmzFZ6d9v7LhcLrS2tgpMkxtYoJClqqqqEA6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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "draw_conditional_barplot('job',df)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Students and retirees seem to be eager to subscribe. Both jobs are highly correlated with age, so we might have captured this already. The other jobs seem to be a bit more mixed. What we can also see from this chart is that there is a relatively small number of different jobs in the dataset, with just three groups (administrators, blue-collar workers, technicians) making up the bulk of customers in the data set. We also see that there are relatively few customers whose jobs are unknown. We will simply treat it as its own category here. Now we can convert this categorical variable into a set of dummy variables:" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "# Add job_ to every value in jobs so that the dummies have readable names\n", "df['job'] = 'job_' + df['job'].astype(str)\n", "\n", "# Get dummies\n", "dummies = pd.get_dummies(df['job'])\n", "\n", "# Add dummies to dataframe\n", "df = pd.concat([df,dummies],axis=1)\n", "\n", "# Remove original job column\n", "del df['job']" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Theory: What are dummy variables and why do we use them?\n", "[Dummy variables](https://goo.gl/37nWD) are proxy variables for qualitative information. They convert cateogrical data into mutually exclusive features. So in the case of the jobs the turn the textual information ('admin', 'blue-collar', etc.) into new features that describe weather an instance is members of a certain category or not. Is this customers job admin (yes /no) is this customers job blue-collar (yes / no) and so on. This enables us to use qualitative information in our quantiative model. You can see how the single job feature was turned into a bunch of new features if we print out the data types again:" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "age int64\n", "marital object\n", "education object\n", "default object\n", "housing object\n", "loan object\n", "contact object\n", "month object\n", "day_of_week object\n", "duration int64\n", "campaign int64\n", "pdays int64\n", "previous int64\n", "poutcome object\n", "emp.var.rate float64\n", "cons.price.idx float64\n", "cons.conf.idx float64\n", "euribor3m float64\n", "nr.employed float64\n", "y object\n", "job_admin. uint8\n", "job_blue-collar uint8\n", "job_entrepreneur uint8\n", "job_housemaid uint8\n", "job_management uint8\n", "job_retired uint8\n", "job_self-employed uint8\n", "job_services uint8\n", "job_student uint8\n", "job_technician uint8\n", "job_unemployed uint8\n", "job_unknown uint8\n", "dtype: object" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.dtypes" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Age\n", "The age is the age of the client in years. As we can see from the datatype, it is an integer. It might be interesting to see weather age has any direct influence on weather a customer subscribes. We will draw a conditional distribution plot that shows the age distribution of all customers that said yes and of those that said no." ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "# Function to draw conditional distribution plot splot by weather the customer subscribed\n", "def draw_conditional_distplot(feature,df):\n", " # Seat seaborn to use nice colors\n", " sns.set(palette=five_thirty_eight)\n", " # Draw the no plot\n", " sns.distplot(df[df.y == 'no'][feature],label='no')\n", " # Draw the yes plot\n", " sns.distplot(df[df.y == 'yes'][feature],label='yes')\n", " # Draw the legend\n", " plt.legend()\n", " # Display the plot\n", " plt.show()" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "image/png": 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df+aZZ5JUnqRcLIarchuxsjE4OUM6fNuBtkGkowyFjq4K5+Tx2rJ7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\nmXu2Efh4NX5mDtHJl/ZqQbcDnoiV0YrNdHc7g3T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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Now we can just use the function defined above\n", "draw_conditional_distplot('age',df)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "There is a couple of useful findings in here. First, over a certain age, around 60 a lot of people subscribed! Between 35 and 60 the nay sayers have the upper hand while below 35 it seems to be a little more of the yes crowd. We can probably improve our forcasts if we don't treat age as a [ratio variable](https://www.graphpad.com/support/faqid/1089/) but split it into three buckets and treat those as categorical variables." ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "# Create old people group\n", "df['age_old'] = np.where(df['age'] >= 60, 1,0)\n", "# Create mid age people group\n", "df['age_mid'] = np.where((df['age'] <= 60) & (df['age'] >= 35), 1,0)\n", "# Create young people group\n", "df['age_young'] = np.where(df['age'] <= 35, 1,0)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now, the feature age itself is redundant and we can remove it" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [], "source": [ "# Remove original age\n", "del df['age']" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we can see that the original age is gone but we have three new categorical variables" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Marital status\n", "Marital status is one of the common variables many firms collect about their customers. It usually correlates highly with age, although through the rise of divorce rates this relationship is in decline. We will plot our conditional barplot again to get an overview." ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "image/png": 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ulNls1qpVq5SWlqYZM2ZwmzEAAChSVj8HpUmTJpbbjC9cuCBPT095eHjYMhsA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\nAAB2ZPcRlJiYGF29elWLFy/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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "draw_conditional_barplot('marital',df)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The majority of people called where married, there seems to be no category 'widow', only 'single' which decreases the correlation with age. While there are some unknowns, they make up for a tiny fraction, so again, we will just treat it as its own category. There seem to be some differences between married and single people, but the difference is quite small. However, there might be some higher order correlation of combined features and the outcome of which marital status is a part. We will convert this one to dummy variables as well." ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [], "source": [ "# Add marital_ to every value in marital so that the dummies have readable names\n", "df['marital'] = 'marital_' + df['marital'].astype(str)\n", "\n", "# Get dummies\n", "dummies = pd.get_dummies(df['marital'])\n", "\n", "# Add dummies to df\n", "df = pd.concat([df,dummies],axis=1)\n", "\n", "#remove original column\n", "del df['marital']" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Education\n", "Education is another standard demographic variable, so we will proceed as we did with the marital status." ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "image/png": 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JEybg+++/x6BBg/Dxxx/DysoKZ86cwa1bt+Dq6or+/fu/dD3Dhg3DsWPHMHv2bBw9ehSO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\n0bHoLQX+oDQRDavi4mIAQE5ODkJCQuDxeODxePD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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "draw_conditional_barplot('education',df)" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [], "source": [ "# Add education_ to every value in education so that the dummies have readable names\n", "df['education'] = 'education_' + df['education'].astype(str)\n", "\n", "# Get dummies\n", "dummies = pd.get_dummies(df['education'])\n", "\n", "# Add dummies to df\n", "df = pd.concat([df,dummies],axis=1)\n", "\n", "#remove original column\n", "del df['education']" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Default\n", "Weather the client has a credit in default." ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "image/png": 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j0NBQmUwmrV69WidPnlRqaqqWLl1q75wAAMCF2DSC0qhRI61atUpt27ZVUlKS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\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "draw_conditional_barplot('default',df)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here, 'unknown' makes for an interesting feature. If the bank does not know weather the client defaulted before, the sales agent might have to ask because the bank might not want to sign up defaulting customers. We can see, that relatively more members of the nay sayers have unknowns default status than of the yes crowd. There are very few people who have defaulted, it is not even visible in the chart. We will give the numbers a closer look." ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " yes no\n", "no 90.452586 77.155172\n", "unknown 9.547414 22.844828\n" ] } ], "source": [ "# This code was copied from our barchart plotting function, but we will print out the numbers instead of plotting them\n", "# Count total yes responses\n", "n_yes = len(df[df.y == 'yes'])\n", "# Count the total no responses in our dataset\n", "n_no = len(df[df.y == 'no'])\n", "# Count the frequencies of the different jobs for the yes people\n", "yes_cnts = df[df.y == 'yes']['default'].value_counts() / n_yes * 100\n", "# Count frequencies of jobs for the nay sayers\n", "no_cnts = df[df.y == 'no']['default'].value_counts() / n_no * 100\n", "\n", "# Create a new dataframe that includes all frequencies\n", "res = pd.concat([yes_cnts,no_cnts],axis=1)\n", "# Name the columns of the new dataframe (yes crowd and nay sayers)\n", "res.columns = ['yes','no']\n", "# Fill empty fields with zeros\n", "res = res.fillna(0)\n", "\n", "print(res)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "A tiny share of the nay sayers defaulted earlier, while none of the yes crowd did. A significant higher share of the nay sayers had an unknown status. This will make for an interesting feature, we will convert it to dummy variables as well." ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [], "source": [ "# Ensure dummy names are well readable\n", "df['default'] = 'default_' + df['default'].astype(str)\n", "# Get dummies\n", "dummies = pd.get_dummies(df['default'])\n", "# Get dummies\n", "dummies = pd.get_dummies(df['default'])\n", "# Add dummies to df\n", "df = pd.concat([df,dummies],axis=1)\n", "#remove original column\n", "del df['default']" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Housing\n", "Weather a customer has a housing loan." ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "data": { "image/png": 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YMUN79uzRjh075Ofnl+/bB/DXxxkUwEWtX79e7dq10+HDh+2W//jjjzp69KiaNGnikHJy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\nYDoUFAAAYDr/B8YZde2s93BpAAAAAElFTkSuQmCC\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "draw_conditional_barplot('housing',df)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "While this one does not look very interesting, we will convert it to a dummy variable and add it in, as it might have a correlation to the outcome in correlation with other variables." ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [], "source": [ "# Ensure dummy names are well readable\n", "df['housing'] = 'housing_' + df['housing'].astype(str)\n", "\n", "# Get dummies\n", "dummies = pd.get_dummies(df['housing'])\n", "\n", "# Add dummies to df\n", "df = pd.concat([df,dummies],axis=1)\n", "\n", "#remove original column\n", "del df['housing']" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Loan\n", "Weather the customer has a personal loan." ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "data": { "image/png": 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xdBfPc889p+HDh6tfv34KDAyUxWLRvHnzdOzYMVksFs2ZM8fZOQEAQCli6AxK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\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "draw_conditional_barplot('loan',df)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "No obvious observations here, convert to dummies and proceed." ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [], "source": [ "# Ensure dummy names are well readable\n", "df['loan'] = 'loan_' + df['loan'].astype(str)\n", "\n", "# Get dummies\n", "dummies = pd.get_dummies(df['loan'])\n", "\n", "# Add dummies to df\n", "df = pd.concat([df,dummies],axis=1)\n", "\n", "#remove original column\n", "del df['loan']" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Type of contact\n", "How the customer is contacted, cellphone or land line (marked as 'telephone')" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "data": { "image/png": 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//94h4QAAgHuyq6AULVpUFy9ezHfelStX7ikuAAAAv4VdBaVJkyaaOXOmjhw5\nkmf6kSNHNGvWLDVq1Mgh4QAAgHuya9fHgAEDtG/fPvXq1Utly5ZVqVKldO3aNV24cEFBQUEaOHCg\no3MCAAA3YldBKVWqlBYtWqQ1a9Zo3759un79usqVK6fu3burY8eO8vX1dXROAADgRuwqKO+++646\nduyoLl26qEuXLo7OBAAA3Jxd56Bs3rxZt2/fdnQWAAAASXYWlNq1a2vnzp2yWq2OzgMAAGDfIZ7Q\n0FAtW7ZMW7ZsUZUqVVSqVKk8800mk0aNGuWIfAAAwA3ZVVD++9//6pFHHpEknTp16p7775hMpsJP\nBgAA3JZdBWXVqlWOzgEAAGBj992M70pJSdGBAwd069YtZWZmOiITAABwc3aPUb9jxw5NnTpVp06d\nkslk0rx58zR37lwFBARo+PDh8vCwr+u88MIL8vPzkyRVqFBBnTt31qRJk+Tp6akGDRooOjr6wR4J\nAAB4aNhVUBISEjR06FA1bNhQ3bp10wcffCBJCg8P15QpUxQUFKTevXvfdz13L1WOi4uzTevZs6cm\nTpyooKAgDRkyRIcPH1aNGjUe5LEAAICHhF27PWbOnKk//OEP+vDDD9WpUyfb5cbPP/+8XnzxRa1Z\ns8aujZnNZmVmZmrgwIHq37+/kpOTlZ2drYoVK8pkMqlhw4ZKSkp68EcDAAAeCnbtQTl+/LhiYmLy\nnRcZGalPPvnEro35+PjohRde0LPPPqtTp07p1Vdflb+/v22+r6+vzp49a9e6zGazXcv9OgzZD/fh\nmPcQ4Hi8dh8eYWFhBc6zq6D4+fkpJSUl33nnzp3LUzJ+SeXKlW17S4KDg+Xn56fr16/b5mdkZNi9\nrl96UA9sh33lCHgYOOQ9BDiY2Wzmtesm7DrE06JFC8XFxem7776zTTOZTLpw4YLmzZunpk2b2rWx\nL7/8UlOmTJEkXbp0SZmZmSpWrJjOnDkjq9WqxMRE1a1b9wEeBgAAeJiYUlNT7zt+/c2bNxUbGyuz\n2ayAgABdu3ZNFStW1MWLF1W+fHnNmjVLAQEB991Ydna2Ro8ebdsbM3DgQJlMJn344YfKyclRgwYN\nFBsb+9sf1QMKmMceFFewbO3p6ghuKW3BVldHAH419qC4D7sKinSnXKxdu1a7d+/W9evX5efnp/Dw\ncHXs2FE+Pj6OzukUFBTXoKC4BgUFv0cUFPdh9zgoRYoUUadOndSpUydH5gEAALC/oPz444+aP3++\n9u7dq+unSkm5AAAXm0lEQVTXr6tUqVJq2LCh+vbtq6CgIEdmBAAAbsaugpKUlKRXX31VJUuWVNOm\nTVWqVClduXJFCQkJ2rJli+bMmaPQ0FBHZwUAAG7CroIyY8YM1a1bV5MmTVLRokVt0zMzMzV48GBN\nmTJFU6dOdVhIAADgXuy6zPjo0aPq2bNnnnIi/f/Aaz+//BgAAOC3squglCtXrsARXlNTUxUYGFio\noQAAgHuzq6AMHjxYs2fP1oYNG5STk2ObnpiYqLi4OA0ePFi5ubm2/wAAAH4Lu8ZB6dChg27cuKGs\nrCx5eHgoMDBQN27cUHZ2tqxWq0wm0/+v0GTSN99849DQjsI4KK7BOCiuwTgo+D1iHBT3YddJss8+\n+6yjcwAAANjYVVCio6MdnQMAAMDGrnNQAAAAnImCAgAADIeCAgAADIeCAgAADIeCAgAADMfuuxl/\n88032r59u27dunXPYGwmk0mjRo0q7GwAAMBN2VVQFi1apKlTp8rb21uBgYF5BmaTdM/3AAAAv4Vd\nBWXp0qVq27atRo4cqSJFijg6EwAAcHN2nYNy9epVderUiXICAACcwq6CEhISotOnTzs6CwAAgCQ7\nC0psbKzmzZunxMREZWRk5LlzMXcwBgAAhc2uc1A++OADpaam6tVXX813/u/5DsYAAMB47Coo7dq1\nc3QOAAAAG+5mDAAADKfAgpKUlKRatWrJ19dXSUlJ911RvXr1CjUYAABwXwUWlFdeeUVz585VrVq1\n9Morr8hkMslqteZZ5u40k8mkxMREh4cFAADuocCCMmPGDIWEhNi+BgAAcJYCC0p4eHi+XwMAADia\n0+9mfPXqVXXo0EEnTpzQ6dOnFR0drejoaE2YMIHxVAAAgCQnFxSLxaLx48eraNGikqTJkycrJiZG\ns2fPltVqVXx8vDPjAAAAg3JqQZkyZYqee+45lSlTRpJ0+PBh2+Gjxo0b23W1EAAAePjZNQ5KYViz\nZo0CAgLUqFEjLViwQJJsVwBJkq+vr9LS0pwVBwB+k4B5Z10dwS0lNXV1AjiLXQVl7Nix6tu3r4KC\ngu6Zd/LkSU2ZMkWTJk36xXV8+eWXMplMSkpK0pEjRzRq1Chdu3bNNj8jI0P+/v52BzebzXYvaz9f\nB6wTMCbHvIfcCZ8XrsJr9+ERFhZW4LwCC8qFCxdsX69du1YtWrSQp6fnPcvt3LnTrkMzs2bNsn0d\nExOj4cOHa+rUqdqzZ48iIiKUkJCgyMjI+67nrl96UA9sB/8igvtwyHvInfB54TK8dt1DgQVl4sSJ\neW4AOGzYsHyXs1qtaty48QNtfPDgwRo3bpyys7MVEhKiqKioB1oPAAB4uBRYUIYPH67ExERZrVaN\nHz9evXv3vucQj6enp/z9/dWwYcNftdG4uDjb1zNnzvyVkQEAwMOuwIJStmxZPfvss3cW8vJS06ZN\nFRAQYJtvsVhs8wAAAAqTXZcZ/+lPf9Jnn32m/v3726bt27dPbdu2tV2RAwAAUFjsKijz58/XwoUL\n9cQTT9imValSRV26dNHs2bO1bNkyhwUEAADux67jM6tXr9aAAQPUs2dP27RHHnlEsbGx8vPz07Jl\ny9S1a1eHhQQAAO7Frj0oly5dUvXq1fOdV7NmTZ0/f75QQwEAAPdmV0EJCgpSYmJivvOSkpJUtmzZ\nQg0FAADcm12HeDp37qzJkycrOztbLVu2VGBgoK5du6b4+HgtW7ZMAwcOdHROAADgRuwqKN26ddOV\nK1e0aNEiLV26VNKdAdq8vLzUvXt3de/e3aEhAQCAe7F7EJPY2Fj16tVLBw4c0PXr1+Xv769atWrl\nGRsFAACgMPyqUdb8/PzUqFGje6bfuHFDJUqUKLRQAADAvdlVUG7fvq3FixcrOTlZWVlZslqtku4c\n5rl165ZOnDihHTt2ODQoAABwH3YVlI8++kjLli1TaGiorl27pqJFiyowMFBHjx6VxWJRv379HJ0T\nAAC4EbsuM966dat69uypxYsX6y9/+Ytq1KihefPmafny5SpfvrxtjwoAAEBhsKugXL16VY0bN5Yk\nVatWTd9//70k6dFHH1Xv3r21ceNGxyUEAABux66C4u/vr6ysLElSxYoVdfHiRaWlpUmSKlWqpAsX\nLjguIQAAcDt2FZS6devq888/V0ZGhipVqqRixYpp69atkqQDBw7Iz8/PkRkBAICbsaug9OvXTwcO\nHNBrr70mLy8v/fnPf9b48ePVo0cPxcXFqVWrVo7OCQAA3IhdV/GEhYVp6dKlOnbsmCRpwIABKl68\nuL777ju1atVKL774okNDAgAA92JXQUlOTlaNGjXUoEEDSZLJZLKVkps3b2rLli1q27at41ICAAC3\nYtchntjYWJ04cSLfeT/++KPGjh1bmJkAAICbK3APyjvvvKOLFy9KujNi7IQJE1S8ePF7ljt9+rRK\nlSrluIQAAMDtFLgHJSoqShaLRRaLRZKUk5Nj+/7uf1arVTVr1mQPCgAAKFQF7kFp0aKFWrRoIUnq\n37+/hg0bppCQEKcFAwAA7suuk2RnzJjh6BwAAAA2dhWUzMxMffzxx9q2bZsyMzOVm5ubZ77JZNKX\nX37pkIAAAMD92FVQPvzwQ61atUrh4eF69NFH5eFh18U/AAAAD8SugrJlyxbFxMSoT58+Do4DAABg\n5zgoWVlZeuKJJxydBQAAQJKdBSU8PFx79uxxdBYAAABJdh7ieeGFF/Tmm2/KYrHoiSeekI+Pzz3L\n1KtX777rycnJ0bhx43Ty5El5eHho5MiRslqtGjNmjCQpNDRUw4YN4xwXAADcnF0FJTY2VpK0YMGC\nPNNNJpOsVqtMJpMSExPvu57t27dLkubMmaM9e/Zo8uTJslqtiomJUUREhMaPH6/4+HjujgwAgJtz\n6jgoLVu2VNOmTSVJ58+fV6lSpbRz506Fh4dLkho3bqxdu3ZRUAAAcHN2FZS7BaJQNujlpVGjRik+\nPl7jx4/Xjh07ZDKZJEm+vr5KS0srtG0BAIDfJ7sKiiSdOHFCcXFx2rNnj9LS0lSyZEnVrVtX0dHR\nCg0N/VUbHTVqlC5fvqy+ffvq9u3btukZGRny9/e3ax1ms/lXbdM+vg5YJ2BMjnkPuRM+L1yF1+7D\nIywsrMB5dhWUY8eOqV+/fipSpIiaNWum0qVL6/Lly9qxY4e++eYbzZ07166Ssm7dOl28eFF9+vSR\nj4+PTCaTatasqT179igiIkIJCQmKjIz8zQ/qge04W/jrBAzKIe8hd8Lnhcvw2nUPdhWUadOmKSgo\nSHFxcfLz87NNT0tLU2xsrOLi4vTBBx/cdz2tWrXSmDFj9PLLL8tisei1115TlSpVNG7cOGVnZysk\nJERRUVEP/mgAAMBDwa6CsnfvXr399tt5yokk+fn5qXfv3ho/frxdGytWrFi+y86cOdOunwcAAO7B\nrgFHihQpoiJFiuQ7z9vbW9nZ2YUaCgAAuDe7Csrjjz+upUuXymq15plutVr1+eef6/HHH3dIOAAA\n4J7sOsTzt7/9Tf369dPzzz+v1q1bq1SpUrp69ao2b96s06dP69///rejcwIAADdiV0GpWbOmpkyZ\nomnTpmnu3Lm20WPvTi/McVIAAADsHgclMjJS8+bNU2Zmpm7evKlixYrJx8dHXl52rwIAAMAudp2D\nYrVaFRcXp/79+8vHx0dlypTR4cOH1bZt23vuzwMAAPBb2VVQ5s+fr4ULF+qJJ56wTatSpYq6dOmi\n2bNna9myZQ4LCAAA3I9dx2dWr16tAQMGqGfPnrZpjzzyiGJjY+Xn56dly5apa9euDgsJAADci117\nUC5duqTq1avnO69mzZo6f/58oYYCAADuza6CEhQUpMTExHznJSUlqWzZsoUaCgAAuDe7DvF07txZ\nkydPVnZ2tlq2bKnAwEBdu3ZN8fHxWrZsmQYOHOjonAAAwI3YVVC6deumK1euaNGiRVq6dKmkO1f2\neHl5qXv37urevbtDQwIAAPdi9yAmsbGx6tWrlw4ePKjU1FT5+/urVq1aCggIcGQ+AADghn7VKGt+\nfn5q2LCho7IAAABIsvMkWQAAAGeioAAAAMOhoAAAAMOhoAAAAMOhoAAAAMOhoAAAAMOhoAAAAMOh\noAAAAMOhoAAAAMOhoAAAAMOhoAAAAMOhoAAAAMOhoAAAAMOhoAAAAMOhoAAAAMPxcubGLBaLxo4d\nq3Pnzik7O1t9+/ZVSEiIxowZI0kKDQ3VsGHD5OFBbwIAwJ05taCsX79eJUuW1OjRo5Wamqq//vWv\nql69umJiYhQREaHx48crPj5erVq1cmYsAABgME7dVdG6dWv97W9/s33v6empw4cPKzw8XJLUuHFj\nJSUlOTMSAAAwIKfuQfH19ZUkpaena8SIEYqJidHUqVNlMpls89PS0uxal9lsdkRCB6wTMCbHvIfc\nCZ8XrsJr9+ERFhZW4DynFhRJSklJ0dChQ9WlSxe1a9dO//73v23zMjIy5O/vb9d6fulBPbAdZwt/\nnYBBOeQ95E74vHAZXrvuwamHeK5cuaKBAwfqlVde0TPPPCNJql69uvbs2SNJSkhIUN26dZ0ZCQAA\nGJBT96DMnz9fN27c0Ny5czV37lxJ0muvvaZ//etfys7OVkhIiKKiopwZCQAAGJApNTXV6uoQRhEw\nj122rmDZ2tPVEdxS2oKtro7wu8bnhWskNc3gEI+bYMARAABgOBQUAABgOBQUAABgOBQUAABgOBQU\nAABgOBQUAABgOBQUAABgOE4f6h4AgAf11LvRro7gllwxbhJ7UAAAgOFQUAAAgOFQUAAAgOFQUAAA\ngOFQUAAAgOFQUAAAgOFQUAAAgOFQUAAAgOFQUAAAgOFQUAAAgOFQUAAAgOFQUAAAgOFQUAAAgOFQ\nUAAAgOFQUAAAgOFQUAAAgOFQUAAAgOFQUAAAgOFQUAAAgOFQUAAAgOFQUAAAgOE4vaAcPHhQMTEx\nkqTTp08rOjpa0dHRmjBhgnJzc50dBwAAGJBTC8rChQv13nvvKSsrS5I0efJkxcTEaPbs2bJarYqP\nj3dmHAAAYFBOLSgVK1bUxIkTbd8fPnxY4eHhkqTGjRsrKSnJmXEAAIBBeTlzY1FRUTp37pzte6vV\nKpPJJEny9fVVWlqa3esym82Fnk/ydcA6AWNyzHvInfB5AffhqM+LsLCwAuc5taD8Lw+P/9+Bk5GR\nIX9/f7t/9pce1APbcbbw1wkYlEPeQ+6Ezwu4EVd8Xrj0Kp7q1atrz549kqSEhATVrVvXlXEAAIBB\nuHQPyuDBgzVu3DhlZ2crJCREUVFRrowDAAAMwukFpUKFCpo7d64kKTg4WDNnznR2BAAAYHAM1AYA\nAAyHggIAAAyHggIAAAyHggIAAAyHggIAAAyHggIAAAyHggIAAAyHggIAAAyHggIAAAyHggIAAAyH\nggIAAAyHggIAAAyHggIAAAyHggIAAAyHggIAAAyHggIAAAyHggIAAAyHggIAAAyHggIAAAyHggIA\nAAyHggIAAAyHggIAAAyHggIAAAyHggIAAAyHggIAAAyHggIAAAyHggIAAAyHggIAAAzHy9UBcnNz\nNXHiRJnNZnl7e+vNN99UpUqVXB0LAAC4kMv3oMTHxysrK0tz587VgAEDNGXKFFdHAgAALubygrJv\n3z41atRIkvTEE0/ohx9+cHEiAADgai4/xJOeni4/Pz/b9x4eHrJYLPLycn601BeDnL5NSGkvbnV1\nBOBX4/PCNfi8cB8u34NSvHhxpaen2763Wq0uKScAAMA4XF5Q6tSpo4SEBEnSgQMHFBoa6uJEAADA\n1UypqalWVwa4exXP0aNHZbVaNXLkSFWpUsWVkQAAgIu5vKAAAAD8L5cf4gEAAPhfFBQAAGA4FBQA\nAGA4FBQAAGA4FBQAAGA4FBS4xNtvv+3qCAB+R7799lutXLlSZrNZt2/fdnUcOAFDtsIlsrKyZDab\nVblyZXl43OnJRYoUcXEqAEY0ffp0paSk6MSJE/Ly8tKCBQv07rvvujoWHIyCApc4deqUXn/9ddv3\nJpNJK1eudGEiAEa1b98+zZo1S/3791eHDh20YsUKV0eCE1BQ4BJLlixxdQQAvxM5OTm2wzo5OTm2\nva54uFFQ4BLbtm3TsmXLZLFYJEnXr1/X4sWLXZwKgBF1795dvXr1Umpqql588UX16NHD1ZHgBBQU\nuMScOXP0+uuva8WKFYqMjNSuXbtcHQmAQbVp00b169fXmTNnVKFCBQUEBLg6EpyAggKXKFmypJ58\n8kmtWLFCHTp00OrVq10dCYBBHTlyRP/5z3+UlZVlm8aVgA8/CgpcwtvbW8nJybJYLPrmm290+fJl\nV0cCYFCjR49W165dVbZsWVdHgRNxN2O4xMWLF3XixAk98sgjmjlzplq3bq22bdu6OhYAAxo0aJCm\nTp3q6hhwMgoKnOrkyZMFzgsODnZiEgC/F+PHj1eFChVUvXp1mUwmSVLDhg1dnAqOxiEeONWECRMK\nnDdjxgwnJgHwe5Gdna2TJ0/a/oFjMpkoKG6APSgAAMM7duyYjh8/ruDgYFWvXt3VceAEFBS4xLPP\nPmvbVStJfn5++vTTT12YCIBRff755/rqq69Uu3Ztfffdd2rTpo1eeOEFV8eCg3GIBy6xbNkySZLV\natXhw4e1efNmFycCYFRfffWVZs2aJS8vL1ksFr300ksUFDfAeMFwCW9vb3l7e6to0aKqU6eODh8+\n7OpIAAzMy8vL9v+7X+Phxm8ZLjFt2jTbIZ5Lly5xbw0ABapTp46GDx+uunXrat++fapTp46rI8EJ\nOAcFLrFmzRrb10WLFlWjRo3k5+fnwkQAjGzHjh06ceKEQkJC1KRJE1fHgRNQUOBUiYmJBc7jskEA\n+Tl37py2bNmizMxM27R+/fq5MBGcgUM8cKqvv/463+mMawCgIG+99ZYaNWqk0qVLuzoKnIiCAqca\nMWKEqyMA+J3x8fFRdHS0q2PAySgocKouXbrkGf9EunOpsclk0sqVK12UCoAR3R05tlSpUvrqq69U\no0YN2zxujfHw4xwUuNT169dVokSJe0oLAPTv37/Aedwa4+FHQYFLJCcn6/3331dubq5at26tcuXK\n6dlnn3V1LAAGde3aNZ05c0aVK1dWyZIlXR0HTsDgE3CJuLg4zZw5U6VLl1afPn20fPlyV0cCYFBf\nfPGF+vXrp4ULF+qll17S+vXrXR0JTsA5KHAJDw8P27+CihYtKl9fXxcnAmBUK1eu1OLFi1W0aFFl\nZmbqb3/7m9q3b+/qWHAw9qDAJSpWrKhp06bp+vXrWrBggcqVK+fqSAAMqlSpUvL09JR05x80HOJx\nD5yDApfYt2+f9u/fr5SUFH399deaOnWqHn/8cVfHAmBAAwcO1KVLl/Tkk0/qxx9/lMViUUhIiCTp\n3XffdXE6OAoFBS7Rp08fjRw5UlWrVtXZs2c1evRozZo1y9WxABhQcnJygfPCw8OdmATOxDkocAlP\nT09VrVpVkhQUFMTNAgEU6LHHHtPChQt1+fJlNW3aVNWqVVOlSpVcHQsOxl8FuET58uU1ffp0bd++\nXXFxcSpTpoyrIwEwqLFjxyooKEinTp1S6dKlOazjJigocIm3335bgYGB2rlzpwIDA/X222+7OhIA\ng7p+/bqeeeYZeXl56cknn5TVypkJ7oBDPHCJokWLqnv37q6OAeB34sSJE5KklJQU2xU9eLhxkiwA\nwNCOHj2qcePG6cSJE6pSpYqGDRuW5748eDhRUAAAgOFwiAcAYEjt27cv8O7n69atc1EqOAt7UAAA\ngOGwBwUAYGjHjh3ThAkTlJaWpnbt2qlq1apq1qyZq2PBwbjMGABgaP/61780cuRIBQQE6JlnntHs\n2bNdHQlOQEEBABje3ZFjAwMDVbx4cRengTNQUAAAhlaiRAmtWLFCmZmZ+vrrr+Xv7+/qSHACCgoA\nwNCqVaum8+fPKyAgQD/88IMCAwNdHQlOwEmyAABDWrVqlVatWmUboE2S9u3bJ4vF4tpgcAouMwYA\nGFJWVpYuX76s+fPn68UXX5QkeXh4KDAwUN7e3i5OB0ejoAAAAMPhHBQAAGA4FBQAAGA4FBQAAGA4\nFBQAAGA4FBQAAGA4/wcPuXg4gx5dJQAAAABJRU5ErkJggg==\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "draw_conditional_barplot('contact',df)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Cellphones seem to be more popular overall but also a bit more successful. Probably calls on landlines are missed more often." ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [], "source": [ "# Ensure dummy names are well readable\n", "df['contact'] = 'contact_' + df['contact'].astype(str)\n", "\n", "# Get dummies\n", "dummies = pd.get_dummies(df['contact'])\n", "\n", "# Add dummies to df\n", "df = pd.concat([df,dummies],axis=1)\n", "\n", "#remove original column\n", "del df['contact']" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Month of the call\n", "The month in which the call was made." ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [ { "data": { "image/png": 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00Ny5c1W+fHktX75cdrtdH3/8sZKSkjRr1izDlxmvW7dOFSpU0KJFixQdHa03\n33xT0dHRGjJkiBYtWiS73a64uLgCfSAAAOD8DN8HpWHDho7LjJOTk+Xp6amyZcvma2ft27dXu3bt\nHF+7urrq4MGDjhvBtWrVSj/++KNCQkLytV0AAHB3sSUlJdmNvnjr1q3atWuXkpOT5ePjo7/97W93\ndJfZK1euaOzYserRo4fmzJmjr776SpIUHx+v1atXO0753ApXDgFwRs235O8Pu7+KD0otpCSA+fz8\n/G66ztAIyqVLlzRq1Cjt379fbm5uqlChgpKSkvT++++rZcuWmjFjhkqVKmUozB9//KFx48apd+/e\n6tSpk959913HutTUVJUrV87Qdm71ocyUkJBg2Wy3Q3ZzOGt2Z80tmZx9y8kCb4LjXvzIXvwMzUF5\n++23deLECc2cOVNbtmzRV199pS1btigqKkp79uzR/PnzDe3s/PnzevHFFzV8+HA99thjkiR/f3/H\nnWi3bdumRo0a3eFHAQAAdwtDIyhbt27Viy++qDZt2jiWubi4KCQkRBcvXlRMTIxGjBhx2+289957\nSk5OVmxsrGJjYyVJ4eHheuutt5Senq7atWvnmKMCAABKJsOTZH18fPJcfu+99+rPP/80tI0xY8Zo\nzJgxuZYvWLDAaAwAAFACGDrF061bNy1ZskSpqTknZ2VkZOiTTz5R9+7diyQcAAAomQyNoLi7u+vY\nsWPq0aOHgoODVblyZV26dEnbt2/XmTNn5OXlpSlTpki6fofZiIiIoswMAADucoYKyoYNG+Tp6SlJ\njgmt2apUqaI9e/Y4vr7xYYIAAAB3wlBBWbVqVVHnAAAY0DgyrMDbSHl/U8GDAEWMh94AAADLoaAA\nAADLoaAAAADLoaAAAADLoaAAAADLMXwn2aysLB08eFB//vmnsrKycq1v3rx5oQYDAAAll6GCsn//\nfo0fP15nz56VJNntdknX73lit9tls9m0ffv2oksJAABKFEMFJTo6Wq6urpo8ebKqVKkiFxfODAEA\ngKJjqKAcOHBAU6dOVUhISFHnAQAAMDZJtnz58ipdunRRZwEAAJBksKB07dpVH3/8sTIzM4s6DwAA\ngPGnGe/du1c9e/ZU/fr1c42m8ARjAABQmAwVlDVr1sjLy0vS9fkof8UTjAEAQGHiacZAAXgvOVng\nbcQHFUIQALjLcL0wAACwnJuOoHTv3l1vvfWW/P391a1bt1uexrHZbPryyy+LJCAAACh5blpQmjdv\nLk9PT8ce23dUAAAWUUlEQVS/mWcCAACKy00LyuTJkx3/njJlSrGEAQAAkJiDAgAALIiCAgAALIeC\nAgAALIeCAgAALIeCAgAALMfQnWQl6ciRI9qxY4eSk5Nlt9tzrLPZbBo4cGChhwMAACWToYLy9ddf\nKyIiQllZWXmup6AAAIDCZKigxMbG6pFHHtGrr76q6tWrc9M2AABQpAwVlJMnTyo8PFy+vr5FnQcA\nAMDYJNnq1asrOTm5qLMAAABIMlhQBgwYoAULFujIkSNFnQcAAODmp3j++gTjc+fO6emnn5aXl5fK\nlCmT47U8zRgAABSmWz7NmMmwAADADDctKPl5gnFGRkahhAEAAJAMzkHp2bOnEhIS8ly3b98+derU\nyfAO9+7dqyFDhkiSDh48qK5du2rIkCEaMmSIvvnmG8PbAQAAd6+bjqB8/fXXjpGR06dP67vvvtOh\nQ4dyvS4+Pl6ZmZmGdvbBBx9o3bp1jjksBw8eVN++ffXMM8/cSXYAAHCXumlB2bdvnz7++GNJ1yfB\nLl68+KYb6d+/v6Gd1ahRQzNmzFBERISk6wUlMTFRcXFxqlmzpsLDw+Xp6Wk8PQAAuCvZkpKS7Hmt\nSEtL05kzZ2S329W7d29FRUXJ398/x2tcXFxUvnx5eXl5Gd7hqVOnNHHiRMXGxmr16tV68MEHFRAQ\noNjYWF2+fFkjR440tJ2bnXICilPzLWULvI34oNRCSAJnUdDvmYxNBR9x/nniogJvAygMfn5+N113\n0xGUUqVKqUaNGpKkL774Qvfcc4/c3Aw/W9CQtm3bqly5co5//+tf/zL83lt9KDMlJCSYks17yckC\nbyM+KNWyx/V2zDru2lLw4y5Z9/v5Vkw75oXA1OyF9D1TEGZ9dr5nzOGs2Q01jmrVquno0aPavHmz\n/vzzzzyfZjxo0KB873zEiBEaO3as6tevr/j4eNWtWzff2wAAAHcfQwVl/fr1ioiIyFVMst1pQRk/\nfrzefPNNubu7q1KlSnr55ZfzvQ0AAHD3Mfw04+bNm2vixImqUqVKgW7gdt999yk2NlaSVLdu3VtO\nvgUAACWTofugnDp1Sv369VPVqlW5uywAAChyhp9mfPHixaLOAgAAIMlgQenfv79iYmKUmJhY1HkA\nAACMzUFZu3atLly4oKeeekrlypWTh4dHjvU8zfju0DgyrEDvT3l/U+EEAQCUeIYKSpUqVVSlSpWi\nzgKUSBRDAMjNUEGZPHlyUecAAABwyNetYTdv3qxdu3bp8uXL8vb2VqNGjfT3v/+dK3sAAEChMlRQ\nrl27prFjx+qnn36Si4uLvL29lZSUpA8//FCNGzfW7NmzVapUqaLOCgAASghDBWXhwoXavXu3Xnvt\nNXXs2FGurq7KyMjQhg0bNHPmTMXExGjYsGFFnRVAISro85vigwopCADkwdBlxt98843CwsLUqVMn\nubq6SpLc3NzUpUsXhYWFacOGDUUaEgAAlCyGCkpSUpL8/f3zXOfn56dz584VaigAAFCyGSooNWrU\n0M8//5znul27dqlq1aqFGgoAAJRshuagPPHEE5o1a5ZKly6tjh07qlKlSjp//rw2bNigpUuXKiys\nYPdxAAAAuJHhgnLw4EHNmzdP8+fPdyy32+3q2rWrnnvuuSILCAAASh5DBcXFxUUTJ05U3759HfdB\nKV++vJo0aaLatWsXdUYAAFDC5OtGbQ888IAeeOCBosoCAAAgyWBBuXTpkubNm6fdu3fr8uXLudbz\nsEAAAFCYDBWU119/XZs3b1arVq1Ut27dos4EAABKOEMFJT4+XiNHjlSfPn2KOg8AAICx+6B4enrK\n19e3qLMAAABIMlhQnnrqKX3wwQdKSUkp6jwAAADGTvH06tVLq1evVvfu3VWzZk15eHjkWG+z2bRg\nwYIiCQjAmhpHFvwGjSnvbyp4EAB3JUMF5Y033lBiYqJq1aolT0/Pos4EAABKOEMFZfPmzRo+fLj6\n9etX1HkAAACMzUEpVaoUlxcDAIBiY2gEpWvXrlqxYoWaNGkiV1fXos7k1Ap6Xp5z8gAAGCwoHh4e\nio+PV48ePVS3bt1c81BsNpsiIiKKIh8AACiBDBWUtWvXqnz58pKkhISEXOttNlvhpgIAACWaoYKy\natWqos4BAADgYGiSLAAAQHGioAAAAMuhoAAAAMuhoAAAAMuhoAAAAMuhoAAAAMsp9oKyd+9eDRky\nRJJ0/PhxhYWFKSwsTG+88YaysrKKOw4AALCgYi0oH3zwgV5//XWlpaVJkqKjozVkyBAtWrRIdrtd\ncXFxxRkHAABYVLEWlBo1amjGjBmOrw8ePKgmTZpIklq1aqX4+PjijAMAACyqWAtKu3bt5Ob2/29e\na7fbHbfJL1u2rFJSUoozDgAAsChDt7ovKi4u/78fpaamqly5cobfm9czgaygcQHff+efq2wB91xw\nZv83MWf/znzcnTm7M+/beY978y0Fyx4fZP7PiYIge+Hz8/O76TpTC4q/v7927typpk2batu2bWrW\nrJnh997qQzmzO/5cW04WbpA7YOZ/k4SEBHP278zH3ZmzF5Bp3y+Scx/3QsjurD+7Tf2eKSBnzW5q\nQRk5cqSmT5+u9PR01a5dW+3atTMzDgAAsIhiLyj33XefYmNjJUm+vr5asGBBcUcAAAAWx43aAACA\n5VBQAACA5VBQAACA5VBQAACA5VBQAACA5Zh6mTEAoORoHBlW4G2kvL+p4EHgFBhBAQAAlkNBAQAA\nlkNBAQAAlkNBAQAAlkNBAQAAlkNBAQAAlkNBAQAAlkNBAQAAlkNBAQAAlkNBAQAAlkNBAQAAlkNB\nAQAAlkNBAQAAlkNBAQAAlkNBAQAAlkNBAQAAlkNBAQAAlkNBAQAAluNmdgAAAKyucWRYgbeR8v6m\nggcpQRhBAQAAlkNBAQAAlkNBAQAAlkNBAQAAlkNBAQAAlkNBAQAAlsNlxjfwXnKywNvIKIQcAIoW\nl4wC1scICgAAsBwKCgAAsBwKCgAAsBzmoAAAcBdz1jlXligo//znP+Xl5SVJuu+++zR58mSTEwEA\nADOZXlCuXbsmSZo/f77JSQAAgFWYPgclISFBV69e1YsvvqihQ4dqz549ZkcCAAAmM30ExcPDQ//8\n5z/Vo0cPHTt2TKNGjdKKFSvk5nbraAkJCUWQpmwRbDN/7vxzOXN2Z96/Mx93Z85eMI0LYRsl87g7\nc/aCMfd7pmCsnN3Pz++m60wvKPfff79q1Kghm80mX19fVahQQefPn1fVqlVv+b5bfag7tqXgN2or\nqDv+XM6cvRAkJCSYs39nPu7OnN0CSuRxd+bsFkD2/DH9FM+XX36p2bNnS5LOnj2rK1euqFKlSian\nAgAAZjJ9BKVHjx567bXXFBZ2/TKoSZMm3fb0DgAAuLuZ3gTc3d0VGRlpdgwAAGAhpp/iAQAA+CsK\nCgAAsBwKCgAAsBwKCgAAsBwKCgAAsBwKCgAAsBwKCgAAsBwKCgAAsBwKCgAAsBwKCgAAsBwKCgAA\nsBwKCgAAsBwKCgAAsBwKCgAAsBwKCgAAsBwKCgAAsBwKCgAAsBwKCgAAsBwKCgAAsBwKCgAAsBwK\nCgAAsBwKCgAAsBw3swMAhaFxZFiB3p/y/qbCCQIAKBSMoAAAAMuhoAAAAMuhoAAAAMuhoAAAAMth\nkiwA4K7nveRkgd6fUUg5YBwjKAAAwHIoKAAAwHIoKAAAwHIoKAAAwHKYJAsAgIWV1Am+FBQATqek\n/sAGShJO8QAAAMsxfQQlKytLM2bMUEJCgkqVKqVXX31VNWvWNDsWAAAwkekjKHFxcUpLS1NsbKxe\neOEFzZ492+xIAADAZKaPoPzyyy9q2bKlJOmRRx7RgQMHTE6E4lbQ+QQScwoA4G5jS0pKspsZIDIy\nUu3atVOrVq0kSd27d9fnn38uNzfTuxMAADCJ6ad4PD09deXKFcfXdrudcgIAQAlnekFp2LChtm3b\nJknas2eP6tSpY3IiAABgNtNP8WRfxXP48GHZ7XZNnjxZtWrVMjMSAAAwmekFBQAA4K9MP8UDAADw\nVxQUAABgORQUAABgORQUAABgOdxwpIDWr1+vTp06mR0DTuDzzz+/6brHH3+8GJPcmR9//FHLli1T\nWlqaY9m8efNMTJR/O3bsULNmzcyOUeJkZGQ45f2t9u/fr3r16jm+3rVrl5o0aWJiopLF+b5jLObz\nzz932oKya9euHF+7ubmpatWqqlq1qkmJjNu8ebP279+vwYMHa8SIEerbt68CAwPNjnVL586dy3O5\nzWYr5iR3ZtasWQoPD3eK74+bWbhwodMWlNGjR6tHjx4KDg6Wq6ur2XHypW/fvgoKClKPHj3k6+tr\ndpzb+vnnn3XkyBEtX75cffv2lSRlZmZq5cqV+uijj0xOZ8yFCxe0ZMkSHTt2TA888IAGDBig8uXL\nmx0rX7jMuIBCQ0OVlpYmX19fxy+ayMhIk1MZM2jQIJ0/f14BAQH6v//7P7m7u+vatWvq2bOn+vXr\nZ3a8W+rXr59mz54tHx8fpaSkaOTIkVq8eLHZsQz5/fffcy279957TUiSP6NGjVJ0dLTZMQpk8ODB\nKl++vHx9feXicv0M97Bhw0xOZczRo0f15Zdf6scff1RgYKB69Oih+++/3+xYhqSnp+v777/XmjVr\nlJaWpu7du1v6D7tff/1VGzdu1Jo1a9S9e3fZ7Xa5uLiobt26+vvf/252PEOGDh2q9u3bq0GDBtq9\ne7e2bt2qWbNmmR0rXxhBKaDhw4ebHeGOeXh4aNmyZSpdurTS0tI0fvx4zZw5U4MHD7Z8QXFzc5OP\nj48kycvLy/HLxhm88sorstlsstvtOnXqlGrWrKlFixaZHeu2KlasqKioKD300EOOMu4Mp6Zu1L17\nd7Mj3LFatWppxIgRunjxot566y09/fTTaty4sYYNG5bjNIQVubu7q3379qpUqZI++ugjxcbGWrqg\n1KlTR3Xq1FHPnj118eJF+fv7a9OmTfrb3/5mdrR86d27tyTJ399f3377rclp8o+CUkC1atXKNYzm\nLJKSklS6dGlJUqlSpXTp0iW5u7srKyvL5GS3V69ePU2cOFGPPPKI9u/fr4ceesjsSIbFxsY6/n35\n8mVFRUWZmMa4++67T5J0/vx5k5PcuU6dOmn//v3KyLj+/OuzZ8+anMi4bdu2ac2aNTp69Kg6d+6s\n8PBwZWRkaNSoUVq2bJnZ8W4pJiZG3377rR566CH9z//8j9PM43jrrbfUvHlz+fv769ixY4qIiHCa\nEXJfX1+tX79eTZs21cGDB1WhQgUlJiY61jkDCkoBvfrqq+rQoYO6d++u3bt3a8qUKU4zjNamTRuF\nhYWpXr162r9/v4KDg7Vy5UqneB7S2LFjFRcXp2PHjqlDhw4KDg42O9Id8fLy0okTJ8yOYYgzjz5k\nGz9+vNLT03X27FllZWWpcuXKevTRR82OZci6devUu3fvXL/cw8LCTEpkXLly5RQTEyMvLy+zo+TL\nmTNn1KtXL0nSs88+q6FDh5qcyLjExEQlJiZq1apVjmVvvPGGJOeZ3E5BKQTZ38DONow2cOBAtW7d\nWkePHtVjjz2mOnXq6OLFi47PY2Wpqak6cOCAzp07p5o1a+r48eOqWbOm2bEMCQ0NdZwiuXjxolq0\naGFyImOc9dTUjVJSUrRgwQJFRkZq7NixevHFF82OZNiUKVO0f/9+x+T2s2fP6tFHH1VISIjJyW6v\nbdu2ioyM1JEjR3T//fdr9OjRjhE5q0tMTJSvr6+OHz+uzMxMs+MYNm/ePKWkpOj06dOqXr26ypYt\na3akfKOgFJCvr6/WrVunZs2aOd0wWkxMjOPfiYmJ+u677/T888+bmMi4adOmqVWrVtq1a5cqVaqk\nyMhILViwwOxYt/TFF1+oZ8+eOX4w+/n5qVy5clq4cKECAwPVoEEDExPemrOemrpR9tUvV69elYeH\nR45Lpq3OmUd/pk+frl69eqlx48bauXOnIiMjNXfuXLNj3daYMWM0YcIEHTlyRNWrV3ea0zuStHHj\nRsXGxiozM1MdOnSQdP2PUmfiPDMLLSoxMVFffvmlJk+erGXLlun8+fN64403HENpVubj4yMfHx9V\nrFhRZ86cyfPqEqu6dOmSHnvsMbm5ualBgway261/MVr25bmBgYGO/z388MPy9fVVlSpVnOJ7Jpsz\nnZq6UUhIiBYvXiw/Pz+FhoY61SmHlJQUzZkzR/Xr19f777/vVOUqLS1NrVu3Vrly5dS2bVunGYk4\ndeqU0tLSFBQUpKysLB09etTsSIYtW7ZMsbGx8vb2VmhoqOLi4syOlG+MoBRQhw4dtGzZMsekOzc3\nN3366acmpzLmiSeeyPH1yJEjTUpyZ7J/WPzxxx9OcV+Ili1bSpK6deuW5/oqVaoUZ5x8c9ZTUzeq\nWrWqfvzxR6Wnp8vDw8Mpvm+yOfPoT0ZGhg4fPqwHH3xQhw8fNjuOYcuXL9fSpUtVtmxZXblyRcOG\nDVPnzp3NjmWIzWZTqVKlHP/28PAwOVH+UVAKaNWqVVqwYIFiY2PVvn17p7mJjyTHqSjp+k3EnGkE\nZezYsZo2bZqOHj2ql19+WePHjzc7UoG1atXK7Ah5cvZTUzeaM2eOXn75ZZUrV87sKPnmzKM/48aN\nU2RkpM6dO6fKlSvrlVdeMTuSIS4uLo65G56eno5f+M6gcePGmjhxos6cOaOoqCjLX4qeFwpKAXl7\ne6ty5cq6cuWKmjZtavl5EDe68ZRC6dKlneKeLj169HD8FW+32+Xt7a3z589r0qRJ+uSTT0xOd3e6\n8dTUX2VkZOiNN96w/GWu2R544AE1bdrU7Bh3xJlHfw4dOqTU1FS5urrq4sWLeumll/TFF1+YHeu2\nqlevrujoaDVu3Fg///yzatSoYXYkw5588kl99913ql27tlavXq0ZM2aYHSnfKCgF5OXlpU2bNslm\ns+mzzz5TUlKS2ZEM++vpqejoaMtfrrtixQrZ7XbNnDlTTzzxhOrXr6//+7//08qVK82Odtdy9lNT\nN2rdurVCQ0NVu3Ztx7JJkyaZmMg4Zx79Wbp0qd566y2ne0zCpEmT9Pnnn+unn35SrVq1nOKPuGyv\nvfaa+vfvr5UrV2rYsGGKjo52msuLs1FQCujVV1/ViRMnNHz4cH344YdOdarhr6enli9fbnak28oe\nYj158qTq168vSXrooYdynK5C8bLqqam8fPLJJ+rXr59TnR7J5syjP9WrV3ea2wDcyM3NTU8++aTZ\nMe5IZmamGjdurPfee0//+Mc/nPKPOApKAXl6ejruYjpq1CiT0+SPM5+e8vLy0vz581W/fn3t2bNH\n1apVMzsSnEClSpXUsWNHs2PcEWce/fHw8NDIkSPl7+/vOEXrLM9Aclbp6emKjo5Wo0aNtGPHDqe5\ncupGFJQSzJlPT02bNk1r1qzRtm3bVKtWLQ0ePNjsSHACpUuX1ogRI3I8T8hZflE68+iPM42y3S0m\nT56sn376SY899pji4uI0depUsyPlG08zLsGuXLmiEydOqFKlSvrwww8VHBzstEPIgBFr1qzJtexm\nc2usZvTo0U7zGA2gMFBQAMAJTJgwQampqU45+gPcCU7xAIATCAoKMjsCUKwYQQEAAJbDs3gAAIDl\nUFAAAIDlUFAAAIDlUFAAAIDlUFAAAIDl/D/pBkhFjdME9AAAAABJRU5ErkJggg==\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "draw_conditional_barplot('month',df)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This graph is quite interesting: In december, there seems to have been very little calling. And while the yes crowd signed up pretty equally through the year, the nay sayers are more unevenly spread. There seems to have been a bigger push in the summer, in which many calls where made, but the share of successful calls declined. Again, this one will be converted into dummy variables." ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [], "source": [ "# Ensure dummy names are well readable\n", "df['month'] = 'month_' + df['month'].astype(str)\n", "\n", "# Get dummies\n", "dummies = pd.get_dummies(df['month'])\n", "\n", "# Add dummies to df\n", "df = pd.concat([df,dummies],axis=1)\n", "\n", "#remove original column\n", "del df['month']" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Day of the call\n", "The week day the call was made." ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [ { "data": { "image/png": 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vXvXo0UMnT560WkAAAOB4DBWU2NhYFStWLMNxuXPn1v3797M0FAAAcGyGCkrF\nihW1ZcuWDMft2bNHFStWzNJQAADAsRk6SbZnz54aOXKkoqKi1KhRI5lMJkVERGj9+vXasGGDpk+f\nbu2cAADAgRgqKL6+vpo6dao++eQTHTx4UJI0f/58FSxYUKNHj1ZAQIBVQwIAAMdi+D4ozZo1U7Nm\nzXTx4kXduXNHrq6uKleunJycDB0lAgAAMMxwuzhw4IAWLFggd3d31axZU3FxcRo+fLiOHj1qzXwA\nAMABGb7MeOjQoTpz5oxlWI4cOXT37l298847ioiIsFpAAADgeAwVlM8++0xt27bVggULLMMqVqyo\nZcuWqVWrVlq0aJHVAgIAAMdjqKD89NNPatGiRYbjWrRoofPnz2dpKAAA4NgMFZT8+fPrwoULGY67\ndOmScufOnaWhAACAYzNUUPz9/RUcHKzdu3crJSVF0oOnG+/Zs0fBwcHy9/e3akgAAOBYDF1m3L9/\nf33//fcKDAxUtmzZlC9fPkVHRyslJUU1atTQwIEDrZ0TAAA4EEMFJVeuXFq8eLH++9//6tixY4qK\nipKrq6tq164tHx8f7oUCAACylKGCMmjQIHXp0kUNGjRQgwYNrJ0JAAA4OEO7Pk6dOiVnZ2drZwEA\nAJBksKDUr19f69atU1xcnLXzAAAAGDvE4+zsrG3btum7776Tm5ubChUqlGa8yWRScHCwVQICAADH\nY6igXLt2TbVq1bJ2FgAAAEkGC0pQUJC1cwAAAFgYKiipEhISdPr0aV2/fl316tXT/fv3Vbx4cWtl\nAwAADspwQfnyyy8VFBSku3fvymQyafny5QoODlZSUpJmz54tFxcXa+YEAAAOxNBVPJs3b9asWbPU\nrFkzzZ07V2azWZLUunVrnTx5UkuWLLFqSAAA4FgM7UFZtWqVXn/9dY0YMULJycmW4c2aNdP169e1\nZs0aDRo0yGohAQCAYzG0B+Xnn39Ww4YNMxxXpUoV3bx587EWeurUKfXr10+SdPbsWbVp00b9+vVT\nv379tG3btseaFwAA+OsxtAelUKFCOn/+vOrWrZtu3I8//pjuviiPsnLlSoWGhipXrlySHhSUzp07\nq0uXLobnAQAA/toM7UFp3ry5lixZoq1bt+r+/fuSHtyc7dSpUwoJCVGTJk0ML7B06dL64IMPLL+f\nPXtWe/fuVd++fTV16lTFxsY+5lsAAAB/NYb2oLz99ts6f/68Jk6cKJPJJEnq27ev4uPj5eHhob59\n+xpeYEBYaMCWAAAZF0lEQVRAgK5evWr5vXr16mrXrp2qVq2qkJAQLV26VEOGDDE0r3PnzhlerjG5\ns3h+z7esX79GsA0exjawPbaB7bENbM9a26BSpUqZjjN8q/u5c+fq4MGDioiI0J07d+Tq6ipPT0/5\n+PhYSsuT8PPzU968eS0/f/jhh4Zf+6g39kT2Xsna+T3nsnz9GsE2SINtYHtsA9tjG9ieLbbBY92o\nzdvbWzVr1lR0dLQKFCigHDlyPHWAwYMH691331X16tUVERGhKlWqPPU8AQDA881wQQkLC9OyZct0\n9uxZSQ/OQfHw8FD//v1Vs2bNJw4watQozZ49W87OzipcuLDGjBnzxPMCAAB/DYYKyvbt2zVu3DhV\nqlRJffr0UaFChXTz5k3t3LlT/fv31/z581W7dm3DCy1ZsqRCQkIkPbhMedmyZU+WHgAA/CUZKigh\nISEKCAjQjBkz0gzv3bu3Ro0apYULF3I3WQAAkGUMXWZ8+fJlvfzyyxmOa9++vf73v/9laSgAAODY\nDBWU8uXL68yZMxmOu3TpkkqVKpWloQAAgGMzdIjn3XffVWBgoCSpVatWKlq0qKKiohQWFqbg4GAF\nBgbqypX/vySLwgIAAJ6GoYLSp08fSVJwcLAWL15sGZ76VONJkyalmT48PDyL4gEAAEdkqKCMHz/e\n2jkAAAAsDBWUtm3bWjsHAACAhaGTZAEAAJ4lCgoAALA7FBQAAGB3KCgAAMDuUFAAAIDdMXQVj9ls\n1rp16xQWFqb79+8rJSUlzXiTyaTg4GCrBAQAAI7HUEEJCgrSihUrVLJkSRUrVkzZsmWzdi4AAODA\nDBWUTZs2qWPHjho+fLi18wAAABg7B+Xu3bvy8/OzchQAAIAHDBWUqlWr6uzZs9bOAgAAIMngIZ6h\nQ4dq9OjRypUrl2rWrCkXF5d00/AEYwAAkFUMFZRevXrJbDbr/fffl8lkynAanmAMAACyiqGCMnbs\nWGvnAAAAsOBpxgAAwO5kWlA2btyoRo0aqUCBAtq4ceMjZ2IymfT3v/89y8MBAADHlGlBmT59uipU\nqKACBQpo+vTpj5wJBQUAAGSlTAvK+vXrVaRIEcvPAAAAz0qmBcXNzS3DnwEAAKyNpxkDAAC7Q0EB\nAAB2h4ICAADsDgUFAADYHUMFJSwsLNNxP/30k/r06ZNlgQAAAAwVlDFjxqQrKcnJyVq2bJm6du2q\nn376yRrZAACAgzJUUNq0aZOmpJw5c0bdunXT4sWLFRAQoDVr1lg1JAAAcCyGnsUzZswY5cqVS2PG\njFFAQIC2b98uNzc3ffrpp/L29rZ2RgAA4GAMFRRJGjp0qHLlyqWQkBA1aNBAs2bNkrOzszWzAQAA\nB/XIhwX+kZubm6pWraqDBw/q888/V6FChSzjXn75ZeskBAAADueRDwt8lKCgIMvPJpOJggIAALLM\nIx8WCAAAYAuGHhYIAADwLBk+SfbatWsKCQnRgQMHdOPGDS1ZskTffvutKleurJYtW1ozIwAAcDCG\n7oNy8eJFvfnmm9q1a5eqV6+uxMRESdKdO3c0adIkfffdd1YNCQAAHIuhPSjz5s1TyZIltWjRImXP\nnl3btm2TJI0fP17x8fFatWqV/P39rRoUAAA4DkN7UI4cOaLu3bvLxcVFJpMpzbiXX35ZFy5csEo4\nAADgmAwVFJPJlK6YpLp//36m4wAAAJ6EoYJSu3ZthYSEKCYmxjLMZDIpOTlZa9eulYeHh9UCAgAA\nx2PoHJRBgwapd+/e+sc//iFPT0+ZTCatXLlSFy5c0NWrV7V48WJr5wQAAA7E0B6U8uXLa8WKFapb\nt66OHz8uJycnHTp0SGXLltXSpUtVuXJla+cEAAAOxPB9UEqXLq0pU6ZYMwsAAICkxygoknTq1Ckd\nOHBA169f11tvvaULFy6oSpUqKliwoLXyAQAAB2SooCQlJWnixInavn27nJycZDab1b59e/3zn//U\nTz/9pMWLF6tUqVLWzgoAAByEoXNQFi9erL1792r69Onavn27zGazJGnUqFFycXHRokWLrBoSAAA4\nFkMFZcuWLerXr5+aNm0qFxcXy/CyZcuqT58+OnTokNUCAgAAx2OooERFRalChQoZjitcuHCa+6MA\nAAA8LUMFpWzZstqzZ0+G4w4dOqQyZcpkaSgAAODYDJ0k26lTJ02bNk0JCQny9fWVyWTSTz/9pAMH\nDuhf//qXhg0bZu2cAADAgRgqKH//+98VFRWlpUuXauPGjTKbzZo4caKcnZ3VtWtXdejQwdo5AQCA\nAzF8H5TUInLixAnduXNHefPmVY0aNZQ/f35r5gMAAA7IUEEJDAxUo0aN5OPjo/r161s7EwAAcHCG\nCkpMTIzef/99JScnq1q1amrUqJEaNmyY6ZU9AAAAT8NQQVm4cKHu37+vgwcPav/+/frqq6+0cOFC\nubm5ydfXV76+vvL29rZ2VgAA4CAMn4OSK1cuNW7cWI0bN5YknThxQvPnz9eaNWv0xRdfKDw83Goh\nAQCAYzFcUGJiYnT06FEdOXJEhw8f1rlz5yRJ1apVk5eX12Mt9NSpU5o/f74WLVqky5cvW56SXKFC\nBQUGBsrJydDtWQAAwF+UoYLSrVs3nTt3TiaTSRUrVpSnp6f69OkjT09P5cmT57EWuHLlSoWGhipX\nrlySpI8//lj9+vWTl5eXZs6cqd27d8vf3//x3wkAAPjLMLSr4vLly0pJSVGFChXUsGFD+fr6qm7d\nuo9dTiSpdOnS+uCDDyy/nz17Vp6enpKkBg0aKCIi4rHnCQAA/loM7UHZsWOHzpw5o4iICEVERGjV\nqlWSpBo1asjLy0uenp6WkvFnAgICdPXqVcvvZrNZJpNJkpQ7d+7Heq5P6mGmrJM7i+f3fMv69WsE\n2+BhbAPbYxvYHtvA9qy1DSpVqpTpOEMFxcnJSTVq1FCNGjXUo0cPJSQk6Pjx41q7dq2WLFkik8n0\nxCfJPny+yb1795Q3b17Dr33UG3sie69k7fyec1m+fo1gG6TBNrA9toHtsQ1szxbbwPBJspJ07do1\nHTx40LIn5ebNmypVqpR8fHyeOEDlypV1+PBheXl5af/+/apTp84TzwsAAPw1GCoos2fPVkREhC5d\nuiQnJyfVqlVLnTt3VsOGDVWuXLmnCjBkyBDNmDFDiYmJKl++vAICAp5qfgAA4PlnqKBs375d9erV\nU58+fVS/fn25uro+1UJLliypkJAQSZK7u7uCg4Ofan4AAOCvxVBB2bp1q+VE1kdJTk6Wj4+Pli9f\nripVqjx1OAAA4JgMXWZspJykMpvNTxwGAABAMlhQAAAAniUKCgAAsDsUFAAAYHcoKAAAwO5QUAAA\ngN2hoAAAALtDQQEAAHYn04LSqVMnnT17VpK0efNmRUVF/enMTCaTPD09lTs3T4EEAABPLtOCcvny\nZUVHR0uSpk6dqqtXr/75zJycFBQUpLJly2ZdQgAA4HAyvdV9qVKlNHPmTNWsWVNms1mLFy9W/vz5\nM5zWZDJp0qRJ1soIAAAcTKYFZdSoUZo3b56OHz8uk8mkyMhIOTs7Zzjt49wKHwAA4M9kWlA8PT21\nYsUKSVLdunU1e/ZsVa9e/ZkFAwAAjsvQ04zXr1+vokWLSpJiY2MVExOj/Pnzy8XFxarhAACAYzJU\nUNzc3HT48GHNmzdPkZGRluEvvviiBg4cKG9vb6sFBAAAjsfQfVCOHTumQYMGKS4uTr169VJgYKB6\n9uyp2NhYDR06VMePH7d2TgAA4EAM7UEJDg6Wp6en5s2bp2zZslmG9+7dW4MHD9aSJUs0f/58q4UE\nAACOxdAelNOnT6tjx45pyon04L4nHTt21OnTp60SDgAAOCZDBcXV1VWJiYkZjktISMjSQAAAAIYK\nSs2aNbV8+XLdu3cvzfDY2FitWLFCHh4eVgkHAAAck6FzUAYOHKhu3bqpffv2atCggQoXLqybN29q\n//79SkxM1Pjx462dEwAAOBBDBaVMmTIKCQnRkiVLFB4erujoaOXLl0916tRR79699cILL1g7JwAA\ncCCGCooklS9fXjNmzHjkNGazWdOmTVOfPn1UokSJpw4HAAAck6FzUIxKSUnR5s2bFRUVlZWzBQAA\nDiZLC4r0YC8KAADA08jyggIAAPC0KCgAAMDuUFAAAIDdoaAAAAC7Q0EBAAB2h4ICAADsDgUFAADY\nHUMFpWfPnlq3bp1iYmIeOV22bNm0fv16VaxYMUvCAQAAx2SooBQqVEgffvihWrdurYkTJ+rQoUOZ\nTuvm5qbs2Q3fQR8AACAdQ03iww8/VFRUlL755htt3rxZAwcOVIkSJdS2bVu1bdtWbm5u1s4JAAAc\niOFdHQUKFFDHjh3VsWNHnT9/Xtu2bdP27dsVEhIiLy8vvfLKK/L395eTE6e1AACAp/NEbeLWrVu6\nffu27ty5I5PJpHv37mnChAl6/fXXFRkZmdUZAQCAgzG8B+XixYvavHmztm7dqmvXrql06dLq1KmT\n2rRpoyJFiujmzZsaMmSIJkyYoH//+9/WzAwAAP7iDBWUHj166Pvvv1fOnDkVEBCgl19+WbVr104z\nTeHChdW4cWOtXr3aKkEBAIDjMFRQzGazRo0apebNmytPnjyZTufn5ycfH58sCwcAAByToYKyfPly\nQzOrVKnS02QBAACQ9BjnoJw4cUKHDx9WYmKizGazJCklJUX379/XsWPHtHLlSquFBAAAjsVQQVmz\nZo0++ugjSzF5mJOTk+rVq5flwQAAgOMydJnx2rVrVb9+fW3btk1dunRR+/bttXv3bs2cOVM5c+ZU\ny5YtrZ0TAAA4EEMF5erVq3rttdeUL18+Va1aVceOHZOLi4sCAgLUvXt3/ec//7F2TgAA4EAMFRRn\nZ2flzJlTklSmTBldvnxZSUlJkqRatWrp0qVL1ksIAAAcjqGCUqlSJYWFhUmS3N3dZTabdeLECUnS\ntWvXrJcOAAA4JEMnyXbu3FmjRo3SnTt3NHHiRDVq1EgTJ06Un5+ftm3bJg8PD2vnBAAADsTQHhQ/\nPz/NmTNHFStWlCSNGTNG5cqV04YNG1S+fHmNHDnSqiEBAIBjMXwflIYNG6phw4aSHjzZ+NNPP7Va\nKAAA4NgyLSi//vrrY82oRIkSTx0GAABAekRBadeunUwmk+EZhYeHZ0kgAACATAvKmDFjLAXl7t27\nCgoKUp06dRQQEKAiRYooKipKYWFh2r9/v4YMGfLMAgMAgL++TAtK+/btLT8HBgaqTZs2eu+999JM\n06ZNG3344YfatWuX/vGPf1gvJQAAcCiGruIJDw9XkyZNMhzn6+urY8eOZWkoAADg2AwVlAIFCujU\nqVMZjouIiFDRokWzNBQAAHBshi4zbteunUJCQnTv3j01bNhQBQoU0K1bt7Rjxw6tW7dOQ4cOtXZO\nAADgQAwVlJ49e+ru3bv697//rc8//1ySZDablTNnTvXt21cdO3a0akgAAOBYDBUUk8mkoUOHqnfv\n3jp58qSio6NVoEAB1axZU7ly5bJ2RgAA4GAM30lWklxdXVW/fn2rBHnzzTfl6uoqSSpZsqQmTJhg\nleUAAAD791gFxVri4+MlSYsWLbJxEgAAYA/soqCcO3dOcXFxGjRokJKSkjRgwAD97W9/M/S6rJU7\ni+f3fMv69WsE2+BhbAPbYxvYHtvA9qy1DSpVqpTpOLsoKC4uLnrzzTfVrl07Xbp0SUOHDtUXX3yh\n7NkfHe9Rb+yJ7L2StfN7zmX5+jWCbZAG28D22Aa2xzawPVtsA7soKGXLllXp0qVlMpnk7u6u/Pnz\n6+bNmypevLitowEAABswdKM2a9u4caPmzZsnSbp+/bpiY2NVuHBhG6cCAAC2Yhd7UNq1a6fJkyer\nT58+kqTx48f/6eEdAADw12UXLcDZ2VnTpk2zdQwAAGAn7OIQDwAAwMMoKAAAwO5QUAAAgN2hoAAA\nALtDQQEAAHaHggIAAOwOBQUAANgdCgoAALA7FBQAAGB3KCgAAMDuUFAAAIDdoaAAAAC7Q0EBAAB2\nh4ICAADsDgUFAADYHQoKAACwOxQUAABgdygoAADA7lBQAACA3aGgAAAAu0NBAQAAdoeCAgAA7A4F\nBQAA2B0KCgAAsDsUFAAAYHcoKAAAwO5QUAAAgN2hoAAAALtDQQEAAHaHggIAAOwOBQUAANgdCgoA\nALA7FBQAAGB3KCgAAMDuUFAAAIDdoaAAAAC7Q0EBAAB2h4ICAADsDgUFAADYHQoKAACwOxQUAABg\ndygoAADA7lBQAACA3aGgAAAAu0NBAQAAdoeCAgAA7A4FBQAA2B0KCgAAsDsUFAAAYHcoKAAAwO5Q\nUAAAgN2hoAAAALtDQQEAAHaHggIAAOwOBQUAANgdCgoAALA7FBQAAGB3KCgAAMDuUFAAAIDdyW7r\nACkpKfrggw907tw55ciRQ2PHjlWZMmVsHQsAANiQzfeg7N69WwkJCQoJCdHAgQM1b948W0cCAAA2\nZoqKijLbMsDcuXNVvXp1NW/eXJLUpk0bbd682ZaRAACAjdl8D0psbKxcXV0tvzs5OSkpKcmGiQAA\ngK3ZvKDkyZNHsbGxlt/NZrOyZ7f5qTEAAMCGbF5QatWqpf3790uSTp48qQoVKtg4EQAAsDWbn4OS\nehXPDz/8ILPZrAkTJqhcuXK2jAQAAGzM5gUFAADgj2x+iAcAAOCPKCgAAMDuUFAAAIDdoaAAAAC7\nQ0EBAAB2hzui2djs2bM1cuRI9ezZUyaTSdKDm9WZTCYtW7bMxumAZ+vIkSPphnl6etogieNKTk7W\npk2b9Ntvv8nLy0sVKlRQgQIFbB3LIUyZMsXyOfBH48ePf8ZpbI+CYmM9e/aUJE2YMEE5c+a0cRrH\ntnnzZq1YsUIJCQmWkrh+/Xpbx3IoX331laQHJf3HH3+Um5sbBeUZmzlzpooWLaoDBw6oatWqmjRp\nkj7++GNbx3IIzZo1kyR9+eWXqlmzpmrVqqUzZ87o9OnTNk5mGxQUGytcuLAkafr06VqyZImN0zi2\nlStXas6cOSpevLitozisadOmWX5OTEzUmDFjbJjGMV25ckXjxo3TsWPH5OvrqxUrVtg6ksOoX7++\nJOlf//qXunXrJunB3dbfeecdW8ayGQqKjcXExMjV1VUuLi766KOP5O7uLienB6cGvfLKKzZO51hK\nlSqlMmXK2DoGfpecnKyrV6/aOobDSUpKUlRUlKQHD3PN7JADrOfevXuKiIhQtWrVdOLECSUmJto6\nkk1QUGxs+PDhWrx4sUqWLKl8+fLp9u3bto7ksFxcXDRkyBBVrlzZ8p/ygAEDbJzKsbRq1Uomk0lm\ns1nJycnq2LGjrSM5nP79+6t37966efOmevbsqeHDh9s6ksMZP368Fi1apA8//FDlypXT9OnTbR3J\nJrjVvY0NGjRI0dHRunz5cppnEHGS7LO3adOmdMPatm1rgySA7d2+fVsFChRgD4qNXLx4UT///LMq\nVqyoYsWKOeR2oKDYWEpKiq5fv673339fgYGBaca5ubnZKJVjSkpK0rp163ThwgWVLVtW//jHP+Ts\n7GzrWA5l7969+vLLLxUXF2cZFhQUZMNEjqd///7phrENnq01a9Zo165dio6OVtu2bXX58mWNHDnS\n1rGeOQoK8LupU6cqb9688vDw0JEjR3Tnzh1NnjzZ1rEcSteuXTVs2DDLyeOS5O7ubsNEjufixYuS\nHlxJdfbsWUVGRmrw4ME2TuVY+vTpo8WLF2vAgAEKCgpS9+7dHfJkZc5BAX53+fJlLV68WJLk5+en\nXr162TiR48mXLx+XFdvYw4WwXLly2rhxow3TOKaUlJQ0v+fIkcNGSWyLggL8LiEhQXFxcXJxcVFc\nXFy6/yRgPevWrZP04D/iGTNmqEqVKpZj7lzN9mylbgtJun79uu7fv2/DNI6pefPmevvtt/XLL79o\n6NChaty4sa0j2QQFBfjdG2+8oS5duuiFF17QhQsX1LdvX1tHchg3btyQJIWHh6tXr166deuWJCk+\nPt6WsRzSrFmzLHsPc+bMqVKlStk4kePZvHmzSpcurddee03ly5dXxYoVbR3JJigowO9y586tcuXK\n6d69eypRooS2bNmi5s2b2zqWQyhWrJg2bNggFxcX7d+/X9KDcyCSkpI0cOBAG6dzDBs2bMh0G+DZ\nWrlypS5cuKCwsDD95z//UaFChTRr1ixbx3rmOEkW+N2rr76q9957T66urpZhlStXtmEix5GQkKCb\nN2/qs88+U48ePSRJTk5OKliwoMMef3/WEhISdOPGDS1fvpxtYGORkZE6ePCgDhw4oPv378vT09Mh\n78lEQQF+FxgY6JDfUgDYF39/f5UqVUr9+/eXj4+PrePYDAUF+N2mTZv01VdfqXz58pZhjvgEUQC2\nlZSUpOPHjys8PFynT59WoUKF0jynylFwDgrwuzVr1qhr165pDvEAwLMWExOj69ev65dfflFcXJxK\nlChh60g2QUEBfle4cGHL484BwFYGDx6sxo0bq0ePHqpQoYKt49gMh3iA340ePVr37t3Tiy++yMMC\nAcDG2IMC/K5hw4a2jgAA+B17UAAAgN1xsnUAAACAP6KgAAAAu0NBAQAAdoeCAgAA7M7/ATLa9quo\nN7E/AAAAAElFTkSuQmCC\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "draw_conditional_barplot('day_of_week',df)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Interestingly, this bank does not seem to call people on the weekend. There also seem to be a bit fewer calls on fridays. Thursday seems to be the best day with the most total calls made and the most customers signed on." ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [], "source": [ "# Add job_ to every value in jobs so that the dummies have readable names\n", "df['day_of_week'] = 'day_of_week_' + df['day_of_week'].astype(str)\n", "\n", "# Get dummies\n", "dummies = pd.get_dummies(df['day_of_week'])\n", "\n", "# Add dummies to df\n", "df = pd.concat([df,dummies],axis=1)\n", "\n", "#remove original column\n", "del df['day_of_week']" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Previous outcome\n", "The outcome of previous calls to the customer." ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [ { "data": { "image/png": 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dO6bq1aurQoUKDowIAABcjV0FJSUlRQMHDtSRI0fk7++va9euKS0tTZs2bdLU\nqVO1YMECVa1a1dFZAQCAi7DrNuM5c+YoMTFRS5cu1bp162SxWCTdnTwbGBjISrAAACBP2VVQvvrq\nK/Xt21fVq1e3uYunSJEiCg0N1dGjRx0WEAAAuB67CkpqaqpKliyZ6z5vb2+lp6fnaSgAAODa7Coo\nlStXVnR0dK77YmJiVLly5TwNBQAAXJtdk2TDwsI0dOhQJSUl6bnnnpPJZNLBgwe1du1arVu3Tu+9\n956jcwIAABdi1whK48aNNWHCBCUkJOiDDz6QxWLR7NmztWvXLg0fPlzNmzd3dE4AAOBC7F4HpWXL\nlmrZsqUSEhKUnJwsHx8fVahQgXc0BgAAec7ugiJJP/74ow4dOqSbN2+qePHiKlSokMqUKeOobAAA\nwEXZVVAyMjI0ZswY7dq1y7oGiiS5ubmpU6dOGjp06H3fRBAAAOBB2VVQZs+erb179+rvf/+7mjVr\npuLFi+v69evavn275s+fr5IlS6pnz54OjgoAAFyFXQVl27Zt6tu3r1599VXrtlKlSqlbt27Kzs7W\nv/71LwoKAADIM3bNcL19+7YqVqyY674qVaooKSkpT0MBAADXZldBadasmVavXq3s7Owc+zZt2qQm\nTZrkeTAAAOC67LrE88QTT2jRokV69dVX1apVK/n7+ys5OVkxMTE6ceKEunbtan3DQJPJpMjISIeG\nBgAAjza7CsqMGTMkSbdu3VJUVFSO/atWrbJ+TEEBAAAPy66CcuDAAUfnAAAAsGIZWAAAYDgUFAAA\nYDgUFAAAYDgUFAAAYDgUFAAAYDgP9G7GN27cUHp6us0bBt4TEBCQZ6EAAIBrs6ugJCYmasyYMTpx\n4sR9nxMbG5tnoQAAgGuzq6BMnz5diYmJioiIUMmSJWUymRydCwAAuDC7CkpcXJxGjBih1q1bOzoP\nAACAfZNkvby8VKxYMUdnAQAAkGRnQWnRooWio6MdnQUAAECSnZd4qlSporlz5yo8PFxPPfWUvLy8\nbPbzBoEAACAv2VVQpkyZIkk6evSojh49mmM/BQUAAOQl3s0YAAAYDivJAgAAw7nvCEpkZKRGjBih\nChUq/O7lG5PJpAULFuR5OAAA4JruW1Dc3d2tH7u5ubE4GwAAyDf3LSjz5s2zfjx//vx8CQMAACAx\nBwUAABgQBQUAABgOBQUAABgOBQUAABgOBQUAABiOXSvJSlJ2drZ27Nih2NhYXb16VYMHD9axY8dU\nvXp1VaiIIKFNAAAXpElEQVRQwYERAQCAq7GroKSkpGjgwIE6cuSI/P39de3aNaWlpWnTpk2aOnWq\nFixYoKpVqzo6KwAAcBF2XeKZM2eOEhMTtXTpUq1bt04Wi0WSNGnSJAUGBrKKLAAAyFN2FZSvvvpK\nffv2VfXq1W1WlC1SpIhCQ0NzfYdjAACAP8qugpKamqqSJUvmus/b21vp6el5GgoAALg2uwpK5cqV\nFR0dneu+mJgYVa5cOU9DAQAA12bXJNmwsDANHTpUSUlJeu6552QymXTw4EGtXbtW69at03vvvefo\nnAAAwIXYNYLSuHFjTZgwQQkJCfrggw9ksVg0e/Zs7dq1S8OHD1fz5s0dnRMAALgQu9dBadmypVq2\nbKmEhAQlJyfLx8dHFSpUkJsba70BAIC8ZXdBuScwMFCBgYHWx9nZ2ZJEUQEAAHnGroJy/vx5TZky\nRUeOHMn1jh2TyaT9+/fneTgAAOCa7CooEydO1IkTJ9S+fXv5+vo6OhMAAHBxdhWUEydOaMiQIWrf\nvr2j8wAAANh3F0+JEiVUtGjRPDng9evX1b59e505c0aJiYmKiIhQRESE3n//fet8FgAA4NrsKiih\noaFauHChzp49+1AHM5vNmjx5sjw9PSVJ//znP9WnTx9FRUXJYrFo9+7dD/X6AADg0WDXJZ5nnnlG\ny5cvV5cuXVSkSBF5eXnZ7DeZTFq/fv3vvs7MmTPVuXNnffTRR5KkkydPqm7dupKkRo0a6cCBA2rW\nrNmDfg0AAOARY1dBmTBhgs6dO6eGDRuqePHif+hAGzZskJ+fn5555hlrQbFYLNY3H/T29lZKSord\nrxcfH/+HcuB/OId/Rt7ODgAH4Gfxz4vv3cOpUqXKfffZVVAOHTqkYcOGqWPHjn84xPr1661L5J86\ndUpjx47VjRs3rPvT0tJUpEgRu1/vt74o/L74+HjO4Z/RnnPOTgAH4Gfxz4nfo45lV0EpWrTofd/N\n2F4LFy60ftynTx8NHz5cH374oeLi4hQSEqJ9+/apXr16D3UMAADwaLBrkmzXrl310Ucf6datW3l6\n8LfeeksLFy5UWFiYzGYz7+kDAAAk2TmCkpiYqO+//15t27ZVuXLlVLhwYZv9JpNJCxYssPug8+fP\nt378IJ8HAABcg10F5dy5c6pWrZqjswAAAEiys6DMmzfP0TkAAACsHujdjH/44QfFxcXp1q1bKlas\nmGrVqqWgoCBHZQMAAC7KroKSnZ2tSZMmacOGDbJYLNbtJpNJL7zwgsaOHWtdzwQAAOBh2VVQVqxY\noY0bNyoyMlJt2rRRiRIldPXqVW3atEmLFy9WlSpV9Le//c3RWQEAgIuwq6CsX79ePXr0UFhYmHVb\n2bJl1atXL2VmZmr9+vUUFAAAkGfsWgfl8uXL1vfM+bW6devqwoULeRoKAAC4NrsKSpkyZe77fgOn\nTp1SsWLF8jQUAABwbXYVlBdeeEGLFi3Sli1bZDabJUlms1mbN2/W4sWL9fzzzzs0JAAAcC12zUHp\n3r27Dh8+rNGjR2vs2LHy9fVVcnKysrOzFRISot69ezs6JwAAcCF2FRQPDw/Nnj1b+/fvV1xcnG7e\nvKmiRYuqbt26atSokaMzAgAAF2P3Qm3Xrl3T7du31b9/f0nS2bNntXPnTlWvXl1+fn4OCwgAAFyP\nXXNQ4uPj9dprr+nDDz+0brt8+bIWL16s7t276+zZsw4LCAAAXI9dBWXWrFkqV66cli5dat1Wt25d\nbdiwQaVLl7YpLgAAAA/LroJy/Phx9erVK8elnCJFiqhHjx46fPiwQ8IBAADXZFdBcXNzU0pKSq77\nMjIylJWVlaehAACAa7OroISEhGjx4sW6du2azfZr165pyZIlCgkJcUg4AADgmuy6i6d///7q2bOn\nOnXqpBo1aqhYsWK6ceOGjh8/Li8vL02aNMnROQEAgAuxawTl8ccf12effaauXbvqzp07+v7775We\nnq7OnTtrxYoVKl++vKNzAgAAF2LXCMrFixfl7++vAQMG5NiXkZGhI0eOKDg4OM/DAQAA12TXCMpL\nL72kU6dO5brv+PHj6tevX56GAgAAru2+IygzZ87UzZs3JUkWi0WLFi3K9V2Lv//+e/n4+DguIQAA\ncDn3LSjly5fXokWLJEkmk0knTpxQgQK2T3d3d1eRIkX09ttvOzYlAABwKfctKC+99JJeeuklSVLH\njh01depUVa1aNd+CAQAA12XXJNl169Y5OgcAAICVXQVlwoQJv/ucd99996HDAAAASHYWlAMHDuTY\nlp6erpSUFPn6+uqJJ57I82AAAMB12VVQNmzYkOv2H374Qe+88446duyYp6EAAIBrs2sdlPsJCgpS\nRESEoqKi8ioPAADAwxUUSfLx8dH58+fzIgsAAIAkOy/xnDt3Lse2rKwsXb58WfPmzVOFChXyOhcA\nAHBhdhWUzp07y2Qy5dhusVjk5eWlKVOm5HkwAADguuwqKLndQmwymVS4cGHVq1ePpe4BAECesqug\ntG/f3tE5AAAArOwqKJJ0/fp1rVixQnFxcUpJSZGfn59q166t119/Xf7+/o7MCAAAXIxdd/FcunRJ\n3bt315o1a1S4cGFVr15dHh4eWrVqlbp3765Lly45OicAAHAhdo2gzJo1SwULFtSqVasUEBBg3X7u\n3DkNGDBAc+fO1bhx4xwWEgAAuBa7RlBiY2PVu3dvm3IiSQEBAYqIiMh1KXwAAIA/yq6Ckp2dLT8/\nv1z3+fr6KjU1NU9DAQAA12ZXQalSpYo2btyY677o6GgFBQXlaSgAAODa7JqD0qtXLw0YMEDJyclq\n1aqVSpQooWvXrmnr1q06ePCgJk+e7OicAADAhdhVUBo0aKCxY8dq1qxZmjhxonV7iRIlNGrUKDVr\n1sxhAQEAgOuxex2UNm3aqHXr1kpISNDNmzdVtGhRlS9fPtcl8AEAAB6G3QVFkvbt26dDhw7p5s2b\nKlasmBo2bKi6des6KhsAAHBRdhWU5ORk/f3vf9eJEydUoEAB+fr6KikpScuXL9czzzyjKVOmyMPD\nw9FZAQCAi7DrLp7p06fr7Nmz+sc//qE9e/YoOjpae/bs0eTJk3X06FHNnz/f0TkBAIALsaug7N27\nV/3791eTJk2sc07c3NzUrFkzvfnmm9q8ebNDQwIAANdiV0GRpOLFi+e6vXTp0kpPT8+zQAAAAHYV\nlPbt22vp0qVKS0uz2W42m7V69Wq9+OKLDgkHAABck12TZAsWLKiff/5ZHTt2VOPGjeXv76/k5GTF\nxsbq8uXL8vHx0ZgxYyRJJpNJY8eOdWRmAADwiLOroGzdulWFCxeWJMXFxdnsK1mypI4ePWp9zLoo\nAADgYdlVUNatW+foHAAAAFZ2T5IFAADILxQUAABgOBQUAABgOBQUAABgOBQUAABgOBQUAABgOBQU\nAABgOBQUAABgOBQUAABgOBQUAABgOHYtdZ9XzGazJkyYoPPnzyszM1NhYWGqWLGixo8fL0kKCgrS\nsGHD5OZGbwIAwJXla0HZtGmTfH19NW7cOCUlJal79+6qWrWq+vTpo5CQEE2ePFm7d+9Ws2bN8jMW\nAAAwmHwdqmjRooV69+5tfez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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "draw_conditional_barplot('poutcome',df)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Perhaps unsurprisingly, customers that said yes before are more likely to say yes again. Interestingly, customers that said no before, are also a little bit more likely to say yes this time. Customers that where not contacted before seem to say no most of the time." ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [], "source": [ "# Add job_ to every value in jobs so that the dummies have readable names\n", "df['poutcome'] = 'poutcome_' + df['poutcome'].astype(str)\n", "\n", "# Get dummies\n", "dummies = pd.get_dummies(df['poutcome'])\n", "\n", "# Add dummies to df\n", "df = pd.concat([df,dummies],axis=1)\n", "\n", "#remove original column\n", "del df['poutcome']" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Duration of the call\n", "As any sales person knows, how long the customer is willing to stay on the phone greatly influences the likely hood of success." ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [ { "data": { "image/png": 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J+qK4qD+KR6n3xYCHeGTgoha4szW+Awxzaz8eEREpDQooORRL2niy+LaNhnhE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\nDKNea8VyODj7jRcYufelArUwsw8l9rL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GPJQZRmLZjD+U0VCgEusNI7EuPunW26JaPoGl\nDHRzW5SRDHmtA59uDn7JMNi83BwSzZZjnjju8QSvUTSva1WerKPR5zOxLjW2rEv2HVx5O9eVZcm2\nG4l2q1avhoFOHo9WZYnjpVvV44r5Nsq0Bu20fF/Hadmnq/fHadkvtI7/TDZv7ziJV41KLifKWh//\nxLy+ql1tljXvY/PP1dX70Hq7a6A9MVQwdE11U0V7+/T6vWydBpRQKITX2zKOiGmaxGIxLMsiFArh\n87Ukn+zsbEKh0BXrs7OzCQaDXaqbCu4vd+P98F87rKM9mey79T7+OrCAk0MnsnDiH3updaK7KOAW\n3cQtdhP38b8A1OPipOHnpNGHi8qDhYNfR7jVaeBW3cAA3YiFvvILJT5sFMagEYuwMglj0qRMAiqD\nOuWhTmVSpzKoVRn8qjJxaRtXInBYOn62xUc8LNgoYhhEMQgpRRRXcln39JtCVmLqRZYTwxttxBdp\nwBdtwBdtxBuNz/sT6/pEQvgiIfyRBvzREP5ICF9jLZl2GLcT690G/w45gD/VjRBAvC9yUt2IToT/\n8VEa/ry4V79npwHF6/XS0NCQXHYcBysxSuvVZQ0NDfh8vuT6jIwMGhoa8Pv9XarbmfZOB92Qx2fH\np078U2LqyJP9AfrEF/L/fIMNE0IIIVIrBX/HdH4ZPz8/n4qK+Ai9x44dY8SIlsHOxo4dS1VVFeFw\nmGAwyMmTJxkxYgT5+fkcOHAAgIqKCsaPH9+lukIIIYS4uV3zp3h++ukntNasWLGCiooKBg8ezOTJ\nk9myZQubN29Ga828efOYNm0adXV1lJaW0tDQQE5ODu+99x5ZWVldqiuEEEKIm1enAUUIIYQQorfJ\ngB5CCCGESDsSUIQQQgiRdiSgCCGEECLtyFg83aSzIQFE9/v6669Zs2YNGzdu5JdffmHRokUopRg+\nfDhvv/02hmHw/vvvs3//fizLYsmSJYwdO7bduuL6RKNRlixZwrlz54hEIixYsIDbb79d+iMFbNtm\n6dKlnD59GtM0WblyJVpr6YsUunDhAkVFRXz44YdYliV90RVadIvdu3fr0tJSrbXWR48e1fPnz09x\ni37fNmzYoGfMmKGLi4u11lrPmzdPV1ZWaq21XrZsmd6zZ48+fvy4Likp0Y7j6HPnzumioqJ264rr\nt23bNl1eXq611joQCOiHHnpI+iNFPv/8c71o0SKttdaVlZV6/vz50hcpFIlE9EsvvaSnTp2qT5w4\nIX3RRTdZHOs5HQ0JILrf4MGDWbduXXL5u+++o6AgPuxAYWEhhw4doqqqigceeAClFAMHDsS2bQKB\nQJt1xfWbPn06r732WnLZNE3pjxSZMmUKy5cvB6CmpoZ+/fpJX6TQ6tWreeqpp8jLywPkfaqrJKB0\nk/aGBBA9Y9q0acknGgNorVGJR8K3Hl6hdZ80r2+rrrh+2dnZeL1eQqEQr776Kq+//rr0RwpZlkVp\naSnLly9n2rRp0hcpsmPHDnJzc5N/uIK8T3WVBJRu0tGQAKLntb4229nwCm3VFTfm/PnzPPvss8ya\nNYuZM2dKf6TY6tWr2b17N8uWLSMcDifXS1/0nu3bt3Po0CFKSkr44YcfKC0tJRAIJMulLzonAaWb\ndDQkgOh5o0aN4vDhw0B8yIQJEyaQn5/PF198geM41NTU4DgOubm5bdYV16+uro45c+bw5ptvMnt2\nfDwr6Y/U+OSTT1i/fj0AmZmZKKUYPXq09EUKbNq0iY8++oiNGzdy5513snr1agoLC6UvukCeJNtN\n2hoSYNiwYalu1u/a2bNneeONN9iyZQunT59m2bJlRKNRhg4dSnl5OaZpsm7dOioqKnAch8WLFzNh\nwoR264rrU15ezq5duxg6dGhy3VtvvUV5ebn0Ry9rbGxk8eLF1NXVEYvFmDt3LsOGDZPfjRQrKSmh\nrKwMwzCkL7pAAooQQggh0o5c4hFCCCFE2pGAIoQQQoi0IwFFCCGEEGlHAooQQggh0o4EFCGEEEKk\nHQkoQoges2jRInbs2HHDX6ekpCQ5P2vWrBv+ekKI9CcBRQiR9o4cOZKc//TTT1PYEiFEb5FnsQsh\nuo3WmlWrVrF//37y8vKwbZuCggIeeeQR9u3bB5Ac5PGVV15h0qRJjB49mtraWrZt28Y777zDzz//\nTF1dHSNHjmTt2rWsWbMGgOLiYrZu3crIkSOprq7m8uXLLF26lOrqapRSvPDCCzzxxBPs2LGDgwcP\nUl9fz6+//sr9999PWVlZqg6JEOI6SUARQnSb3bt38/3337Nz506CwSCPP/54h/UvXrzI3Llzuffe\ne/nqq69wuVxs3rwZx3F47rnnOHDgAEuXLmXjxo1s3br1im3XrVtHTk4OO3fuJBAIUFxczB133AHA\n0aNH2blzJ6ZpMn36dJ5++mlGjhzZY/sthOh+ElCEEN3myJEjTJ06FZfLRW5uLoWFhZ1uc/fddwMw\nceJE+vbty6ZNmzh16hRnzpyhsbGx3e0qKytZsWIFALm5uUyePJkjR47g9XoZN25ccoTYQYMGUV9f\n3w17J4ToTXIPihCi2yilaD16hmVZ1NTUXLEuFotdsU1GRgYAe/fuZeHChWRkZFBUVMTEiRPpaCSO\nq8u01ti2DYDH42m3TUKI3wYJKEKIbnPfffexa9cuIpEI9fX1HDx4EJ/Px6VLlwgEAkQiEQ4ePNjm\ntl9++SWPPvooTz75JH6/n8OHDycDh2ma/y/YTJo0iW3btgEQCATYu3cvBQUFPbuDQoheI5d4hBDd\nZsqUKXz77bfMmDGDfv36MWzYMHw+Hy+++CKzZ89mwIABjBkzps1ti4uLWbhwIZ999hkul4v8/HzO\nnj0LwOTJk5k1a9YVH1l++eWXKSsrY+bMmdi2zfz587nrrruorq7ulX0VQvQsGc1YCCGEEGlHLvEI\nIYQQIu1IQBFCCCFE2pGAIoQQQoi0IwFFCCGEEGlHAooQQggh0o4EFCGEEEKkHQkoQgghhEg7ElCE\nEEIIkXb+D8b8d5dZzA7OAAAAAElFTkSuQmCC\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "draw_conditional_distplot('duration',df)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "And in fact, we can see that the duration of the call has a close correlation with the outcome. However, once the duration of the call is known, the outcome is also known, and we can not estimate the duration of the call in advance. So we can not use this feature for our forecast and have to remove it." ] }, { "cell_type": "code", "execution_count": 35, "metadata": {}, "outputs": [], "source": [ "del df['duration']" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Number of contacts during this campaign\n", "From the dataset description:\n", "> Number of contacts performed during this campaign and for this client (numeric, includes last contact)" ] }, { "cell_type": "code", "execution_count": 36, "metadata": {}, "outputs": [ { "data": { "image/png": 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UqmAIteB6BgwiIqLSfBEwMvYUSbEejIIpknIqGEJhkycREZEHXwSMVNZaDeLV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\n/wdS5m+lZ4hg8AAAAABJRU5ErkJggg==\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "draw_conditional_distplot('campaign',df)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This graph is a little bit hard to read as there is a long tail of customers who have had many contacts. We will plot it again without the long tail." ] }, { "cell_type": "code", "execution_count": 37, "metadata": {}, "outputs": [ { "data": { "image/png": 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Up7jzzjvxer2ce+65zJ8/Px81ioiIyBDTp4BRUVHBU089BcC1117b9fgNN9zADTfckJ/K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\nrdi0aRMsLS2xevVqDA0NfXI540vs7OwQGhqK3t5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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "draw_conditional_distplot('campaign',df[df.campaign < 10])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In the smaller range, there seems to be a negative correlation between the number of contacts and the likely hood of a yes. Since there is a very long tail that will distort the actual data, we will clip the value to 10 before scaling the data." ] }, { "cell_type": "code", "execution_count": 38, "metadata": {}, "outputs": [], "source": [ "df['campaign'] = df['campaign'].clip(upper = 10)" ] }, { "cell_type": "code", "execution_count": 39, "metadata": {}, "outputs": [], "source": [ "df['campaign'] = (df['campaign'] - df['campaign'].mean())/(df['campaign'].std())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Theory: Feature scaling\n", "[Feature scaling](https://en.wikipedia.org/wiki/Feature_scaling) through standardization is a method that ensures that all input features have the same mean and standard deviation. This makes it easier for our learning algorithm to work with them since all features have the same 'scale'. In raw data, age and economic indicators such as the euribor rate might have very different ranges. While age has values between 20 and 100, the euribor will never be this high. The learning algorithm might then detect stronger or weaker correlations between certain variables just because their scales are very different. When all input features have the same mean and standard deviation, this hurdle is resolved. We can also picture this graphically. Remember that our learning algorithm is using gradient descent to optimize parameters for a minimum loss. If the 'valley' is very narrow it can be hard to find the minimum as the learning algorithm is often jumping over it. Feature scaling makes the valley more round and therefore easier to find the minimum.\n", "![feature scaling](https://storage.googleapis.com/aibootcamp/Week%202/assets/feature_scaling.jpg)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Days since last contact\n", "The number of days that passed since the customer was last contacted. 999 means they where not contacted before. We will therefore plot it without this outlier data." ] }, { "cell_type": "code", "execution_count": 40, "metadata": {}, "outputs": [ { "data": { "image/png": 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iZ6ELbo3kXYvC1BMjmIrTVjrfq6IBuXEYRiaF2d3m6X1FRGY7BQwZky8Rpai/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\nIYQQwun+B5Km8HSKTV7xAAAAAElFTkSuQmCC\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "draw_conditional_distplot('pdays',df[df['pdays'] < 500])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can see that if we cut off the values greater than 500 the next greatest value seems to be around 30. For customers not contacted before we will create a new dummy variable not contacted before." ] }, { "cell_type": "code", "execution_count": 41, "metadata": {}, "outputs": [], "source": [ "df['contacted_before'] = np.where(df['pdays'] == 999, 0,1)" ] }, { "cell_type": "code", "execution_count": 42, "metadata": {}, "outputs": [ { "data": { "image/png": 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cSluxbt0660hKWloaGzduJDk5udh2u3btoqCgwH4JRURERP5fqcUlKSmJpUuX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\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "draw_conditional_barplot('contacted_before',df)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As we can see, that most customers have not been contacted before, those that have are more likely to say yes. To not distort our data, we will set the value of days till contact for those which have not been contacted before to the mean contact time." ] }, { "cell_type": "code", "execution_count": 43, "metadata": {}, "outputs": [], "source": [ "df['pdays'] = np.where(df['pdays'] == 999,df[df['pdays'] < 999]['pdays'].mean(),df['pdays'])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We will see that now the large majority of values are at the mean of the distribution." ] }, { "cell_type": "code", "execution_count": 44, "metadata": {}, "outputs": [ { "data": { "image/png": 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h+OICROTXoBFe3ijJ8GEaEAy0Y9RX9/ewRUSkHylAyA7aogECm9y4Jsq0nSxh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\nkAO3LuHkyk+YUr0G12fw5fB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f0G2cIh63cOFC7r//fmbPnv21XicUCnH11VdzyimncNJJ\nJ6VodCIyWGkJQ0S+Ntd1OeaYYzAMgxNOOGGghyMi/UAVCBEREUmaKhAiIiKSNAUIERERSZoChIiI\niCRNAUJERESSpgAhIiIiSVOAEBERkaT9f0flxB5dKj0UAAAAAElFTkSuQmCC\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "draw_conditional_distplot('pdays',df)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we can scale the data:" ] }, { "cell_type": "code", "execution_count": 45, "metadata": {}, "outputs": [], "source": [ "df['pdays'] = (df['pdays'] - df['pdays'].mean())/(df['pdays'].std())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Number of contacts without last contact\n", "Our data set also includes the number of contacts excluding the last one. Let's see weather it is different:" ] }, { "cell_type": "code", "execution_count": 46, "metadata": {}, "outputs": [ { "data": { "image/png": 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Ejm1I2rlHAgCQ3eyWNiaTSd100036+uuvlUgkdPXVV+vkk0/uqNqQYfXrQEQsQ1VJT47r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\nlBIpVzmGp4gpFReEtL28VnE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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "draw_conditional_distplot('previous',df)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We see that this feature shows a different pattern than the number of contacts including the last one. It now shows that more contacts are relatively better. We will scale the feature and include it:" ] }, { "cell_type": "code", "execution_count": 47, "metadata": {}, "outputs": [], "source": [ "df['previous'] = (df['previous'] - df['previous'].mean())/(df['previous'].std())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Economic indicators\n", "Our dataset also features a set of economic indicators. We will not discuss these in depth, instead we will just check them for distorting long tails and scale them." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Employment variation rate" ] }, { "cell_type": "code", "execution_count": 48, "metadata": {}, "outputs": [ { "data": { "image/png": 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YsSMvv/wyABdeeGHV4+vXr6/29Y888kgCShMREZHjTWr0LYmIiMhxQcFDREREbKPgISIi\nIrZR8BARERHbKHiIiIiIbRQ8RERExDYKHiIiImIbBQ8RERGxjYKHiIiI2EbBQ0RERGyj4CEiIiK2\nUfAQERER2yh4iIiIiG0UPERERMQ2Ch4iIiJiGwUPERERsY2Ch4iIiNhGwUNERERso+AhIiIitlHw\nEBEREdsoeIiIiIhtFDxERETENgoeIiIiYhsFDxEREbGNgoeIiIjYRsFDREREbKPgISIiIrZR8BAR\nERHbKHiIiIiIbRQ8RERExDa1Ch6rV69m6tSpRzw+f/58JkyYwMSJE3n55ZcBCIVCXH/99UyZMoVp\n06ZRWFiY2IpFREQkZdUYPJ555hnuuOMOwuHwYY9Ho1EeeOAB/vKXvzBz5kxeeuklCgoKePHFF+nV\nqxezZs3ikksuYcaMGY1WvIiIiKSWGoNHbm4u06dPP+LxTZs2kZubS2ZmJh6Ph8GDB7N8+XJWrFjB\niBEjABg5ciRLly5NfNUiIiKSkmoMHmPHjsXlch3xeGlpKcFgsOrn9PR0SktLD3s8PT2dkpKSBJYr\nIiIiqezIRFFLgUCAsrKyqp/LysoIBoOHPV5WVkZGRkat9teiRRoul7O+5dgmOztY84uaIbVL9erT\nLmbAV+dtMlKo/XWuVE/tcqRDbRIsMm07VipIpVqrU+/g0b17d7Zt28aBAwdIS0tj+fLlXHvtteza\ntYuFCxcyYMAAFi1axODBg2u1v6Ki8vqWYpvs7CAFBerB+Ta1S/Xq2y7e0lCdtwmnSPvrXKme2uVI\n32yTknr8TtRVQUFqXOSZKufKscJRnYPH3LlzKS8vZ+LEidx2221ce+21WJbFhAkTyMnJYfLkydx6\n661MnjwZt9vNI4880qDiRURE5PhRq+DRsWPHqstlL7zwwqrHzznnHM4555zDXuv3+3nssccSWKKI\niIgcL1Kjb0lERESOCwoeIiIiYhsFDxEREbGNgoeIiIjYRsFDREREbKPgISIiIrap9wJiIiIiTU3P\nFW/Wb8NOlyW2EDkq9XiIiIiIbRQ8RERExDYKHiIiImIbBQ8RERGxjYKHiIiI2EbBQ0RERGyj4CEi\nIiK2UfAQERER2yh4iIiIiG0UPERERMQ2Ch4iIiJiGwUPERERsY2Ch4iIiNhGwUNERERso+AhIiIi\ntlHwEBEREdsoeIiIiIhtFDxERETENgoeIiIiYhsFDxEREbGNgoeIiIjYRsFDREREbKPgISIiIrZx\n1fQC0zS5++672bBhAx6Ph/vuu4/OnTsD8Pnnn3P//fdXvXbVqlU88cQTDBgwgLFjx9KrVy8ARo0a\nxdVXX91Ib0FEROTYTMACnMkuRGoOHu+++y6RSISXXnqJVatW8eCDD/Lkk08C0KdPH2bOnAnAW2+9\nRZs2bRg5ciRLlixh/Pjx3HnnnY1bvYiISA02ODL5u7snRYYPjxXHR5wMK8IlsS2cYB5MdnnNTo1D\nLStWrGDEiBEADBw4kLVr1x7xmvLycqZPn86vf/1rANauXcu6deu48sorueGGG9i7d2+CyxYRETm2\nGAb/cXXhcXc/DuKhh3mQHKscrxVnl5HGDHc/3ne2TXaZzU6NPR6lpaUEAoGqn51OJ7FYDJfr601n\nz57NeeedR8uWLQHo1q0b/fr147TTTmPOnDncd999PPbYY41QvoiIyJH2GV6ec/chzxEg26zge9EN\ndLZKq57fZAR5xnMiL7l7kG/4udWycBpGEituPmoMHoFAgLKysqqfTdM8LHQAzJ0797BgMXz4cPx+\nPwCjR4+uVeho0SINl6vpj75lZweTXUKTpHapXn3axQz46rxNRgq1v86V6qldjnSoTYJFZq238Xrd\nRDF4hr7sNNI43drLJGMbPo8JuKtedyIhfsU6plu9eM/VAcdnpfz+zHYYKRA+Uv1cqTF4DBo0iAUL\nFnDBBRewatWqqgmjh5SUlBCJRGjXrl3VY3fccQdjxozhggsuYOnSpfTt27fGQoqKyutRvr2ys4MU\nFJQku4wmR+1Svfq2i7c0VOdtwinS/jpXqqd2OdI326SkDr8T4XCUV11d2OlK47TYHibHvqx8vJrX\nZhDlJlbztOdE5ufBrFV7GdMxLRHlN5pUOVeOFY5qDB6jR49m8eLFTJo0CcuyuP/++3n++efJzc3l\n3HPPZcuWLXTo0OGwbW6++WZ+9atf8eKLL+L3+7nvvvsa/i6kyTLfeaV+fyxHXtQI1YhIc7bRkcl8\nZweyzQomxDbX+Ho/ca6ObOD+9GE8vaGEoW18ZHm00kRjqjF4OBwO7rnnnsMe6969e9W/BwwYwIwZ\nMw57vlOnTlVXu4iIiNghFLd4yd0LA7g6ugEvtRuiaUGE7/UM8OT6Ep5eX8ytA7Iat9BmTrFORESO\nCwt2V3DA8HJ+bDtdvjGRtDYu6pxGrwwX83aFWLGvuoEZSRQFDxERSXl5pTE2HozRxSxmTDyvzts7\nDYMb+2XiMOCxdcWE4lYjVCmg4CEiIseBjwoqeykuj26u9+qk3TPcTOiSzu6KOLO3lNW8gdSLgoeI\niKS0HWUxdpbH6RJwHbZWR31c2T2doNvgtW1lhNXr0SgUPEREJKUd6u0Ylu1p8L78LgfjOqVxMGrx\n7q6KBu9PjqTgISIiKWtnWYwdZXE6B5y0TavxQs1auTg3DZcBr2wtw7TU65FoCh4iIpKyDvV2DM32\nJmyfrXxOzm7nY0dZnGUFusIl0RQ8REQkJe0qj5FXFqdTupP2CertOGRC13QAXtna9FfVTjUKHiIi\nkpI+LogAMCyBvR2HdAu6GdTKw+rCCF8cjCZ8/82ZgoeIiKSc4ojJ1tIYbf1OOqQntrfjkMu6VPZ6\nzN6qS2sTScFDRERSztqiyt6O/i3cNbyy/ga39tAl4GLhnhD7Q/FGO05zo+Ah0hSZtb8NuEhzE7cs\n1h2I4nFAz8zGCx6GYTA+Nw3TggW7634jTKmegodIE2GUHMCz+C28C/5D2j8fw/f6CxhFBckuS6TJ\n2VISozxm0SfLg9thNOqxzmrrw2WgNT0SqHEGxkSkTjwfzyP9ufsw4pXduWYgE2dRAf43/05kyFnE\neg0Eo3E/YEVSxaeFjT/MckiGx8GwNl4W54fZVByle0bjH/N4p+AhkmSO3VtJ/+tD4PZSfunVEA1j\nZbTEmbcJ79K38S6bh3PXVsIjxoNLH3rSvB2MmGwvi9MuzUkrX33vylI3o9r7WZwf5t1dFQoeCaCh\nFpFkClcQeOo3GJEQpVffSmjsZKyMlgDEO3WnYvzVxNvm4tqxCffaj5JcrEjy2TGp9NuGZnsJug3m\n7woRN7WSaUMpeIgkUfqsR3Ht2kLo7EuJDjn7iOettAChsy7BTAviXvcxRnFREqoUaRripsW6oihe\nJ/S0sefB7TA4u52foojJiv0R2457vFLwEEkSz5K38C55i1jn3pRf/tOjv9DtITLkbAwzjmfZPNC9\nI6SZ2lwaoyJu0SfTg6uRJ5V+26j2PgDe3alJpg2l4CGSDOEK0l5+AtOfTukPfwvuY99VM57bk1j7\nLrh2b8W5faM9NYo0MRsOVK4geqKNwyyH9M500zHdyZK9Icqiuty9ITS5VMRGb+RV3veh97I5DC0r\nZvXI77ImlAV5X98Pomdh9V25vl4jGbBnO8ay+azzd8B0fR1Wvsg7/H4S4zqlNUL1IskTillsKY3R\nyusg26ZJpd9kGAaj2vv56xelvJ8f4ryO+h2rL/V4iNjMMOP0+fDfxFweNp5yYa23C6VnsavbELyh\nMjp8ubwRKxRper4ojmJacEJW8q4qObe9H4B5u7SYWEMoeIjYrNP6JQQP7GHzSaMIpWfVadudPYYQ\n8aaRs+1THHHduEqaj/Vf3aitdyOuVFqTHL+TE7PcfFoY4UBEwy31peAhYifLou+S2VgYfD7s0rpv\n7nSxt1M/XLEwrXZuaIQCRZqe4ojJrvI4HdOcBN3J/bN1Ro4PE1iSr16P+lLwELFRdt46Wu/awI7e\nwylu3ale+8jv3A/LMGi7bY2ucJFmYcOh3o4kDrMcMqJt5dUt7+9R8KgvBQ8RG/Vd+goA606dUO99\nRH0BCtt2J714H8Gi3YkqTaRJsiyL9QejOA171+44mhy/k14ZLj4pjFCs4ZZ6UfAQsYkjP4+OGz6k\noMMJFHTq26B97el8EgA5W1cnojSRJmtzSYzCsEnXoAuvs2ncr2hEWx+mBUv3qtejPhQ8RGziXfI2\nBhbrh17c4Bu+lbRsT1mwFS33bMIdKk1QhSJNz7yv7gqbzEml33ZGzlfDLfnhJFeSmhQ8ROxgWXg+\nnkfU7WNH71Mbvj/DIL/LSTgsk5ztaxu+P5EmyLQsFuwO4XVAl0DTWXaqQ7qL7kEXK/eFtZhYPSh4\niNjAueVznAW72NH7VGIeX0L2ua99b2IuD222r9WltXJcWlsUZX/YpEeG2/Yl0mtyRlsfMQuWFqjX\no64UPERs4F32LgBb+p2VsH2aLjcFHfvgCZfTbtPKhO1XpKlY9NWVIz2b0DDLISNydHVLfdXYd2Wa\nJnfffTcbNmzA4/Fw33330blz56rn77vvPlauXEl6ejoAM2bMIBqN8stf/pJQKESbNm144IEH8Pv9\njfcuRJoyM45n+XzM9Ax2dx+U0F3v63AC7baupuun89nZa1hC9y2STHHT4v09ITLdBp3S7V8ivSa5\nARedAy6W7wtTHjNJc+l7fG3V2FLvvvsukUiEl156iZtvvpkHH3zwsOfXrVvHs88+y8yZM5k5cybB\nYJAZM2Ywfvx4Zs2axYknnshLL73UaG9ApKlzbViF42AhkcFnYToT+82tLLMNFelZdNrwIa5wec0b\niKSIFfkVFEVMzmj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mnM3IJ48RzLoFpOoHIGyKTNcmJtYd9/LFMTe9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\nAABJRU5ErkJggg==\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "draw_conditional_distplot('emp.var.rate',df)" ] }, { "cell_type": "code", "execution_count": 49, "metadata": {}, "outputs": [], "source": [ "# Feature scaling\n", "df['emp.var.rate'] = (df['emp.var.rate'] - df['emp.var.rate'].mean())/(df['emp.var.rate'].std())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Consumer price index" ] }, { "cell_type": "code", "execution_count": 50, "metadata": {}, "outputs": [ { "data": { "image/png": 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xteNldX/lztlWy7A8D+MKfZzRw4+7GXtFWP4MomdchO/1v+NZ+hGWL0By0NGH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\nQmCC\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "draw_conditional_distplot('cons.price.idx',df)" ] }, { "cell_type": "code", "execution_count": 51, "metadata": {}, "outputs": [], "source": [ "# Feature scaling\n", "df['cons.price.idx'] = (df['cons.price.idx'] - df['cons.price.idx'].mean())/(df['cons.price.idx'].std())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Consumer confidence index" ] }, { "cell_type": "code", "execution_count": 52, "metadata": {}, "outputs": [ { "data": { "image/png": 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SqucJ1bQ9wvBe75x95dFPfI9ha19l2ku/Il57gGQ0zpoLrmf7xIupKRvBiVJY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\nzzDnznZ3Lza23iLeebhU+/+2bbfT/8sL+H9s0d3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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "draw_conditional_distplot('cons.conf.idx',df)" ] }, { "cell_type": "code", "execution_count": 53, "metadata": {}, "outputs": [], "source": [ "# Feature scaling\n", "df['cons.conf.idx'] = (df['cons.conf.idx'] - df['cons.conf.idx'].mean())/(df['cons.conf.idx'].std())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 3 month euribor" ] }, { "cell_type": "code", "execution_count": 54, "metadata": {}, "outputs": [ { "data": { "image/png": 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xvzQT2+UicVierr1oxdahRxH3FdBn7bt4I43ZLkekW8n36ZEWLeswluj49rRQ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\nHY//tnto77DyXJ80Ht/i5s39XrbWhviv7sm0tl0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5SYIhxDVo3bp17Nq1iyVLlqDRaJg5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\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "draw_conditional_distplot('euribor3m',df)" ] }, { "cell_type": "code", "execution_count": 55, "metadata": {}, "outputs": [], "source": [ "df['euribor3m'] = (df['euribor3m'] - df['euribor3m'].mean())/(df['euribor3m'].std())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Employment number" ] }, { "cell_type": "code", "execution_count": 56, "metadata": {}, "outputs": [ { "data": { "image/png": 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sdNX4Ex8+fDiLFy8GID8/n/79+1cfGzx4MKtWrSIajVJeXs7WrVuPOX60UCjEJZdcQkVF\nBZZl8dFHH2nuiIiIiNTcMzJhwgSWLl3KlVdeiWVZzJkzh7lz55KXl8f48eOZMWMG06dPx7IsZs6c\nidfrPe55gsEgM2fO5Nprr8Xj8TBy5EjGjh2b8gaJiIhIy2JYlmXZXcSJ1NR11Za6t46mdrctanfb\n0lLa/drOkw+j19ak7mlAw9udqnrgq5qaQkt5vVOhQcM0IiIiIo1JYURERERspTAiIiIitlIYERER\nEVspjIiIiIitFEZERETEVgojIiIiYiuFEREREbGVwoiIiIjYqsbt4EVEGtvxds8MFpt1vvlZU+6c\nKSKpo54RERERsZXCiIiIiNhKYURERERspTkjIiLSpu2qSLC5LE40aRFJwseFUWYOyiTLo8/rTUVh\nRERE2qziaJJ/FVSStL76XkEoge/zMv5rSJZ9hbUxCiMiItImWZbFu3siJC0Y39lHj6ALj8Pg/b0R\nFu2NML5LlDNzvXaX2SaoD0pERNqkdcVxdlcm6R10MTDbTdDtwOs0mDkoA6cBf1pfSjhh2l1mm6Aw\nIiIibU4obvLh/ggeB4zr7MMwjOpjvYJupvZK50DE5NnNIRurbDsURkREpE2xLIv390aImTC6o4+A\n+5tvhVf3CdAtzcm/CirZWBKzocq2RWFERETalH3hJFvLE3RJczIo233cx3icBj8ZmIEF/GN7RdMW\n2AYpjIiISJuyoSQOwJm53mOGZ75uSI6HrmlOPj4YI3r0chtJOYURERFpMxKmxRelcQIug+7pzpM+\n1jAMRnf0EU1arDwYbaIK2yaFERERaTO2lieImTAgy43jJL0iR4zu5ANg6f663bRR6kZhRERE2owN\nxVWTUU/LOv5cka/rn+Givc/B8gNR4qaGahqLwoiIiLQJ5XGTLyuSdPY7yfaefIjmiCNDNRUJi88O\naVVNY1EYERGRNmHj4Ymrp9ayV+SI0R2rhmqWaKim0SiMiIhIq2dZFhtK4jgN6J9ZtzAyMNtNlsfB\nsgNRkpaGahqDwoiIiLR6+8JJSmImfTJceJ01T1w9mtMwOKeDl9KYyfrieCNV2LYpjIiISKu3qbQq\nRJyW5anX848M1XyooZpGoTAiIiKtmmVZbC1P4HVAtxr2FjmRIe08BFwGyxVGGoXCiIiItGqFEZNQ\n3KJn0I2zFnuLHI/bYXB6jof9EZMD4WSKKxSFERERadW2llcN0fQJuhp0noHZVUM864u1xDfVGvbK\niIh8jXfxy3V+Tr+ib/5x93rdRKPHnyy4ecTEOl9D2q5tZQmcBvQINOwtb9DhJcHrS+KM6+JPRWly\nmHpGRESk1SqNmRyMmnRLd+Gp4yqar+ub6cbtUM9IY1AYERGRVmtbioZoADwOg1My3WwvT1CZMBt8\nPvmKwoiIiLRa28oSAPROQRgBGJjlwQQ+L9F+I6mkMCIiIq1SOGGyuzJJJ7+TdHdq3u4GZh+eN6Kh\nmpRSGBERkVZpRyiBRep6ReCrTdPWq2ckpRRGRESkVdp6eIimT0bqwkiGx0FeupPPS+IkTd2nJlUU\nRkREpNVJmBYFoQRZHgc53vrtunoiA7M9RJIW28oTKT1vW6YwIiIirc6XFQkSVmpW0Xxd9eZnJZo3\nkioKIyIi0upUr6JJ4RDNEQOPbH6mO/imjMKIiIi0KqZVNYSS5jLo5E/tEA1AlzQnWR4H64tjWJbm\njaSCwoiIiLQq+8JJwkmLXgEXjnreGO9kDMNgYLabg1GTAxFtfpYKCiMiItKqNMYqmq879fAS302l\nGqpJhRrDiGma3HHHHUybNo0ZM2ZQUFBwzPH58+czefJkpk6dyqJFi4459uyzz3L//fdXf/3ee+8x\nZcoUpk2bxvz581PUBBERkSqWZbG1PIHbAd3TGy+M9DscdDYrjKREja/UwoULicVizJs3j/z8fO69\n914ee+wxAAoLC3nhhRdYsGAB0WiU6dOnM2rUKEzT5Pbbb2fNmjVccMEFAMTjce655x5efPFF/H4/\nV111FePGjSM3N7dxWygiIm1GUdSkNGbSN8OFy5H6IZoj+mZUTWLdUqYwkgo19oysWrWKMWPGADB0\n6FDWrVtXfWzNmjUMGzYMj8dDMBgkLy+PjRs3Eo1Gueyyy7jllluqH7t161by8vLIzMzE4/EwYsQI\nVq5c2QhNEhGRturI3h+p3HX1eIJuB538TraUxTWJNQVqDCOhUIhAIFD9tdPpJJFIVB8LBoPVx9LT\n0wmFQmRmZjJ69OhvnOd4jxUREUmVbeUJDKBXwN3o1+qX4aI0blGoSawNVmN0DAQCVFRUVH9tmiYu\nl+u4xyoqKo4JHCc7z8kee0R2dhou18mXZeXmnvwcrZXa3ba0pHabAV+dn+OtOP4nS6/3+G8owRNc\noyX9nE6mJbQjWJyaN+Cj29qQdgeLTcpiSfaFk/TIcJOb5U9JTSczpHOcJfujHMDFwNxAzU9o4PVa\nsxrDyPDhw1m0aBETJ04kPz+f/v37Vx8bPHgwDz30ENFolFgsxtatW485frQ+ffpQUFBASUkJaWlp\nrFy5khtuuOGk1y4urjzp8dzcIIWF5TU1odVRu9uWltZubyhS5+dEo98cd/d63cf9PkD5Ca5RWNjy\nFwi2lNf7RK9BXR15zRra7vJQhJWFUQB6pzsbVF9tf4+6uKoC2cpdZQzy12+opqW83qlwstBVYxiZ\nMGECS5cu5corr8SyLObMmcPcuXPJy8tj/PjxzJgxg+nTp2NZFjNnzsTr9R73PG63m1mzZnHDDTdg\nWRZTpkyhY8eO9W+ViIjIYZZlsaEkjtOAUzIbf4gGjp7EqnvUNFSNYcThcHDXXXcd870+ffpU/3vq\n1KlMnTr1uM+dPHnyMV+fd955nHfeefWpU+S4vItfbvRrRL99aaNfQ6Q1sg7vhLqiMEplwuLUTDeD\ncjxkeVLfg7UvnKQkZtI/04XX2XiraI6W5XGQ63NoRU0KNO50YxERaZN2hhIsPRBhf9jEADxOWHUo\nxqpDMXoEnIzv4ifoTl0o2VBSFQhOO7wZWVPpl+Fm2YEohyJJ2vlSv/V8W6EwIiIiKbWnMsE/C6rm\n/PXLcHF2By8ZbgdbyhKsLY5REEry0o5KvtcrLSXXiyQtviiNE3AZdE9v2kDQ93AY2VwWVxhpgJY/\n20tERJqNhGmxcHfV5NHLe6QxsXsaOV4nLofBgCw33+uZxoh2HopjJi/tqKQs1vBVOUv3R4iZcGqW\nu1HuRXMy/Q/PT9mseSMNojAiIiIp80lhlOKYyeAcN3mBb3a+G4bBqI5eBh++0dyvVhVTmWhYIHl7\ndxioCiNNre/hbeE1b6RhNEwjIiIpURhJsvJgjIDbYFSHE+83YxgG53b2Ebfg85I4d+eX8HjHjBrP\nf7wJ6/tMD/mRIfQxyzhz3YcNqr9a9+/V+qE5Xic5XgebFUYaRD0jIiLSYKZl8e6eMCZwXmcfnhpW\ntBiGwfldfJzR3sPKgzGeWFNUr+v+M9ERC4Ozkgfq9fxU6Jfh5mDEpDiatK2Glk5hREREGmxDSZz9\nYZNTMl30CtZuuMRhGNw2OItOfidPrS1i+YG6bVS20/SxINGRTkaUbyUL61N2SvSrHqrRvJH6UhgR\nEZEGW1sUwwBGdazb7QAyPA5+MywLr9Pg92tK2V1R+zf0R2J5JHBwq/tLPNh3f5h+1ZNYNVRTXwoj\nIiLSIPvDSQ5ETHoFXfXaO6RPhptfndWBioTF7E9LarXCZkUyk+VmFkMdZYx1Ften7JTpc7gnaKvC\nSL0pjIiISIOsK44BMCi7/qtZvtMng8k90igIJZi1sojy+IkDSdwyeDiWhwOLn3gKaOLVvN+Q63MQ\ndBtsLdcwTX0pjIiISL3FkhabSuME3AY9jrOUty5uGhDk4m5+tpQl+OXKYipOsOT3b4nO7LT8XOo6\nQB9HuEHXTAXDMOgTdLOnMtngZcptlcKIiIjU2xdlceImDMryNHjDMYdh8B8DM7igq59NpXF+tbKY\njSUxLKvqjriFpptfRfvyTLwbmcT5gXt3KpqQEn0OT2Ldrt6RetE+IyIiUm/riqsmrp7WgCGaozkM\ng5mDMoibFov2RvjJiiI6+Z2MaO/h/cjpVOBiiKOMX3h2kGk0nzf+o+eNDMxu2vvjtAYKIyIiUi8H\nwkn2h+s/cfVEnIbBbYMzGdfZx/t7Iyw/EOW1nWECwM8925nkLMRh8zyRrzvSM6J5I/WjMCIiIvWS\niomrJ+IwDM7u4OPsDj4iSYvPS2L0X/cuOc2oN+Ro3dNduA2tqKkvhREREakz07LYUpYgzWXQs4ET\nV2vicxoMa+fF20yDCIDLYdAj6GJ7KEHStHA2t66bZk4TWEVEpM72hZOEkxa9g64mv1Nuc9Un6CZu\nws46bNwmVRRGRESkzrYd3vq8V1Ad7Edo3kj9KYyIiEidbS1P4DIgL11h5AjtxFp/CiMiIlInX4YS\nlMRMegRcuDQ3olrvoHpG6kthRERE6uTI3XV7Z6hX5Gjpbged/U62lSeqN2qT2lEYERGROll+IIoB\n9GrkVTQtUZ8MF6Uxk6KotoWvC4URERGpteJoks9L4nROc+J36S3k63ofmTeioZo60W+SiIjU2orC\nKBbQR6tojqt6RY0msdaJwoiIiNTa8v1R4KseADlWH/WM1IvCiIiI1EokabH6UJQeARdZXr19HE+u\nz0HQbbBFPSN1ot8mERGplXVFMWImnJnrtbuUZsswDPpmuNlTmaQioUmstaVBPxFpEkaoFEfhXrBM\nDMsCLMzsDpg5HewuTWpp9aGqIZrh7TzsCydtrqb56hN08emhGFvLEgzO8dhdTougMCIijScew/Xl\nF7i2rse5f+dxH5LM7kCnzgM42PUUEh5/ExcodbH6UAy3AwZle9gXDttdTrPVL6Nq3siWsrjCSC0p\njIhI6lkWrm3r8XyyCCNe9Wk62bEbie79wOUGDLBMnLu349y9jZ7Fi8nbuJSCU0ezv8dg0I3Xmp3i\naJJt5QmGtfPgder1OZm+R4URqR2FERFJrWgY74p3cH35BZbbQ2zwSBK9B2IFs77x0ET/IRCuoHD9\nWrpuXUWv9R8QLN7LttPPA69WazQnqw/FABjRTp/0a9I13YnfabClTCtqakthRERSxrXpU/yvPIcj\nHCLZoSvRUROxApknf5I/nX29h3OoS3/6rX6D9nu+IL20kB0jLyXqreG50mRWHzw8X6S9Jq/WxGEY\n9MlwsaE4TiRp4VNPUo20mkZEUsK9ZjnBh36OEakkNnQ0kQnTag4iR4n7Anx+9mT29BqGv6KYfh/M\nw1de1IgVS21ZlsWnh2Jkuo3qm8HJyfUJujGB7eUaqqkN/VaJSIO58z8k8Pgd4HQSOfcyzM49av3c\nSsvB/7r68omzA25MfMPO4tKsRfz7p3M59ZP/Y905VxD3BRqxeqnJzookB6MmYzv5cGg+T630O7wT\n65ayBKdmaWirJuoZEZEGca9eTODxX4PTRflPfl+nILLZTOOmyECWuzqRacXIsaI4LPjfXhfy6MAr\n8YbLGfDJyzgPT4IVe6yqHqLRm2pt9c3UJNa6UM+ISBviXfxySs/n3L0N76J/VfeInGj57vG8ksjl\nj7EexHFwXmIXlyYKcFF12/W9RhqP9Z9Ix8qDTNm+kL6rX+eLMy7FcjhTWr/UzleTVzVfpLby0l24\nHbBZYaRW1DMiIvVilBzEu+RVcDiIjJ+C2bFbrZ+7OhnkD7GepJHkPu8mJid2VAcRgM5WJb80NvC3\nwVNZ3HkE2Qd30n3j0sZohtQgYVqsKYrRLc1JB7/CYG25HAa9g24KyhPETavmJ7RxCiMiUnfRML73\n/4URjxEdeSFmh9oHkTLLyZxYHxzAPd4vONtZetzHZRLn3xMbeOZb32dHoAtdtueTcbD2PS+SGp+X\nxAknLa2iqYc+QRdxC74MaYlvTRRGRKRuzCS+xa/gKC8hNuhskr1OrfVTLQvuj/Wi0PJwvXs3A50V\nJ328F5MrrALuPOPfSBhOen/2juaPNLFPD28BP0z7i9TZkc3PNFRTM4UREakTz8r3ce77kkT3vsSH\njqrTc19PtueDZA6DHWVc7dpTq+e0t6L0Cnp4+tQp+CIheq57vx5VS32tPhTDYcBQbWteZ/2qJ7Gq\nZ6QmCiOTy2lXAAAgAElEQVQiUmvOgk24N32KmdWe6KiJddq2fZ/p4c+xHgRIcLtnG3XZB2p8cjdv\n9LuAddl9yd2ziR7rP6hH9VJXFXGTjaVxBmS6SXfr7aKuegVcOAytqKkN/XaJSK0YoVK8y9/GcrqI\nfPtScNftk/Iz8W6EcfLvni/p6IjV6blOLKYlt/Obb/2IiNPDWa/9GV9FSZ3OIXWXXxTDtKru0it1\n53Ea9Ay42FqeIGlpEuvJKIyISM1ME++Hr2PEo8TOHI+VmVOnp281/byTbEdfo4ILnAfrVUJvq5xu\naQ4eHjgdbyTE0Pfm1us8UntHlvRq8mr99c1wE01a7KpI2l1Ks6YwIiI1cq9djrNwN4kep5DoM6jO\nz38q1g0Lgxs9u3A0YAPPCxO7WNB7Atuy8uj36Vu0272p/ieTGq0+GCXNaTAgUzctrK/+h3di3VRS\nt97AtkabnknLlYjj3L0Nx6H9OIoLcZQUYoQrsDxeLI8fvD6S7TqR7HkKZnYH3Za+nhz7d+FeuwIz\nPYPo2RPq/HNckwyw3MxmqKOMsxzHX8ZbWzlEGUYx9wz+AU8tns2ZbzzMGzf8EQx9rkq1/eEkuyuT\nnJ3rxdWQBNnGDTi8FfznpXEuqP0K+DZHYURaHEfhHrxLXsH74es4yourv295vJiBTIxYFEeoBKM4\nhnPfl7D+Y8xgFomeA4ifOgK8fhurb2HiMbzL3gAgOnoSeHx1erplwZPx7gDc5N6Zkjw4PrGLe3KH\n837PMZy7Ywl9P32LLcMvbviJ5RirtaQ3JXoHXXgcsLFEk1hPpsYwYpoms2fPZtOmTXg8Hu6++256\n9Pjq3hPz58/n73//Oy6Xi1tvvZVx48ZRVFTEz3/+cyKRCB06dOCee+7B7/dz9913s3r1atLT0wF4\n9NFHCQaDjdc6aVWsvV8SeOJ+PPlLADDTgsQHDCfZuQdmdi5WWvDYT+2JOM49O3AVbMK5ayuetStw\nf/EZsWFjSPQ9XT0lteDJ/xBHqJTYwDMwO3St8/OXJbNYawYZ7SyqcU+R2upqVdIz4OLe065m5O6V\nDHt3Ll+eOpqYX39LUmn1wcNbwGu+SIO4HAb9MtxVm8clTPwu9eIdT41hZOHChcRiMebNm0d+fj73\n3nsvjz32GACFhYW88MILLFiwgGg0yvTp0xk1ahSPPvool1xyCZMnT+bJJ59k3rx5XH/99axfv56n\nn36anJy6TX6TNi5Sif/V5zDf/QeeRIJE74FExl1ObMRYvMvfOvHzXG6Sef1I5vWDRBzXpnw8a5bh\nXfE2ri1riZ01ATOnQ9O1o4VxHNiNa+NqzIxs4oPPqfPzLQueT3TBwOKH7t0pre1b7T28GMph/pAr\nmLHyeYa8/wKfXPxvKb1GW2ZaFvmHorT3Ouieri3gG+rULDfrS+JsLkswWPu1HFeNEW3VqlWMGTMG\ngKFDh7Ju3brqY2vWrGHYsGF4PB6CwSB5eXls3LjxmOd8+9vfZtmyZZimSUFBAXfccQdXXnklL774\nYiM1SVoT92dLybp9Ov63/gbZ7Sm/5S7KZj1K7OwLwF2HT2wuN4mBZxD+7g9I9DgF58G9+N74X5w7\nNjZe8S1ZIo53+ZsAREdeBK66T2DMN4NsNAOMdhbTyxFOaXld0px08jt5pPtFFGd1of+q1wgeSm3g\nacu2liUojVdtAW+oB7HBjswb2ahJrCdUY89IKBQiEAhUf+10OkkkErhcLkKh0DHDLOnp6YRCoWO+\nn56eTnl5OZWVlVxzzTV8//vfJ5lMcu211zJo0CAGDBhwwmtnZ6fhcp08lefmts2u2dbebisawXr2\nQaw35oPbg3HVLRiXXUeW99g5C2agbnMYCPhg4hVYOzZjLHwJ35JXIVIOI0af8I9uRjP4Wafq9a7t\nz8tavhTKiuH0M0nr3ade15pfUjWsc0NGEQH3ya/rrTj+Hgxe7/FDUEbQz5huDv6xOcm8M6/llrfv\n5YzFz5M74aF61drc2P3/+7UDRQCc2yvjhLUEi81jvu6x4pV6XatdYdUcLhNoV9ODT/L7e6LfofpI\n9c9/VJoP8kvYFraOe267X+/moMYwEggEqKj4aqzXNE1cLtdxj1VUVBAMBqu/7/P5qKioICMjA7/f\nz7XXXovfX/WLd/bZZ7Nx48aThpHi4sqT1pabG6SwsLymJrQ6rb3dzl1bSX/yTlx7d5Do2ouKG39D\nsmtvcr2+b7TbG4rU7yLtu2NceBW+RS/h+OQDEgcLiY68AJzf/C8RtflnncrXuzY/L8fBvfg+W4EV\nyCQ88Gyox894q+lnWSyDIY4yekWLCNVwO5lo9JuT+7xe93G/D1AeitDJbRFwGzyfMYzJXU6h69oP\nKProI5K9T6tzvc1Jc/j/vbig6vp93OYJayn/2u/FiV6rmoRCVR8CAgEfofr+f27A9Y8n1T9/h2WR\n43WQfyDMgQNlx3zwaQ6vd1M5WeiqcZhm+PDhLF68GID8/Hz69+9ffWzw4MGsWrWKaDRKeXk5W7du\npX///gwfPpwPPqjarnnx4sWMGDGCHTt2MH36dJLJJPF4nNWrVzNw4MCGtk1aGc+Kt8mYczOuvTuI\nnDeFsl8+SbJr70a5lpWdS/jiq0m274xr+wa8i18Bs41vTJRM4F32JoZlER15YZ13WT3ib/HOAEx3\n701ldcdwGAaDsjzELIN/nnUdAGkLHquarCL1Fk6YrCuK0TfDRba3lc4XMU28laVkHPySnL1bCBTv\nxR0JNdrvjmEYnJrlpihqUhgxa35CG1Rjz8iECRNYunQpV155JZZlMWfOHObOnUteXh7jx49nxowZ\nTJ8+HcuymDlzJl6vl1tvvZXbbruN+fPnk52dzR/+8AfS0tL4zne+w9SpU3G73Xz3u9+lX79+TdFG\naQmSCfwLHsf/znxMfzqhm+8kPqRuN2GrF386kQlT8b3/L1y7tsLSN4mOuhgcbXPGu3vtChylh4j3\nH4LZKa9e59hnengv2Y5eRmWD9xWpycBsNx8VRnk5rT+X9DuLbl98hHvNsqb53Wml1hTFiFvwrda0\nisaySCs/SLs9m8nevxVfRSkO65uhwDQclGd3xtk5k9jwb9dtXloNTs10s3R/lI2lcTr4W2nIa4Aa\nw4jD4eCuu+465nt9+nw1hjx16lSmTp16zPH27dvzzDPPfONcN954IzfeeGN9a5VWyqgoI/DEbNyf\nryTZKY/yH82p9xthvbjcRMZ+F9+7L+La8TmW203srLpv7tXSOYr24173EWZ6kNjwsfU+z/xEJ5IY\nXOXe2+g/woDbQa+gi23lCd4ZfR3Xb/kE/4IniA8667hDblKzlYeX9LaGMOJIxOhYsJYOOzfgr6ja\nkyjpdFOR2YFIWibR9EwSbi+eSAXecDneylIyi3bD07/FTM8gNvJCwhNnYAWzGlzL0ZNYv92pjnPd\n2gD9bxVbOfbtJPjn/8R5YDexIedQ8YPbsdICNT8x1dweIudNxvfOfNyb12C5PcRHnNv0ddjFTOJZ\n9lbV8MzZ9R+eKbVcvJbIpYMRZbyzKMVFHt+gbDfbyhN84OzMVaMuxvfha3iWv0Vs9KQmuX5r88nh\nLeBPy2q5W8A741E67fiMTtvzcccjmA4nhzr15VCXfpR06InpPHHbvBUlDPBVDVf6Fv4Dz0cLqZjx\nc+LDxjSopn4ZLhzA59r87LjaZl+0NAuuTZ+Scc8tOA/sJnzx1YT+bY49QeQIj4/I+O9hZuTg2bAS\n1xf59tXSxNzrPsZZfIB439NJdulZ7/O8lOhABCdTXftwGU0zd6NHwEXQbbCpNE7RxOuwXB78r8yF\neA2zZuUbdlck2FOZZFh7T4vcAt4wk3Teuoph782l+xcrANjZ/2xWnf9DNo+YSFHnficNIgDR9CzC\nU26h5L4XqfzerRjhCoKP/or0Z+7GqKj/RFO/q6oXb3NZnISpeU1fpzAitvAsfYPggz/DiIYJXT+L\n8OSbm8c8DV8akfFTsLx+PB+/h2Pfl3ZX1OiM4kLca5dj+gPERtR/eCZiOfhnvCNBEkxyFaawwpNz\nGAaDsj3ETVgYziBy3uU4iw7g/eD/mqyG1mLVwaoA1xKHaDILCxi8+K/02LgUy3BQMGAUn553Pbv7\nnUmyPnM/XC4iF15F6a+fJtFzAN4Vb5Px2xtw7N9Z7xoHZLmJmbCtPFHvc7RWGqaRRuNd/PI3v2lZ\nuD9dgmf9x1geH5Gxl2IkE8d/7NeYAV/9l/LWgRXIJDL2UnwL/4Hvg5eJnnt5vbZCbxFME+/yNzFM\ns+omeHW898zRXk+0pxQ317p2k2Y07YqB07LcrDgQ5fVdlXznoqvxLX4F/2svEB19CfjSmrSWluyT\nFjhfxCgvwbNyEafu2oqFwb4eg9nZ/2ySDfhdPprZpSdlsx7F/8qz+F97noz7fkz5T++v2tm5jk7N\ndPPazjAbS2L0152Qj9EMPopKmxGP4V38Mp71H2MGswlfPL1pJ6rWgdmxO7Ezz8eIRQg8/F8QTs19\nVZob94ZPcB7aT7z3aSS71W9zM4CEBfMSnfFgMtm9P4UV1s6RiaxbyhJsttIJX3gVjlApvnfmN3kt\nLVXMtMgvitE93UnHlrDaIxHHnf8h/pfn4tq1lbKcLqwdcxU7Bp2bsiBSzekifNkPqZg+EyNUQvD+\nn+D64rM6n+a07Kq5WGuLNW/k6xRGpEkYlSF8b8/D9eVmkh27E754OlZG875HUaLfYOIDhuPau4PA\nX34HZuvaH8AoPYT7s2WYvjRi3xrXoHO9n8xhn+VloquQbMOeLuiB2VWfNN/cFSZy/hWYwSz8b/8d\no7zElnpamvXFMaJJq/n3ilgWzoJN+P/vL3jWrsDy+YmMvoQNZ0+hMqN9o146Ou5yKm74NUYsQvCh\nn+HasLJOz++a5qS910F+UQxT++EcQ2FEGp3j0H58b/wPzqL9xPsMIjL+e+D1211WrcRGnEt8wHA8\n+R/ie+tvdpeTOqaJd/lbGGayahlzA14Py6ra5MyBxTTXvhQWWTc9Ay5yvA7e2xMm6vYTnjgDI1KJ\n743/sa2mluTIkt4zmnEYMYoL8b0zD9/iVzAilcQGnUX40h+Q7DWgyZbix846n9CP7gELgo/djvPL\nzbV+rmEYDG3noTRmUhDSvJGjKYxIo3J+uRnfW3/DqAwRHT6W2MgLwdkCuoCPcDgI3fgbzKxc/C89\nhevzVXZXlBKujatxFu4h0eOUeo19H+1jM5MtVjrnOovo4rBvBYvDMJjQxU8oYbF0f4To2O+SzOmI\nb9G/cBQ1/dBRS7OyMIrHAac3x7vKRsN4PlqI/7Xnce7fRaJbH8Lfub5quW09l6E3RPz0swn98HaI\nhgn+6T9xHKr979fQwz/fTw/ppnlHUxiRxmFZuNd/XL2iITr2uyQGntEiNxKzMrIJ3XInOAwCT92J\nUdx0K0Uag1FWjCf/Qyyvn+iZ4xt8vhfiXYDG3fq9ti7qVtXD8+auMLg9hC/9AUYihu+VZ+0trJk7\nEE6yPVR1e3uvsxn9HzVNXJvySfvXM7i/yMcKZhM5bwrRcZdjZWTbWlp8xLlUXvEjHKWHCPzxF7Ve\n9ju0XVXPU77CyDEURiT1omHSn/4tntWLsdICRC66qsGfvu2W6DOIyit+jKO8hMATd0CihU5As6yq\n4ZlkoiqINHClyWfJIGvNICMdJfRznPzGlk2ha7qL07Pd5BfF2FuZIDbyApKde+Bd+kabWKZdX0v3\nV61SO6dD89kZ1LHvS3yvv4D344VgmURHnEv4kutIdu1ld2nVohOmEjn/iqp5ZY/+slZ/Fzr4nXRJ\nc7KmOEZS+41UUxiRlHIc2EXGPbfi/XghyfadiVx8DWZOR7vLSonoeZOJnnk+7q3rSXvxUbvLqRfX\nF/k4D+wi0b0vyR6nNPh8R3pFrnHvafC5UuWiblUB663dYXA4qbzshxiWif9f37xFhVRZuj+CAYzs\nYP98EUfRfrzvvoj/nfk4iwuJ9xlE5XdvIHHat5rlEG/lFT8iNnws7i8+I23+w7V6zrB2HioTFl+U\ntdAPNY1AYURSxv3ZMjLuvgnX7m1Ezr2MyAXT7N1RNdUMg4prf0GiSy987y7A89FCuyuqE0fhnqre\nKo8vJffe+TyZzidmJsMcZQxyhlJUZcON6eQjzWXw9q4wScsiPuzbVZtWrVqEs2CT3eU1OyUxk3XF\ncU7NctPOZ9+bvVVahHfJq/hfewHXnh0kO/UgPPEaYudcBP502+qqkcNB6Pv/VfV3YdFLeJa/VeNT\njswb0VDNVxRGpOHiMdLmPUzw4VkYiRih7/8XlVf/v9Z5ozKvn9Ctv8XypZH+/O9x7t5ud0W1Y5qk\nP/97jESc2BnjsFLwx/1/ElW9IjOaUa8IgM9pcF5nHwejJisLo2AYVE6+GYC0fz5pc3XNz/IDEUxg\nVEd7hmiMcAWej96Bvz+Oa8dGkjkdCZ9/BZEJV2C262RLTXXmSyP0b3dj+tNJf+G/a1xhM+TIvJEi\nhZEjFEakQRz7viTj3lvxLZxPslMeZbMeI3bOxXaX1ajMTnmErp9VtSHa479uERui+d6Zh3vjahJd\ne5PodVqDz7fV9PNhMpvTHCGGO8pSUGFqTexeNVTz6s4wAIlTRxA/dQTuDZ/g2rjaztKanWX7q1ZA\njerYtEM0zniUbpuW43/pKdxffAbBLCLf/g6Riddgdu7RpLWkgtmxOxU33I4RjxF47HaMihP/v8jy\nOOgddB3e26V17V9UXwojUj+WhXfxy2TefSOuLzcTGT2J0tufavETVWsrPuJcwhdMw7nvSwLP3Ve1\n2UYz5dz+Of6XnsTMzCF6zkUpWdH0XLxqe/xrXHua5QKpvhluTsl083FhlP3hJACVl98EQNo/Hm11\nG9jVV2XCZPXBKL2DLrqkNU1PpmEm6bjjM4a+/xzdtnyC5fESPWsCTLu5ah5Tc/yFqqX4kFGEJ12L\n8+Be0p85+UaJQ3I8xExYU9j4t7hoCVphP7rURm3uBXMiRqgU7/K3ce4rwHJ7iYy5hGTPAXg/eieF\nFTZ/4ck349qxEc+q9/G9M5/IBdPsLumbIpUEnroLI5mk/Ae/wnmw4ctvP0+m80Eyh9McIc5xNt/d\nTS/p7ucPpXHe2FXJ9f2CJHudSvSsCXg/egfPirer5iK0cR8XRolbcE5TTFy1LHL2baH7xmX4K0tJ\nuNx82X8k7YafAW4PPqcTaPkTOsOXfh/X9g141i7H9848IhdeddzHDWvn4aWCSj7eF6Zn12a4t0sT\nU8+I1J5l4friM/yvPItzXwGJrr0IX3o9yZ4D7K7MHk4XoZtmY2bm4F/weL3uVdHY0v/6IM7C3YQv\nvIrEaWc0+HyWBU/EuwNwk3tns/4QO7azn3SXwZu7wtW3bA9ffhOW20PaS09BNGxzhfZbeniIZnSn\nxp0vEizazcBl8+m/+g284XL29hxC/rnXsaffGbZsWtaoHE5CN9xe9XfhpSdxbV133IednuPBYcBH\ne+1fEt8cKIxIrRjFhfje+ltV74fhIHrORUTHTcZKC9pdmq2szHaEbroTgMCTv8EoOWhzRV/xLHsT\n7/K3SPQYQPiyH6bknJ+YmXxqZnCWo4Rhztpt8mQXn9Pg/C5+iqImKw5Uvema7ToSmTANR0khvrfn\n2VyhvWJJi48Lo3T2O+kVaJxOcm9lKf1XvsrA5QsIluznUOe+fDb2GgoGjiXhbb13U7Yycgj98A4w\nLdKfvPO480fSXQ5Oy3Kz7mCEQ5GkDVU2LwojcnLxGJ5V71dtw1y4h0Ref8KXXk+iz6AWPbabSon+\nQ6j83q04SosIPDkbEvbfc8K5fQPpL9yP6Q8QuukOcDX8duWmBU/EumFgcZNnZwqqbHyTulftyPrq\nzq8+fYYvvhozIwf/m39tVuGxqa0+FCWctBjV0YuR4v/LRjJBly2fMPiD/yVn/zbKsruw7pwr2Dx8\nItH0rJReq7lKDBhO+JLrcBbtJ33uPcedVzamow8LWHrAvtsoNBcKI3J8polr8xr8//cM7g0rsdIz\niJw3mejYS9t8b8jxRM+/gtiIc3FvXkPaXx+0dUKrUXKQ4KO3QzJOxY13YHbolpLzvpfMYYuVzvnO\nQ/R1tIwhjp5BN4Oy3aw+FGN3xeGQ6Esj/N0bMGKRquGaNmrhnqqJk99O8RBNxqFdDF7yN/I2LSfp\n9rB56IVsGDmFUHbnlF6nJYhccm3VjTY/W4p34T++cfzI8NiSfZrEqjAix7IsnLu34X/1Obwr3saI\nRYkNHkn4O9eT7Nrb7uqaL8MgdP0sEnn98S15Bd+rz9lTRzxK8NHbcZQcJDzlFuKnn52S00YsB0/F\nu+PC5Ab3rpScs6lMOrzM9+Uvv+odiY6eSKJrbzzL38S5db1dpdmmNGaybH+EHgEXp2Q2vNcMwJGI\n0XPdIk5b8U98FSXs7TmEz8bO4FDXlr1CpkEcTkI//DVmRg5pCx7HuX3DMYdzfU6G5PpYWxSjONq2\nh2oURqSao+gAvoUv4nvvnxilh4j3PZ3wZT8kPmRUSrr5Wz1fGuU/uY9ku06kvfwXPEtebdrrWxbp\nL9yPa/sGomdfQOSCK1N26ufjXdhnefmeaz+dHS1ro6Zvd/LRzuvgzV1hQvHDSy0dTiqnz8SwLNL/\n5w+QtH9orSm9tydMwqq6sWAqhmg6bv+MwYv/SqeCtVQGslk36goKBo4l6bZ/e3m7WZntCP3w12Am\nCTwxG6Py2LlW5+cFMPlqMnFbpTAiGBXleJa+ge+156tWyXTpSfiS64iNvLB1befeBKzMdpT/9H7M\nQCbp//MH3GuWN9GFLdLm/alqwmqvU6m49hcp+zS63fTz90QnOhlRrnfvTsk5m5LbYXB5jzTCSeuY\nuSOJ/kOIjpqIa9cWfO8usLHCpmVZFm/uCuM0YHwXf4PO5UjGGf72U1zwwm14w+Xs7jOCtaOvoiKr\nheyc2kQSp44gMulanIf2kf7svccM447vUfU3dsn+tj1UozDSlsVjuD/9sGpeyLb1mNm5RMZ/j+j4\n72Fl59pdXYtldsqj/Mf3gstF4PFf4/5sWeNe0LLw/+MRfO8uINGlF+X/fh+k6BOpacEfYj1J4uA/\nPAX4jZa5Wdik7mmkOQ3+VVBJ7Kg7pVZOuQUzkIn/5b/gKNpvY4VNZ3NZgu2hBCM7eMny1P8tIHho\nFxf9ZSYDVyygLKcr68+5gp0DRmG1xttApED4O9cTP2UYnk+X4H3vq/DbOb1qg77PimKUxlrm/69U\nUBhpi5IJXF/kk/avp/GsW4Hl8REdeSGRiTNIdulpd3WtQrLPQEK3/g4Mg8Bjv6q690ZjsCz8Cx7H\n/07VdvzlP3sQK5i61QpvJNuz1gwyxlnUrDc4q0m628HE7lXLfBft+WryrRXMovJ7t2JEw6T9/U82\nVth03tpd1f4Lu9a/V8Sz7E0mPflj2u3dwpahF/DaTQ8TylZvyEkdmT8SzCLtH4/g2rK2+tCYTj5M\nC5YdaLu9IwojbYll4f5sGZmzr8f70UJIxIkNGUX4uz8g0fd0cOjXIZXig86kfOYDWB4/6c/cjXfR\nS6m9QCJB2rw/43/rbyQ7dqfsZw9hZeSk7PSHLDePx/Lwk+Qn7oKUndcul/VIx2nAizsqsY7qJo+N\nvIh4v8F4Pl2CO/9DGytsfNGkxXt7wuR4HXyrfT16z+JR0p7/bwJz52A5HCyZPIvll/4/Ep6GDfe0\nFVZWe0I3zQbTIvD4HRilhwAYc/i+QG15VY3efdoIZ8Emgn/4KcGHZ+HYv4t4v8FVk1MHj2x9OyA2\nI4m+p1P+iz9hBbNJ/+uDpM37M8QbPlHNKDlI8IGf4nv3xaobFP7sIays9imouIppwe+ivSnDxQ/d\nu+jgaPnbdHfwOxnX2UdBKMEnB4+ahOtwUHn1z7CcLtJfuB+jvOX2ANVk6f4IFQmLCV38OB11m1Pk\nKNxDxr0/wrfkFRJ5/XjtpkfYMejcxim0FUsMGE54ys04Sg8ReOI3WIk4ndNc9Mtw8emhGGVtdKhG\ng3utnFF6iLSXnsKz7A0MyyJ2+kjCU27BtXVtzU+WlEh270vZbQ8T/NNt+Bb+A9f6T6i44VdVNwWr\nB9emfAJPzsZRVkR0xDgqrr8NfLXfzXJtLW5b/pazG6vcmQxMFtE/spPj/bacntPyQuz3eqWzcE+E\nv28LcUZ7T/VKkmTXXoQvv5G0Fx8j/bn7CP1oTqtcjnpkAu+F3erWk+Fes5z0Z+7GUVlOZPQkKq/6\nKaH9bXspakNELrgS5/aNeFctwnr2IfjuLYzr7GdzWTlv7g4ztVe63SU2OfWMtFbxGL43/0rW7Vfj\nXfo6yS69KPt/DxL6yX0ku/ayu7o2x+zQjdJfP01k3GRce3eQcc8t+P/vLxgVtd9S3bG3gPSnf0vw\nDz/FqCilYtqPqbh5dp2CSG1sM4K85upBlhVlRvwLWtNbcu+gm5EdvKwrjrOi8NgeqsiEaVUTDD9b\ninfJKzZV2HjWFMVYVxznzFwv3dJr+TnUTOL/19ME/3wbRixK6Nr/pPK628CjJbsNYhhUXH8bic49\nsV79K55lb3BRNz8+p8H/FVSQNJvvXcAbi8JIa2NZuPOXkPmb60hb8DiWy03F1T+j7NdPkzh1hN3V\ntW1eP5XTf0rZzD9gBXPwv/osWb+4nPS59+DctuH4u7ZGw1jrV5P+1F1k/uZavB+9Q7JLT8p//iei\n509N+af3ClzM9ZyCBVwX20SA1rf/xg39gziAZzaVH/tH3+Gg4ge/xEwLkDbvYRz7WsaW97X1P1tC\nAFzdp3afuo3yEoIP/QL/a8+TbN+Zsv96lNiYSxqzxLbFl0bo334H6UHSn/9vsrbmc2FXP4URs00u\n89UwTSvi3L2NtHkP4/58JZbTSeT8Kwhfcj1WurZvb04Sp51B6Z3P4l3yKt4PXsa77A28y97ATAtg\nZudiZnfA8qXh3L0d574vMS0TL5Do3o/wJdcRHzq6USYbJzD4i/sUig0fE+MF9LO+eXOv1iAv4OKi\nbu+AVMIAAB5QSURBVH5e3xXmzd3h6h1aAcycjlRe83MCT84m8PRvKZv1SKvY8G99cYz8ohgj2nk4\nNavm4TXXlrWkPzkbZ3EhsSHnUPH9X+nvSCMwO3XH8V8PkPzNrQQevZ1p//FnXiadf+6o5NzObWtS\nsMJIK2BUlOH//+3deXgURd7A8W93z5nJ5CQBAgEhnJH7CCCHCouLIh6gAiqyooiKuqAi6Cryvssh\nrjeurqw3uIqCEpVXUFcFkdNIgCCHXAESyUHIMZO5u94/BoKBAOHKJEN9nmeeSWZqJvVLT/f8uqq6\nKv1tzMvTUfQA3kvTKB/+AHrDS0JdtRpXnfEQ1XUhx0SICDvuP4/EPXA4hm0ZmH/8Ei13L2pRAYac\nPcEyZiv+lu0xtW5HabMO+Nr1vGDjGATwH2NLtmuxtAscYlAgvFoFjndHy0i++93NvJ0O+je0YDUc\nS+683fvj2bwG8+ql2OY/j3P05Do/fuSDXUdaRVqcZhJDIbB8swDrp2+ALii/YSzuq2+TV9pdQEq7\nbjj/MoXIt6aT8u/HGXjtbL4ugV8Pe0mNrXvjss6WTEbqsoAf8/J0rOlvo5aXEUhsTPnwB4PrkdTx\ng+dFQ1Xxp3bHn9r92GPuctTyMvSYBFBVEhLs+AqqP7bkbHxhaMo6LZFL9FLG+LaHff9tnFnjpksi\nmL/LyaK95dx+3Je087aH0XL3HBlvdcl5nVq/pm0r9vJzoZeOcSbaneLLTXGWYXtnJqaNP6FHx+EY\n+zT+1p1rsKYXL2/PqygvyCXi87eZ8t8Z/NTlCT7NtlxUyUi4H3PCluHXn4n637uwffgyCJ3ym8dT\n8j/vBS/VlYlI3WaJQI+rX2Nno8u1hnxtSCZBdzHO+ysmLo5LC29qZiPWpPLxHif5ruOuDDFbKBs/\nEz2mHtaFr1/4WXQvoA92OYFTjxXR9mwl6u93Ydr4E742XSiZ+rZMRGqY+9rReHpfQ9SBHcxdPYtf\n9h8m7/jPZRiTyUgdo+YfIDBzAlEvPoz2+17cfYdQMv0/uK8aHhZ921LN+q+WxCfGFCKFl/t9W7CH\n4YDVk4kwqNzVKhJ3QPCPzSXoxw0gFrEJlD0wC4wmIv/9P2gHdoWopmdvfYGHtQUe2sUa6VhVt6MQ\nmL9bRNTs8ahFebiG/IWyic+f18nzpGpSFJx3TMLTYyCtC7bz0k8zWbg1P9S1qjEyGakjFGcp1o9f\nJfrp0bBuOb5WHSl98t+U3zEJERUb6upJdYwAvjQ04TNjc6KFh796N5MgLr4R/AMbWbks0czGIi+f\n7i0/4flA09Y4xvwNxePC/tIjqLl7a76SZ8np03lxSwmaAuPbRp2wOq/iLMP2xjRsH76MiIikbMJz\nuK4bA6oWohpLqBrOOx/H3X0AnQ5t59qPn+bXgyWhrlWNkMlIbefzYPn6I6KfGIH1m4/Ro+NRJz1L\n2aOvEGjSKtS1k+ogHfjE0JylhibU01087N1EQ+E67evCkaIoTGgXTYxJ5Z0dZewpO3GmWV/XK3CO\n/CtqSRFRzz1UZ1pI5m4vo9CtM7K5jZSoyq2mxs1riJ42GnPG9/hadKDkqbcqj1uSQkczUH7X3yjo\neAWdD22jwUsTCBwuDHWtLjiZjNRWuo5p7TdEPzWKiE9eA6D85vsp+ft8lN4D5bgQ6awUCwP/NLZj\nhSGJJN3Jw95NxItzn56+LosxqTzSLgqfgNmbSiqt6nuUp/8wnKMeRXGUYH/ur2jZ20NQ0+rLKPTw\n1QEXze0GRqb8YXCuu5yI95/F/spjKGXFlN8wlrJHX5KrdNc2mgHtvqlsSB1Ai0O7sEwfh7Z/Z6hr\ndUHJq2lqIcP2DUR88hqG7O0IgxHXVcNxXzMKYYsKddXqBB8K5RhwKkbKMeBTVASgo6AiiBB+rASI\nED4i8YfVDKOn8uthLzPc7SjQTLQPHGKUbwcRXDwD5E6lR6KFwclWlux38VJWCY+2j0Y9LuH39LsO\nYTBie3c29ucn4hg3Df+laSGq8ck5/TovZpWgKvBIu2iMqhKcDDHjB2wLXkUtLsDfuEVwSYLGKaGu\nrnQymoH48U/y1twG3LXxAwKzx1N+z7TgRQphSCYjtYhhx0asS97D+OvPAHjS/oTrhrvRE5JCXLPa\np0QY2Kdb2Ccs7NOt5Agz+cJMjtmIQ6n+5XAmESBBuKknXDTSnTQVDprqZWE186hPF3yyx8m8nQ6E\nMHK9bw8DAjmyWfQ497S2s7vUz7e5biIMKuPb2k8YZ+G97GowmLC9MxP7y5NwDx6Na8joWjPOwhsQ\n/O+GYvKPdM+0jDYGlxH48OXgZIgGE64hf8F1zSg54L0OiDRpxA27g8es9fn7+leJfHUK7qtG4Lp+\nDBjDa0p+mYyEmhAYtv2C9cv3MO7IBMDXtivlN95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77xdXXXWV2Llzp7jxxhtFRkaGEEKI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\nkiRJkqSQksmIJEmSJEkh9f91A3K17bAELwAAAABJRU5ErkJggg==\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "draw_conditional_distplot('nr.employed',df)" ] }, { "cell_type": "code", "execution_count": 57, "metadata": {}, "outputs": [], "source": [ "# Feature scaling\n", "df['nr.employed'] = (df['nr.employed'] - df['nr.employed'].mean())/(df['nr.employed'].std())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## The output $y$\n", "Our output $y$ is weather a customer subscribed (yes) or not (no)." ] }, { "cell_type": "code", "execution_count": 58, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 58, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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k+ehHP5o//elP431KAGCCG/dHMENDQ+np6bn4dVdXVy5cuJDu7iufevbsaeM90oT1P5/r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\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "cnts = df.y.value_counts()\n", "cnts.plot(kind='bar')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As you can see our outcome is balanced, as many customers subscribed as customer did not subscribed. You can also see that our output is still a text, we will convert it so that a yes is indicated by a 1, a no indicated by a 0." ] }, { "cell_type": "code", "execution_count": 59, "metadata": {}, "outputs": [], "source": [ "# Conversion\n", "df['y'] = np.where(df['y'] == 'yes', 1,0)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Overview of new features\n", "We have created a lot of new features in this chapter, we will now look at them all and check weather we have left any features as text." ] }, { "cell_type": "code", "execution_count": 60, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "campaign float64\n", "pdays float64\n", "previous float64\n", "emp.var.rate float64\n", "cons.price.idx float64\n", "cons.conf.idx float64\n", "euribor3m float64\n", "nr.employed float64\n", "y int64\n", "job_admin. uint8\n", "job_blue-collar uint8\n", "job_entrepreneur uint8\n", "job_housemaid uint8\n", "job_management uint8\n", "job_retired uint8\n", "job_self-employed uint8\n", "job_services uint8\n", "job_student uint8\n", "job_technician uint8\n", "job_unemployed uint8\n", "job_unknown uint8\n", "age_old int64\n", "age_mid int64\n", "age_young int64\n", "marital_divorced uint8\n", "marital_married uint8\n", "marital_single uint8\n", "marital_unknown uint8\n", "education_basic.4y uint8\n", "education_basic.6y uint8\n", "education_basic.9y uint8\n", "education_high.school uint8\n", "education_illiterate uint8\n", "education_professional.course uint8\n", "education_university.degree uint8\n", "education_unknown uint8\n", "default_no uint8\n", "default_unknown uint8\n", "housing_no uint8\n", "housing_unknown uint8\n", "housing_yes uint8\n", "loan_no uint8\n", "loan_unknown uint8\n", "loan_yes uint8\n", "contact_cellular uint8\n", "contact_telephone uint8\n", "month_apr uint8\n", "month_aug uint8\n", "month_dec uint8\n", "month_jul uint8\n", "month_jun uint8\n", "month_mar uint8\n", "month_may uint8\n", "month_nov uint8\n", "month_oct uint8\n", "month_sep uint8\n", "day_of_week_fri uint8\n", "day_of_week_mon uint8\n", "day_of_week_thu uint8\n", "day_of_week_tue uint8\n", "day_of_week_wed uint8\n", "poutcome_failure uint8\n", "poutcome_nonexistent uint8\n", "poutcome_success uint8\n", "contacted_before int64\n", "dtype: object" ] }, "execution_count": 60, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pd.set_option('display.max_rows', 100)\n", "df.dtypes" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Saving the processed file\n", "Our data looks good, so we will now save our processed data so we have it ready for building our model in the next chapter." ] }, { "cell_type": "code", "execution_count": 61, "metadata": {}, "outputs": [], "source": [ "df.to_csv('processed_bank.csv')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Summary\n", "In this chapter you have seen an example data processing step. You have learned about dummy variables, feature scaling and how clever feature engineering can get better results out of the data. Data preprocessing actually makes up for the majority of time engineers spend when they build industry applications. It can easily make the difference between a well working system and one that does not work at all. Sometimes, it makes sense to come back to the feature engineering step after you have tried out some models. Often, insights from model building help you to do your feature engineering even better. And perhaps you have find something that could be done better in this chapter. If so, please file an issue on [GitHub](https://github.com/JannesKlaas/MLiFC) so we can improve. In the next chapter we will build a model on top of our processed data." ] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python [default]", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.4" } }, "nbformat": 4, "nbformat_minor": 2 }