{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Problem Statement\n", "\n", "Using the collected from existing customers, build a model that will help the marketing team identify potential customers who are relatively more likely to subscribe term deposit and thus increase their hit ratio." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Solution" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### General and domain knowledge assumption" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This problem statement relates to banking and financial sector. For time being let us forget about data set(though we know source and content) and assumes few things.\n", "\n", "What can be the people's age in data set? We do not know from which region of the world this data belongs. But since it is bancking and financial domain we would have verified & authenticated users having a min age of 18 or 21 years age mostly and upper limit would be around ~100.\n", "\n", "Knowing from the past experience of working with banking data set we know that their experience, salary, loan, cc expenditure are some inputs what we can expect to encounter in new data set and can heavily weight on the outcome of output variable which we need to predict.\n", "\n", "We also need to consider the profession of an individual whom we are considering as input data. A person with high income usually invest in more than one financial domain but still has a good change of being among the people appling for deposit.\n", "\n", "People with low and mid level of income range are very particular about investment and tend to trust banks more rather than investing in other places but as we do encounter outliers in our data set, there are certain inputs in this group of people that would still go and invest in places other than banks. Usually risk takers.\n", "\n", "Our final outcome would be predecition for an individual whether he would be interested in term deposit or not, but why are we takling too much about investment. Well, there is inverse relation between investment and term deposit. Its a contradiction, deposit is also as investment, but if an individual is investing more on other investment plans than naturally his investment in term deposit would be fairly less." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Existing Algorithms and approaches" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Since it is binary prediction problem based on number of input we already have few approaches in mind like NB Classifier, kNN. Logistic regression also seems a good fit for this. We have little more dimensions to consider 17+ we can even consider random fores with variable and random dimensions." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### General Imports" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "#Import Necessary Libraries\n", "\n", "# NumPy: For mathematical funcations, array, matrices operations\n", "import numpy as np \n", "\n", "# Graph: Plotting graphs and other visula tools\n", "import pandas as pd\n", "import seaborn as sns\n", "\n", "# sns.set_palette(\"muted\")\n", "# sns.set(color_codes=True)\n", "# sns.color_palette(\"colorblind\", 10)\n", "\n", "\n", "# color_palette = sns.color_palette()\n", "# To enable inline plotting graphs\n", "import matplotlib.pyplot as plt\n", "%matplotlib inline\n", "\n", "\n", "# palette = sns.color_palette(\"muted\")\n", "\n", "# sns.set_palette(palette)\n", "\n", "# sns.palplot(palette)\n", "\n", "flatui = [\"#9b59b6\", \"#3498db\", \"#95a5a6\", \"#e74c3c\", \"#34495e\", \"#2ecc71\"]\n", "\n", "sns.set_palette(flatui)\n", "\n", "sns.palplot(sns.color_palette())" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "# Custom terminal printer\n", "\n", "#Lets try to print unique values from object data type\n", "from IPython.display import Markdown, display\n", "\n", "def printTextAsMarkdown(title, content, color=None):\n", " if title is None:\n", " colorStr = \"{}\".format(color, content)\n", " else: \n", " colorStr = \"**{}** : {}\".format(color, title, content)\n", " \n", " display(Markdown(colorStr))" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Total Colums in dataframe: 17\n", "Columns list ['age', 'job', 'marital', 'education', 'default', 'balance', 'housing', 'loan', 'contact', 'day', 'month', 'duration', 'campaign', 'pdays', 'previous', 'poutcome', 'Target']\n", "***********************************************************************************************************************\n", "Columns Map {0: 'age', 1: 'job', 2: 'marital', 3: 'education', 4: 'default', 5: 'balance', 6: 'housing', 7: 'loan', 8: 'contact', 9: 'day', 10: 'month', 11: 'duration', 12: 'campaign', 13: 'pdays', 14: 'previous', 15: 'poutcome', 16: 'Target'}\n" ] } ], "source": [ "# Load data set\n", "# Import CSV data using pandas data frame\n", "df_original = pd.read_csv('bank-full.csv')\n", "\n", "# Print total columns\n", "print(\"Total Colums in dataframe: \", len(df_original.columns))\n", "\n", "# Prepare columns names\n", "df_original_columns = []\n", "for column in df_original.columns:\n", " df_original_columns.append(column)\n", "\n", "\n", " \n", "print(\"Columns list {}\".format(df_original_columns))\n", "print(\"***********************************************************************************************************************\")\n", "\n", "# Prepare mapping of column names for quick access\n", "df_original_columns_map = {}\n", "map_index: int = 0\n", "for column in df_original_columns:\n", " df_original_columns_map[map_index] = column\n", " map_index = map_index + 1\n", " \n", "print(\"Columns Map {}\".format(df_original_columns_map))\n", "\n", "# We have separated out columns and its mapping from data, at any point of time during data analysis or cleaning we \n", "# can directly refer or get data from either index or column identifier\n", "\n" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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agejobmaritaleducationdefaultbalancehousingloancontactdaymonthdurationcampaignpdayspreviouspoutcomeTarget
058managementmarriedtertiaryno2143yesnounknown5may2611-10unknownno
144techniciansinglesecondaryno29yesnounknown5may1511-10unknownno
233entrepreneurmarriedsecondaryno2yesyesunknown5may761-10unknownno
347blue-collarmarriedunknownno1506yesnounknown5may921-10unknownno
433unknownsingleunknownno1nonounknown5may1981-10unknownno
535managementmarriedtertiaryno231yesnounknown5may1391-10unknownno
628managementsingletertiaryno447yesyesunknown5may2171-10unknownno
742entrepreneurdivorcedtertiaryyes2yesnounknown5may3801-10unknownno
858retiredmarriedprimaryno121yesnounknown5may501-10unknownno
943techniciansinglesecondaryno593yesnounknown5may551-10unknownno
1041admin.divorcedsecondaryno270yesnounknown5may2221-10unknownno
1129admin.singlesecondaryno390yesnounknown5may1371-10unknownno
1253technicianmarriedsecondaryno6yesnounknown5may5171-10unknownno
1358technicianmarriedunknownno71yesnounknown5may711-10unknownno
1457servicesmarriedsecondaryno162yesnounknown5may1741-10unknownno
1551retiredmarriedprimaryno229yesnounknown5may3531-10unknownno
\n", "
" ], "text/plain": [ " age job marital education default balance housing loan \\\n", "0 58 management married tertiary no 2143 yes no \n", "1 44 technician single secondary no 29 yes no \n", "2 33 entrepreneur married secondary no 2 yes yes \n", "3 47 blue-collar married unknown no 1506 yes no \n", "4 33 unknown single unknown no 1 no no \n", "5 35 management married tertiary no 231 yes no \n", "6 28 management single tertiary no 447 yes yes \n", "7 42 entrepreneur divorced tertiary yes 2 yes no \n", "8 58 retired married primary no 121 yes no \n", "9 43 technician single secondary no 593 yes no \n", "10 41 admin. divorced secondary no 270 yes no \n", "11 29 admin. single secondary no 390 yes no \n", "12 53 technician married secondary no 6 yes no \n", "13 58 technician married unknown no 71 yes no \n", "14 57 services married secondary no 162 yes no \n", "15 51 retired married primary no 229 yes no \n", "\n", " contact day month duration campaign pdays previous poutcome Target \n", "0 unknown 5 may 261 1 -1 0 unknown no \n", "1 unknown 5 may 151 1 -1 0 unknown no \n", "2 unknown 5 may 76 1 -1 0 unknown no \n", "3 unknown 5 may 92 1 -1 0 unknown no \n", "4 unknown 5 may 198 1 -1 0 unknown no \n", "5 unknown 5 may 139 1 -1 0 unknown no \n", "6 unknown 5 may 217 1 -1 0 unknown no \n", "7 unknown 5 may 380 1 -1 0 unknown no \n", "8 unknown 5 may 50 1 -1 0 unknown no \n", "9 unknown 5 may 55 1 -1 0 unknown no \n", "10 unknown 5 may 222 1 -1 0 unknown no \n", "11 unknown 5 may 137 1 -1 0 unknown no \n", "12 unknown 5 may 517 1 -1 0 unknown no \n", "13 unknown 5 may 71 1 -1 0 unknown no \n", "14 unknown 5 may 174 1 -1 0 unknown no \n", "15 unknown 5 may 353 1 -1 0 unknown no " ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Data frame general analysis\n", "df_original.head(16)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "RangeIndex: 45211 entries, 0 to 45210\n", "Data columns (total 17 columns):\n", " # Column Non-Null Count Dtype \n", "--- ------ -------------- ----- \n", " 0 age 45211 non-null int64 \n", " 1 job 45211 non-null object\n", " 2 marital 45211 non-null object\n", " 3 education 45211 non-null object\n", " 4 default 45211 non-null object\n", " 5 balance 45211 non-null int64 \n", " 6 housing 45211 non-null object\n", " 7 loan 45211 non-null object\n", " 8 contact 45211 non-null object\n", " 9 day 45211 non-null int64 \n", " 10 month 45211 non-null object\n", " 11 duration 45211 non-null int64 \n", " 12 campaign 45211 non-null int64 \n", " 13 pdays 45211 non-null int64 \n", " 14 previous 45211 non-null int64 \n", " 15 poutcome 45211 non-null object\n", " 16 Target 45211 non-null object\n", "dtypes: int64(7), object(10)\n", "memory usage: 5.9+ MB\n" ] } ], "source": [ "# Dataframe information\n", "# Lets analyse data based on following conditions\n", "# 1. Check whether all rows x colums are loaded as given in question, all data must match before we start to even operate on it.\n", "# 2. Print shape of the data\n", "# 8. Check data types of each field\n", "# 3. Find presence of null or missing values.\n", "# 4. Visually inspect data and check presense of Outliers if there are any and see are \n", "# they enough to drop or need to consider during model building\n", "# 5. Print shape of the data\n", "# 6. Do we need to consider all data columns given in data set for model building\n", "# 7. Find Corr, median, mean, std deviation, min, max for columns.\n", "\n", "df_original.info()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We cannot use this raw data set as it is, as it container flelds which are of type object.\n", "This data is usually in the form of string and we should be able to get categories out of this obect type. " ] }, { "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", "balance int64\n", "housing object\n", "loan object\n", "contact object\n", "day int64\n", "month object\n", "duration int64\n", "campaign int64\n", "pdays int64\n", "previous int64\n", "poutcome object\n", "Target object\n", "dtype: object" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# data types\n", "\n", "df_original.dtypes\n", "\n", "# also part of info indicating which are int nd object type though redundant." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "False" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Check presence of any null values\n", "\n", "df_original.isnull().values.any()\n", "\n", "# This return `False` it mean we do no have any present of null values" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "False" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Check presence of missing value\n", " \n", "df_original.isna().values.any()\n", "\n", "# This return `False` it mean we do no have any present of missing values" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(45211, 17)" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Shape of the data\n", "\n", "df_original.shape\n", "\n", "# we have 45211 rows and 17 columns" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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agebalancedaydurationcampaignpdaysprevious
count45211.00000045211.00000045211.00000045211.00000045211.00000045211.00000045211.000000
mean40.9362101362.27205815.806419258.1630802.76384140.1978280.580323
std10.6187623044.7658298.322476257.5278123.098021100.1287462.303441
min18.000000-8019.0000001.0000000.0000001.000000-1.0000000.000000
25%33.00000072.0000008.000000103.0000001.000000-1.0000000.000000
50%39.000000448.00000016.000000180.0000002.000000-1.0000000.000000
75%48.0000001428.00000021.000000319.0000003.000000-1.0000000.000000
max95.000000102127.00000031.0000004918.00000063.000000871.000000275.000000
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" ], "text/plain": [ " age balance day duration campaign \\\n", "count 45211.000000 45211.000000 45211.000000 45211.000000 45211.000000 \n", "mean 40.936210 1362.272058 15.806419 258.163080 2.763841 \n", "std 10.618762 3044.765829 8.322476 257.527812 3.098021 \n", "min 18.000000 -8019.000000 1.000000 0.000000 1.000000 \n", "25% 33.000000 72.000000 8.000000 103.000000 1.000000 \n", "50% 39.000000 448.000000 16.000000 180.000000 2.000000 \n", "75% 48.000000 1428.000000 21.000000 319.000000 3.000000 \n", "max 95.000000 102127.000000 31.000000 4918.000000 63.000000 \n", "\n", " pdays previous \n", "count 45211.000000 45211.000000 \n", "mean 40.197828 0.580323 \n", "std 100.128746 2.303441 \n", "min -1.000000 0.000000 \n", "25% -1.000000 0.000000 \n", "50% -1.000000 0.000000 \n", "75% -1.000000 0.000000 \n", "max 871.000000 275.000000 " ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Check data loading and analyse data description\n", "\n", "df_original.describe()" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "object 10\n", "int64 7\n", "dtype: int64" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Print Different data types from dataframe and its reference type\n", "\n", "df_original.dtypes.value_counts()\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As we see, only 7 colums have been loaded and rest 10 are missing from here. These seems to be categorical column and hence we need to convert them in numerical columns.\n", "\n", "Before we move on to converting these values into categorical variable lets examine what are these values.\n", "This can be done by checking unique and unique count on that columns.\n" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/markdown": [ "**age** : has unique data in this range [18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 92, 93, 94, 95]" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "**************************************************************************************\n" ] }, { "data": { "text/markdown": [ "**balance** : has unique data in this range [-8019, -6847, -4057, -3372, -3313, -3058, -2827, -2712, -2604, -2282, -2122, -2093, -2082, -2049, -1980, -1968, -1965, -1944, -1941, -1884, -1882, -1854, -1818, -1781, -1779, -1746, -1737, -1730, -1725, -1701, -1680, -1668, -1664, -1661, -1655, -1636, -1629, -1621, -1613, -1601, -1598, -1586, -1547, -1545, -1531, -1500, -1493, -1490, -1489, -1485, -1480, -1459, -1455, -1451, -1445, -1415, -1414, -1400, -1386, -1385, -1379, -1361, -1350, -1336, -1329, -1322, -1317, -1313, -1312, -1310, -1300, -1272, -1270, -1249, -1246, -1232, -1224, -1217, -1212, -1206, -1202, -1196, -1193, -1185, -1176, -1168, -1164, -1161, -1157, -1148, -1139, -1137, -1136, -1129, -1124, -1122, -1112, -1110, -1105, -1099, -1092, -1091, -1089, -1085, -1083, -1080, -1076, -1053, -1050, -1049, -1042, -1041, -1040, -1038, -1036, -1034, -1027, -1026, -1019, -1014, -1013, -1011, -1007, -1006, -1002, -1001, -999, -998, -997, -995, -994, -988, -985, -983, -982, 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"**************************************************************************************\n" ] }, { "data": { "text/markdown": [ "**day** : has unique data in this range [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31]" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "**************************************************************************************\n" ] }, { "data": { "text/markdown": [ "**duration** : has unique data in this range [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 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"display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "**************************************************************************************\n" ] }, { "data": { "text/markdown": [ "**campaign** : has unique data in this range [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 41, 43, 44, 46, 50, 51, 55, 58, 63]" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "**************************************************************************************\n" ] }, { "data": { "text/markdown": [ "**pdays** : has unique data in this range [-1, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 12, 13, 14, 15, 17, 18, 19, 20, 21, 22, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260, 261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273, 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, 287, 288, 289, 290, 291, 292, 293, 294, 295, 296, 297, 298, 299, 300, 301, 302, 303, 304, 305, 306, 307, 308, 309, 310, 311, 312, 313, 314, 315, 316, 317, 318, 319, 320, 321, 322, 323, 324, 325, 326, 327, 328, 329, 330, 331, 332, 333, 334, 335, 336, 337, 338, 339, 340, 341, 342, 343, 344, 345, 346, 347, 348, 349, 350, 351, 352, 353, 354, 355, 356, 357, 358, 359, 360, 361, 362, 363, 364, 365, 366, 367, 368, 369, 370, 371, 372, 373, 374, 375, 376, 377, 378, 379, 380, 381, 382, 383, 384, 385, 386, 387, 388, 389, 390, 391, 392, 393, 394, 395, 396, 397, 398, 399, 401, 403, 404, 405, 407, 409, 410, 411, 412, 413, 414, 415, 416, 417, 419, 420, 421, 422, 424, 425, 426, 427, 428, 430, 431, 432, 433, 434, 435, 436, 437, 439, 440, 442, 444, 445, 446, 449, 450, 452, 454, 455, 456, 457, 458, 459, 460, 461, 462, 463, 464, 465, 466, 467, 469, 470, 472, 474, 475, 476, 477, 478, 479, 480, 481, 484, 485, 486, 489, 490, 491, 492, 493, 495, 500, 503, 504, 508, 511, 514, 515, 518, 520, 521, 524, 526, 528, 529, 530, 531, 532, 535, 536, 541, 542, 543, 544, 547, 550, 551, 553, 555, 557, 558, 561, 562, 578, 579, 585, 586, 587, 589, 592, 594, 595, 603, 616, 626, 633, 648, 651, 655, 656, 667, 670, 674, 680, 683, 686, 687, 690, 701, 717, 728, 745, 749, 756, 760, 761, 769, 771, 772, 774, 775, 776, 778, 779, 782, 784, 791, 792, 804, 805, 808, 826, 828, 831, 838, 842, 850, 854, 871]" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "**************************************************************************************\n" ] }, { "data": { "text/markdown": [ "**previous** : has unique data in this range [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 32, 35, 37, 38, 40, 41, 51, 55, 58, 275]" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "**************************************************************************************\n" ] } ], "source": [ "# Print unique for each column\n", "\n", "for name in df_original_columns: \n", " if df_original[name].dtype == np.int64:\n", " #Sorting for better understanding\n", " sortedCategories =sorted(df_original[name].unique().tolist())\n", " \n", " formattedText = \"has unique data in this range {}\".format(sortedCategories)\n", " \n", " printTextAsMarkdown(name, formattedText, color=\"red\")\n", " print(\"\\n**************************************************************************************\")" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/markdown": [ "**job** : has unique data in this range ['admin.', 'blue-collar', 'entrepreneur', 'housemaid', 'management', 'retired', 'self-employed', 'services', 'student', 'technician', 'unemployed', 'unknown']" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "**************************************************************************************\n" ] }, { "data": { "text/markdown": [ "**marital** : has unique data in this range ['divorced', 'married', 'single']" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "**************************************************************************************\n" ] }, { "data": { "text/markdown": [ "**education** : has unique data in this range ['primary', 'secondary', 'tertiary', 'unknown']" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "**************************************************************************************\n" ] }, { "data": { "text/markdown": [ "**default** : has unique data in this range ['no', 'yes']" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "**************************************************************************************\n" ] }, { "data": { "text/markdown": [ "**housing** : has unique data in this range ['no', 'yes']" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "**************************************************************************************\n" ] }, { "data": { "text/markdown": [ "**loan** : has unique data in this range ['no', 'yes']" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "**************************************************************************************\n" ] }, { "data": { "text/markdown": [ "**contact** : has unique data in this range ['cellular', 'telephone', 'unknown']" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "**************************************************************************************\n" ] }, { "data": { "text/markdown": [ "**month** : has unique data in this range ['apr', 'aug', 'dec', 'feb', 'jan', 'jul', 'jun', 'mar', 'may', 'nov', 'oct', 'sep']" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "**************************************************************************************\n" ] }, { "data": { "text/markdown": [ "**poutcome** : has unique data in this range ['failure', 'other', 'success', 'unknown']" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "**************************************************************************************\n" ] }, { "data": { "text/markdown": [ "**Target** : has unique data in this range ['no', 'yes']" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "**************************************************************************************\n" ] } ], "source": [ "# We are most interested in qunie values of object data column, so lets filter out only object data type\n", "# Priting ${df[name]} and its unique values\n", "\n", "# Container for object column type, later on while label encoding we need to convert only those column which are of type object\n", "objectColumns = []\n", "\n", "for name in df_original_columns: \n", " if df_original[name].dtype == np.object:\n", " \n", " #Sorting for better understanding\n", " sortedCategories =sorted(df_original[name].unique().tolist())\n", " \n", " formattedText = \"has unique data in this range {}\".format(sortedCategories)\n", " \n", " printTextAsMarkdown(name, formattedText, color=\"red\")\n", " \n", " objectColumns.append(name)\n", " print(\"\\n**************************************************************************************\")\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Let us examine this object data type\n", "\n", "- Data spread is very minimal mean less categories\n", "- There is some presense of invalid data i.e `unknown` in job, education, contact, poutcome(unknown) here indicated we don't know whether we have failed or success response from this person." ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [], "source": [ "# Copy for original dataframe\n", "\n", "df_main = df_original.copy()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### LabelEncoder and Caching" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [], "source": [ "\n", "\n", "from sklearn import preprocessing\n", "\n", "# Create empty of of label encoders for different columns\n", "# i wish to save each encoder corresponding to different colum title\n", "\n", "columnEncoders = {}\n", "\n", "for name in objectColumns:\n", " le = preprocessing.LabelEncoder()\n", " # Fit encoder to pandas column\n", " le.fit(df_main[name])\n", " # apply transformation and assign it to df\n", " df_main[name] = le.transform(df_main[name]) \n", " #put name and encoder in map \n", " columnEncoders[name] = le\n" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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agejobmaritaleducationdefaultbalancehousingloancontactdaymonthdurationcampaignpdayspreviouspoutcomeTarget
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" ], "text/plain": [ " age job marital education default balance housing loan contact \\\n", "0 58 4 1 2 0 2143 1 0 2 \n", "1 44 9 2 1 0 29 1 0 2 \n", "2 33 2 1 1 0 2 1 1 2 \n", "3 47 1 1 3 0 1506 1 0 2 \n", "4 33 11 2 3 0 1 0 0 2 \n", "\n", " day month duration campaign pdays previous poutcome Target \n", "0 5 8 261 1 -1 0 3 0 \n", "1 5 8 151 1 -1 0 3 0 \n", "2 5 8 76 1 -1 0 3 0 \n", "3 5 8 92 1 -1 0 3 0 \n", "4 5 8 198 1 -1 0 3 0 " ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Now lets revist basic operation on data frame\n", "\n", "df_main.head()\n", "\n", "# We can now see all data into numeric form" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "age int64\n", "job int64\n", "marital int64\n", "education int64\n", "default int64\n", "balance int64\n", "housing int64\n", "loan int64\n", "contact int64\n", "day int64\n", "month int64\n", "duration int64\n", "campaign int64\n", "pdays int64\n", "previous int64\n", "poutcome int64\n", "Target int64\n", "dtype: object" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Lets quickly print data types\n", "\n", "df_main.dtypes\n", "\n", "# Should print all int " ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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countmeanstdmin25%50%75%max
age45211.040.93621010.61876218.033.039.048.095.0
job45211.04.3397623.2726570.01.04.07.011.0
marital45211.01.1677250.6082300.01.01.02.02.0
education45211.01.2248130.7479970.01.01.02.03.0
default45211.00.0180270.1330490.00.00.00.01.0
balance45211.01362.2720583044.765829-8019.072.0448.01428.0102127.0
housing45211.00.5558380.4968780.00.01.01.01.0
loan45211.00.1602260.3668200.00.00.00.01.0
contact45211.00.6402420.8979510.00.00.02.02.0
day45211.015.8064198.3224761.08.016.021.031.0
month45211.05.5230143.0069110.03.06.08.011.0
duration45211.0258.163080257.5278120.0103.0180.0319.04918.0
campaign45211.02.7638413.0980211.01.02.03.063.0
pdays45211.040.197828100.128746-1.0-1.0-1.0-1.0871.0
previous45211.00.5803232.3034410.00.00.00.0275.0
poutcome45211.02.5599740.9890590.03.03.03.03.0
Target45211.00.1169850.3214060.00.00.00.01.0
\n", "
" ], "text/plain": [ " count mean std min 25% 50% 75% \\\n", "age 45211.0 40.936210 10.618762 18.0 33.0 39.0 48.0 \n", "job 45211.0 4.339762 3.272657 0.0 1.0 4.0 7.0 \n", "marital 45211.0 1.167725 0.608230 0.0 1.0 1.0 2.0 \n", "education 45211.0 1.224813 0.747997 0.0 1.0 1.0 2.0 \n", "default 45211.0 0.018027 0.133049 0.0 0.0 0.0 0.0 \n", "balance 45211.0 1362.272058 3044.765829 -8019.0 72.0 448.0 1428.0 \n", "housing 45211.0 0.555838 0.496878 0.0 0.0 1.0 1.0 \n", "loan 45211.0 0.160226 0.366820 0.0 0.0 0.0 0.0 \n", "contact 45211.0 0.640242 0.897951 0.0 0.0 0.0 2.0 \n", "day 45211.0 15.806419 8.322476 1.0 8.0 16.0 21.0 \n", "month 45211.0 5.523014 3.006911 0.0 3.0 6.0 8.0 \n", "duration 45211.0 258.163080 257.527812 0.0 103.0 180.0 319.0 \n", "campaign 45211.0 2.763841 3.098021 1.0 1.0 2.0 3.0 \n", "pdays 45211.0 40.197828 100.128746 -1.0 -1.0 -1.0 -1.0 \n", "previous 45211.0 0.580323 2.303441 0.0 0.0 0.0 0.0 \n", "poutcome 45211.0 2.559974 0.989059 0.0 3.0 3.0 3.0 \n", "Target 45211.0 0.116985 0.321406 0.0 0.0 0.0 0.0 \n", "\n", " max \n", "age 95.0 \n", "job 11.0 \n", "marital 2.0 \n", "education 3.0 \n", "default 1.0 \n", "balance 102127.0 \n", "housing 1.0 \n", "loan 1.0 \n", "contact 2.0 \n", "day 31.0 \n", "month 11.0 \n", "duration 4918.0 \n", "campaign 63.0 \n", "pdays 871.0 \n", "previous 275.0 \n", "poutcome 3.0 \n", "Target 1.0 " ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Let analyse more about data, \n", "\n", "#df_main.describe() difficult to view hence lets apply transpose() to visually see it better\n", "\n", "df_main.describe().transpose()\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Visual Analysis" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0 39922\n", "1 5289\n", "Name: Target, dtype: int64\n" ] }, { "data": { "text/plain": [ "" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Lets see response distribution for target column\n", "print(df_main['Target'].value_counts())\n", "\n", "sns.countplot(x='Target',data=df_original)\n", "\n", "# Here we have a kind of improper data, what ever model we build would \n", "# be dominated by column have strong hold on `NO` on output variable becuase we see that data come from the range where\n", "# most of the people have not opted for Term Deposit" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[,\n", " ,\n", " ,\n", " ],\n", " [,\n", " ,\n", " ,\n", " ],\n", " [,\n", " ,\n", " ,\n", " ],\n", " [,\n", " ,\n", " ,\n", " ],\n", " [,\n", " ,\n", " ,\n", " ]],\n", " dtype=object)" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Histogram\n", "df_main.hist(figsize=(15,15))" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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9G7Bt0rbPTrg9DHxsisc9AjxyiTXOW93N/cDUQT/dCo9BMTOW37SAnpOD/ORvd7H5czcRyQ4HXZaIoDNjU1rn8T5CYSO3KDvoUpKSlRth5TsX0nW8n5e+sT/ockQkQUGfwjqO9pJfnouFUnPGzVTK64u47I4G9jx5lDd+qqtRiaQCnaWaotydjiO9lNdf+lWl5nqY57qPr6bjaC8/f/h1yuoKqVxWMmuvpYuUiJyfevQpqv/0EKODUQoqgr3YyMUIRUK8+5NXkVeSzb9/cQc9JweCLklkXlPQp6iOI70AFKZh0EN8LZzb/uBaYqPj/ODzLzLUo1UuRYKioE9RHUf7MIP88vQMeogvZ/z+/3k941HntW1H6D01GHRJIvOSgj5FdRzppWRhAeFIev8nKl9cxPv/53WYGa9+7zAHf9FKdFRXphKZSzoYm6I6jvZSu7os6DJmRFldEVf/ynKO7jhF655O2g50U15fSMWSYuqvHqYgjf9qEUkHCvoUNNw3ykDHMBVLMufs0kh2mOU3LqB6RQlt+7vpONrL6cO97P9JMwUVuSzZUMPaTfWU1V36LCMReSsFfQrqOBo/EFvRUExfho1rF1XlU1SVz/KbFtDfMUReSS4n93Wy76lj7HnyKHVXVrJwXTnZ+TO/LLOmYsp8paBPQWdm3FQsybygP8NCRlFVPms2LeayOxoY6hnhwHPN7HziIG0Huln9rjpKFxYEXaZIRkjvI30ZquNoHwXlueQWp8fSBzMhrySHKz+4nM2fu5FIdojXf3SEUwe7gy5LJCMo6FNQx5FeypfMz7Hqsroirtq8jJLafN547gRdiYXdROTiaegmAOcaKx4ZGKO7pZ/lNy6Y46pSRzgrzNr3LObVHxxm3zPHufyXGyiszAu6LJG0pR59ijnV1A0ONRkytfJiRbLDrH/vEiI5Yfb8+BgjA2NBlySSthT0KebUgS4sZFQtn72FwNJFTkEW625bTHR0nH3PHCc2phOtRC6Ggj7FtB3opnxxEVm5GlUDKCjPZdUvLaTv1BC/+PreoMsRSUsK+hQyHhvnVFM3Navm97DNZJVLS6i7opJ9zxzn9R8dCbockbSjbmMK6TzWR3QkRs2q0qBLSTlLrq0mnBPmhUf2UlSVx5Jra4IuSSRtqEefQtoOdAGoRz8FCxm3/vaVVC0t4dmv7KJdc+xFkqYefQppO9BNQXmuphJOI5IT5rY/vJbvffYX/PAvtnP7H2+YtdfScgmSSRT0KaTtQBfVGrY5p/ySHN7/mevZ9r9e4od/sZ01m+opqZ27pRKm+gJQ+Euq09BNihjpH2OgY1jDNkkorMzj/Z+5nvyyXHb/6OjZIS8RmZqCPkX0tMWvq1qroE9KQXkuH/jT6ymqyeeNn7XQ9HwLseh40GWJpCQFfYroaRkgOz9CeUPmrEE/2/KKc7jsfUuou6KSk/u62PnEQbpbdSFykck0Rp8iulsHWLCuglDIgi7lbaY7MJkKLGQ0bKyhdFEBTT9v5fVtR6heWcria6rJL9OVq0RAPfqUMNw3ykjfGAvXVwRdStoqXVjI1b+ynLorKmk/2MNjn/opr3znoK5PK0KSQW9mt5vZfjNrMrP7ptifY2bfTOx/0cwaEttvM7MdZvZa4t93z2z5maG7JT7csHB9ecCVpLdwJETDxhqu+cgKFl1RSeNjB3j8D3/G4RdbcfegyxMJzHmHbswsDDwI3AY0A9vNbKu775nQ7G6gy91XmNkW4AHgTuA08J/cvcXMLgOeBBbN9C+R7npaB8jKi1C6qDDoUjJCXnE2t/3+NbTs7uCFR/by9JdfoXZNGbWry3SOgsxLyfTorwOa3P2Qu48CjwKbJ7XZDHw9cftxYJOZmbvvdPeWxPbdQK6Z5cxE4ZnC3eluGaB0QQFmqTc+n84Wrq/gQ1+4mZvvXk/3iX5e+e4hmn7eouEcmXeSCfpFwPEJ95t5e6/8bBt3jwI9wOQB548AO919ZPILmNk9ZtZoZo3t7e3J1p4RhrpHGBuKUqLro86KUMhYu2kxH/viLSxcX8HJ/V288p1DGXstXpGpJBP0U3UzJw94nrONma0nPpzzW1O9gLs/5O4b3H1DVVVVEiVljjPTAXUh7NmVU5DFshtqufz9Dfi4s+v7hzm+q11j9zIvJBP0zUD9hPt1QMt0bcwsApQAnYn7dcATwG+4+8FLLTjTdLcMkFOYRW7R/LkQeJBKagu4+sPLqWwo5mjjKXY/eZTBnrf9kSmSUZIJ+u3ASjNbambZwBZg66Q2W4G7Erc/Cjzj7m5mpcAPgE+7+/MzVXSm8HGnp3VAvfk5FskJs/rWOpbfvIDek4M88ennadndEXRZIrPmvEGfGHO/l/iMmb3AY+6+28zuN7MPJpo9DFSYWRPwKeDMFMx7gRXAZ8zslcRP9Yz/FmlqoHOY2Oi4xucDYGYsWFPOlR9cRnZ+hG1feInGxw7oQK1kpKTOjHX3bcC2Sds+O+H2MPCxKR73eeDzl1hjxjozf750gYI+KAXluXzo8zfxH/+4h1e+c5CDz7ew8eOrcXfNgpKMoSUQAtTdMkB+aQ7Z+VlBlzKvZeVGuOUTV7D85oW8+M/7eOZvXiGvJJua1WVUryglO0//m0h60yc4IOOxcXrbBjJyWeJUXhvnXOour2ThX9zMwedbePlbb3DkpTaOvNRGYWUuZfVFVDYUU1Cu9XMk/SjoA9LXPsR41CnRsE1KCYWMle9cRGw0xmDXMKeP9NHV3MfxV9o5vrOd/LIcFq4rp2Z1mYZ2JG0o6APSo/F5ILV7//lluSwuy2Xx1VWMDkU5fbiHU2900/R8K20Hull+80IKK9TDl9Sn1SsD0t0yQGFlLpGccNClSBKy8yIsXFfBlR9cxqpfWsRw3yi7th46e0BdJJUp6AMQi47T1z6kYZs0ZGZUryzlmo+sIK84m71PH6OruS/oskTOSUEfgL5Tg/i4xufTWVZuhHXvXUwoHOLJv2pksGs46JJEpqWgD0DPyUEwKK7JD7oUuQS5Rdmsf+9ihnpHeekb+4MuR2RaCvoA9LQOUFCeSyRb4/PprrAyj3W3LeHg8y306Hq1kqIU9HMsNhaLj8/XatgmU1zxgaWEskLsfKIp6FJEpqSgn2PtB3vwmFNSq2GbTJFXkqNevaQ0Bf0ca93XCUCxgj6jqFcvqUwnTM2xk3u7yC/LISv37W99Kp88JOeWV5LD6nfVs//Z49z0m+u1Po6kFPXo59B4bJy2A10atslQy26oJTY2zvGdp4IuReQt1O2YQ6cP9xIdiVGsA7EXJdX/4qlZVUZ+aQ5HXmpj+U0Lgy5H5Cz16OfQycT4vHr0mclCxpKNNRzf1U50RBcwkdShoJ9DrXs7KVlQoPXnM9jS62qJjsQ4vqs96FJEztLQzRwZH3fa9nex9PraoEuZF4Ia5qldU0ZOYRZHXjrJ0uv031pSg3r0c6TreB+jg1Fq15YHXYrMolA4RMPGGo7tPEVsTMM3khoU9HOkdW98fH7BGgV9pmvYWMvYUIyWPZ1BlyICKOjnzMl9nRRW5lFYmRd0KTLLFqwtJxQ2Wvd0BF2KCKCgnxPuzsl9XSzQsM28EMkJU7WilNbd6tFLalDQz4HulgGGe0epXZN5FwKXqS1cV87pwz2MDo4FXYqIZt3MhTPz53UgNjXNxgydBesq2PnEQU7u72Lx1dUz/vwiF0I9+jlwcm8n+aU5utDIPFK9spRwVohWHZCVFKAe/Sxzd1r3dlK7thwzC7ocmQXT/UVQvbKUlt06ICvBS6pHb2a3m9l+M2sys/um2J9jZt9M7H/RzBoS2yvM7Fkz6zezr8xs6emhu2WAwa4RFq6vCLoUmWML1pbTcbSXkX6N00uwztujN7Mw8CBwG9AMbDezre6+Z0Kzu4Eud19hZluAB4A7gWHgM8BliZ95p+X1eI9u0WUK+vlmbCgKDo2P7adiSTEAazYtDrgqmY+S6dFfBzS5+yF3HwUeBTZParMZ+Hri9uPAJjMzdx9w958TD/x5qeX10xRV5VFUrfH5+aaoOo9Q2HTVKQlcMkG/CDg+4X5zYtuUbdw9CvQASXdhzeweM2s0s8b29sxZDGo8Nk7r3k4WXlYZdCkSgFA4RFF1Pj0nB4MuRea5ZIJ+qiOIfhFtpuXuD7n7BnffUFVVlezDUt7pw72MDkZZqGGbeau4Np+BzmGio1r3RoKTTNA3A/UT7tcBLdO1MbMIUALM+3llJ14/DcDC9Zo/P1+V1OaDQ2+bevUSnGSCfjuw0syWmlk2sAXYOqnNVuCuxO2PAs+4e9I9+kzV8noH5UuKyCvOCboUCUhRdT5mCnoJ1nln3bh71MzuBZ4EwsDX3H23md0PNLr7VuBh4BEzayLek99y5vFmdgQoBrLN7EPAeyfN2MlI0ZEYbQe6WP++hqBLkQCFIyEKK/Po1Ti9BCipE6bcfRuwbdK2z064PQx8bJrHNlxCfWmrdV8n41HX/HmhuDaflt2dxKLjQZci85SWQJglRxvbiOSEWbBO4/PzXUltAT7u9LcPBV2KzFMK+lng487RHaeov7KKSHY46HIkYGfWONI0SwmKgn4WnGrqZqh7hCUba4IuRVJAJCdMfnkOvSd14pQEQ4uazYIj29sIhY3FV1cFdpFqSS0ltQW0HehmPDpOKKL+lcwtBf0MORPo7s4bPz1BcW0Bh37RGnBVkipKFhTQuqeTU03d1Oq6wTLH1LWYYYNdIwz3jVLRUBR0KZJCShcWgEHzq6eDLkXmIQX9DOs42gtAxWIFvbwpkh2muDqf5l2Zs5aTpA8F/Qxyd0419VBck092flbQ5UiKKasr5PThXoZ6RoIuReYZBf0M6tFFwOUcyuoKAWh+TcM3MrcU9DOodV8XkdwwlUuLgy5FUlBBRS65xdk071LQy9xS0M+QkYExOo72UrOylFBYb6u8nZlRd0UlJ15tx8fn/Zp/MoeUSDOk7UAXOBq2kXOqu6KS4b4xTh/pDboUmUcU9DNgPDpO2/5uShcWaEliOadFV1RiBkdeOhl0KTKPKOhnwL5njzMyMKaVKuW88opzqL+6mgPPNTOu1SxljijoL9HoUJSXv9VEcW0+ZfWFQZcjaWDtpnqGekY5uqMt6FJknlDQX6LXfnCY4d5Rll5Xg9lUl84VeatFV1ZRWJnH3qeOB12KzBMK+ksw2DXMaz84zNLraymqyg+6HEkToZCx+t11tOzuoKdVK1rK7FPQXyR35/l/2MN4dJwNd64KuhxJM6tvqcPCxr5n1KuX2aegv0h7f3yMo41tbNyympLagqDLkTSTX5ZLw8Ya9j51TL16mXUK+ovQcaSXF/55L/VXV3HZLzcEXY6kqRt+bQ3hSIhnH9yl68nKrFLQX6CBjiGe+tLL5BZlc8tvXaEDsHLRCiryeMd/vYzTh3p4+fE3gi5HMpiC/gIMdA7zg8+/xHD/GO/5/WvILc4OuiRJc0uvq2X1rXXs+t4hXvnuQcZj6tnLzNMVppLU2zbAjx5oZKh3hDvu20j1itKgS5IMccOvr6XjSC+N3zzA/meOs+yGWgqr8lj7niVBlyYZQkF/DmcuD3j6cC9v/OwEZsa69y6m81gfncf6Aq5OMkVWboQ1766n/WAPB/+jlV3fO0xOYRZdx/spX1JMWV0hZXWFusaBXDQF/TmMDkU52thG24FuCqvyWHNrHblFGq6R2VG1vISyukI6jvVx+nAP+59rJjb65lBOfnkOpQsKiY3FyCnMJrcoi9yibHIKs7jiA8uw0NuPF+17+hjuznjMiY7EiI3GWLKhFoBQxM4+j1ZczWwK+imMDUfZ+9QxdvzbG4xHx1l0eQVLrq3W/wwy6yI5YWpWllKzspTVt9bT1z5EV3Mf3Sf66Wrup/fkIF0n+hkbir7lcS9/q4n80hwiuREiOSFio+OMDkUZ7h0lNhrDJ6yK/PK3D77tdXMKsrCwkZ0XITs/8ZMXYcmGWvLLcsgvyyG3OJus3AjhrJAmIaQZBf0E/R1D7Hv6OHt/fIyRgTHK6gpZen0t+aVakVJmxpnhwGRYyCiuyae4Jp8l19a85Tli0XFG+scY7htlpH+M/PJcBrtGiI7EiI7EiJSEyMqLMNAxTDg7RCQrRDgnTCQrTP3V1QCJ59EJ4gwAAAgvSURBVBhluHeU4b4xTr3RxdhwlIHOYbqao8TGxjnSeGqKwiAcCRGKhAhHjFDidihsZ7eHIvHbWTlhFl5WSU5hVuIn+83bBVmEI+o8zQVzP/8FEMzsduDLQBj4qrv/5aT9OcA/AdcCHcCd7n4kse/TwN1ADPikuz95rtfasGGDNzY2XvhvcpH62gdpfvU0r287TE/rIADlS4qou7yS4hotayDpYc2mxVNuv5AvlsliYzHqrqxmsHuEwa5hhvtGiY7EGBuOceqNbmLRccaj44zHxhmP+pv3E7djY+NER2NwjogJZ4WI5ITP/pTXF539EsgpyianIEJ2fvx+dkGEnPwssguyyM6LTDlUNZ+Z2Q533zDVvvP26M0sDDwI3AY0A9vNbKu775nQ7G6gy91XmNkW4AHgTjNbB2wB1gMLgafMbJW7xy7tV0qOuzMeHWdsKMbYcPTsB7a3bZCOo32cPtRDb1s83HOLs1l8TRXVy0s1bVLSzqUE+nTCWWFa93S8eT8SIhwJkVOQRWFFbVLP4e4su3EhI/1jjAyMMdI/evZ28672s3+BjCX+7Tzed3a/x87dCc3Oj5BdkEVO4t/sgsQXQn4k8W/8yyGSHU78pZH46yP81r9EwhGLD8saGIDZ1LchcTux7cw+i7ezaba9ZV9Akhm6uQ5ocvdDAGb2KLAZmBj0m4E/S9x+HPiKxX+rzcCj7j4CHDazpsTz/WJmyn/T6UM9fP9zL+Ljzvi4xy/Vdo7PSWFVHhVLiln3viUsurySk3s7NO4oMsPMjMMvtL5teyhkLE4MIU3F3eN/EYzEiI7G/zKIjcSIjp75GaeoOp/RgfiXwuhglN6TA4wMjDHcO5a6a/1P+AKJfwlM/HIwll5fyy2fuGLGXzaZoF8ETFx5qRm4fro27h41sx6gIrH9hUmPXTT5BczsHuCexN1+M9ufVPVzpxLQFZ2np/dnenpvzk3vz0T/CPy3t2y5kPdn2hMvkgn6qbq5k/vK07VJ5rG4+0PAQ0nUEggza5xu7Ev0/pyL3ptz0/tzbjP1/iRzyLsZqJ9wvw5oma6NmUWAEqAzyceKiMgsSibotwMrzWypmWUTP7i6dVKbrcBdidsfBZ7x+HSercAWM8sxs6XASuClmSldRESScd6hm8SY+73Ak8SnV37N3Xeb2f1Ao7tvBR4GHkkcbO0k/mVAot1jxA/cRoHfmasZNzMsZYeVUoTen+npvTk3vT/nNiPvT1Lz6EVEJH3ptDQRkQynoBcRyXAK+gnMrN7MnjWzvWa228x+N7G93Mx+bGZvJP4tC7rWIJlZ2Mx2mtn3E/eXmtmLiffnm4mD9vOSmZWa2eNmti/xObpRn584M/v9xP9Xr5vZN8wsdz5/dszsa2Z2ysxen7Btys+Kxf2NmTWZ2atmds2FvJaC/q2iwP9w97XADcDvJJZxuA942t1XAk8n7s9nvwvsnXD/AeBLifeni/iSGPPVl4Efufsa4Eri79O8//yY2SLgk8AGd7+M+MSOM8ulzNfPzj8Ct0/aNt1n5Q7isxZXEj+59G8v6JXcXT/T/ADfJb7Gz35gQWLbAmB/0LUF+J7UJT6A7wa+T/ykuNNAJLH/RuDJoOsM6L0pBg6TmOQwYfu8//zw5tnz5cRn+30feN98/+wADcDr5/usAH8HfHyqdsn8qEc/DTNrAK4GXgRq3L0VIPHv9It0ZL6/Bv4IOLOYSAXQ7e5nFkifcpmLeWIZ0A78Q2Jo66tmVoA+P7j7CeB/A8eAVqAH2IE+O5NN91mZaimapN8rBf0UzKwQ+Bbwe+7eG3Q9qcLMPgCccvcdEzdP0XS+ztmNANcAf+vuVwMDzMNhmqkkxpo3A0uJr2RbQHw4YrL5+tk5n0v6/0xBP4mZZREP+X9x928nNreZ2YLE/gXAFFdjmBduBj5oZkeAR4kP3/w1UJpY+gLm9zIXzUCzu7+YuP848eDX5wfeAxx293Z3HwO+DdyEPjuTTfdZuaTlZBT0EySWVn4Y2OvuX5ywa+ISD3cRH7ufd9z90+5e5+4NxA+kPePuvwY8S3zpC5jf789J4LiZrU5s2kT8rHB9fuJDNjeYWX7i/7Mz740+O2813WdlK/Abidk3NwA9Z4Z4kqEzYycws3cAPwNe480x6D8hPk7/GLCY+Af2Y+7eGUiRKcLM3gX8gbt/wMyWEe/hlwM7gf/s8WsQzDtmdhXwVSAbOAT8JvEO1bz//JjZnwN3Ep/dthP4L8THmeflZ8fMvgG8i/hSxG3AnwLfYYrPSuLL8SvEZ+kMAr/p7klfik9BLyKS4TR0IyKS4RT0IiIZTkEvIpLhFPQiIhlOQS8ikuEU9CIiGU5BLyKS4RT0IhOY2XfMbEdi3fR7EtvuNrMDZvYTM/t7M/tKYnuVmX3LzLYnfm4OtnqRqemEKZEJzKw8cSZiHrCd+FK6zxNfs6YPeAbY5e73mtm/Av/X3X9uZouJL7G7NrDiRaYROX8TkXnlk2b24cTteuDXgefOLFlgZv8GrErsfw+wLn52OgDFZlbk7n1zWbDI+SjoRRIS6/e8B7jR3QfN7CfEL/AwXS89lGg7NDcVilwcjdGLvKkE6EqE/Bril5PMB24xs7LEcrofmdD+34F7z9xJLGgmknIU9CJv+hEQMbNXgc8BLwAngC8QX8H0KeJL6/Yk2n8S2JC4WPMe4BNzX7LI+elgrMh5mFmhu/cnevRPAF9z9yeCrkskWerRi5zfn5nZK8DrxC/+/Z2A6xG5IOrRi4hkOPXoRUQynIJeRCTDKehFRDKcgl5EJMMp6EVEMtz/B2rJYHqQNYnpAAAAAElFTkSuQmCC\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "sns.distplot(df_main['age'],kde=True)\n", "\n", "# We can conclude that data set has people raning from 20-60 and there are some outliers present as well\n", "# becuase the tail on right is spreading a little more" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['admin.' 'blue-collar' 'entrepreneur' 'housemaid' 'management' 'retired'\n", " 'self-employed' 'services' 'student' 'technician' 'unemployed' 'unknown']\n" ] }, { "data": { "image/png": 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5lnt+Y9PPDKPFJcVQsbOAkYExOur6Qljh8qJAD5Dx0QmqXr5AwfpMcirSb/r1EjPiiE+NpcUjwy6DvSN01vdRvGVxu2wmZyeQkBZLwxEFerhoP99De20vW3dXEJ8ae93zGcXJJKbH0XSyQzOUlsjirtrJdd5/4RyDXSPc9x+3BOT1zIyCdZm0nO7EObegi4jhqKVq6oOpeNPCxs+vMjNKtuVSf7CNyfFJoqIX1hepebXhhs+vfaB0UXUtZ6f3NxAd56Pyntlnc5kZRZuyOPdWCz3NV6Zmb0lQqYceAJfOdnPyhxdY+0DJosaH51K4IYvBrhH62iJ/HL2lqoO45Bgyy+bejGs+pdtyGR0c140vwsBw/yh177ay6u7CGw6h5VSkEZsYTfNJb83YClcK9Js0NjzOG395guTsBHb8m7UBfe2rHw6RPuzinKO5qpOC9ZlERS3+TKNoUxZR0abpi2Hg7JtTW1use+jGZzZRvigKN2TR03LF0/sThQsF+k0YH5ng1T87St+lQe795KZFzzufS2p+IokZcbSciuxA72sb5ErnMEUbFzfcclVMfDSFG7I0fTHE3KSj5pUG8tZkkFU6/xlX/poMLMq4fE5nVsGmQF+kwZ4Rjr9UR8upTu75jY0UrAvcUMtVZlNT/FpPd0X0drpXV7wWbrz5f6PSW3Lpaxukp8Wbe91EgtaaLvouDbLuQf+uO0TH+cgsTaG9rk9TGINMgb5AIwNjnP9JK0e/f56xkQke+ewO1sxze7mbUbghi+G+0YhedddS1UlydjypeYk3/VolW6c29dKwS+hcONBGdJzP7w3WYGrXzLGhcZqrIvtsM9wp0P003DfKubeaOfxP52g7001uRRrbPlZB/trMoL5v0fSskOYI3QZgctLRcqqTwo0LW+4/l5ScBDJLU2g4qumLoTA56bhwsI2SrTkL2+e/JJnoOB+1bzUHsTrRtMV5TE46LrzXRvOpTizKyF+TQdHmLOKTr593GwxJmfGkFyXTXNXB5p9buSTvGUid9X2MDo4HdPZP6S25HN9Tx8jAGHHJC1+kJIvXVt3FcN8oK+/IX9D3RfmiyC5Ppf7wJUaHxgN+vUmmqId+A+OjE7z+v4/RXNVJ3uoMtv+rSiruKliyML+qeHM2bTXdjI9OLOn7BkJLAMfPryrdloObdDQeVy99qdUdaCU6zvfB0NdC5K5KZ2J0kouHLgWhMgEF+pwmxibY98XDXDjYRvmOPCrvLiQuKTS9waKNWUyMTUbk/OumEx1klKSQmBYXsNfMqUgnMTOOugOtAXtNmd/kxCT1hy5Rsi2H6Djfgr8/JTeBlNyEqVsQSlAo0Odw5Lu1tJ7u4t5Pbv5gHDtU8tdlEuWziFucMTo4RtuZbkq2Lm65/1wsyqi4s5CmY+3arGsJtdV0Tw+3+HfT82uZGavuLqKlqpMr3ZqTHgwK9FlcOtvNiZfqWHN/MZUfKgp1OcTER5O3JiPiAr25qhM34SjZGvgPxIqdBUxOOOrfawv4a8vsPhhuWeR+PACrdhbiHJz/ic6ugkGBfo2rKz+TshK4/ZcCu/LzZhRtyqbrYj+DvSOhLsVvjcfaiUmIJq8yI+CvnbUilbTCJGrfaQn4a8v1JicmqT/YRum23EUNt1yVVpBETkUa59/WsEsw+BXoZrbLzM6YWa2ZPTvL8x8ysyNmNm5mvxj4MpfOse+fp+/SIB/65KZFbfMaLFc3tWo+Hhm9dOccTcfbKd6UveCNtPxhZqzaWUhbTbduorAEWqu7GO4fo3yBs1tms+ruIjov9kf02opwNe9vmpn5gOeAh4H1wFNmtv6aZg3ArwHfCHSBS2mob4RT+y5ScVcBhesDv/LzZmSVpZKYGUf9ocgYYuhq6Gewe4TiIAy3XFUxfc/W8++G9vS95tWGOf94xdXFRIG4HrLyznzMZ9S+rbOrQPOn67QDqHXO1TnnRoFvAbtnNnDO1TvnTgARva735A/rGR+dYOvPrwp1KdexKKP8tnyaTnQwNjwe6nLm1TQ9pXCx+5/7IzUvkdzKdM692az9toNoanZLG6W35BIdu/jhlqsSUuMo3pzN+XdaInpLi3Dkz+z+IqBxxnETcPti3szMngaeBigtDa/9p4f6Rjj944tU3FlARlF47ttctiOfU/su0nisfd6ZBqHe/7vxWDtZZakkZcQH9X3WPVjKG395gsZj7ZRuW/jcaJlf6+mp4ZaFLia6kVV3F/H60WO0VncFdNHZcudPD3229dqL+lh1zj3vnNvunNuekxO8nttinPjBBSZGJ9gWhr3zq/LWZBCfGsuFg+E97DLcN8qlsz1B7Z1fVXFnAcnZ8Rz/57qgv9dSGegcovlkBx0X+uhpuRLys4+6A23ExPsC+v9zxa25xCT4qNXF0YDyp4feBMzcfaoY8NTg1+jgGNX7G1h5ZwHpYdo7B4iKMspuy6P27RbGRycCcvobDHXvteImHSvvDFyPbi5R0VFserScd/++mraarqDvrRNMkxOTHH+pjqPfrWVy4qchnl6YxMo7C0hMD9ziLH9NjAd2uOWq6Fgf5TvyuXCwjbv+3Yaw/VmONP700A8BlWZWbmaxwJPAnuCWtbTOvtHE+MgEmx65+Zs7B1vZjnzGRyZoCuPZLud/0kpGcTKZJSlL8n5r7ishPiWG4y9Fbi/9SucQL/3fB3j/O+couy2fR/+v29n28xWsvCOf/o4hjn7vPO3ne5e8rqZj7YwMjFGxc/bbzN2MVXcXMTY0oZ0zA2jeHrpzbtzMngH2AT7gq865U2b2eeCwc26Pmd0GfA/IAH7OzP5f59yGoFYeIG7ScfrHDeRWppO9Mi3U5cyrcF0mcckxXHivlbLb8kJdznX624e4dKab7f969ZLdBzU6zseGXWW8/0/nuFzbQ+6qm79J91IaGRjjR390mIHOIT78qa0fXB/pbRkgKTOe7JVp1LzWyNk3m4lNjCatIGnJajv7VjMJabEUbw78bKWCdZkkZcZz7q1mv1afhvq6UCTwa8sz59xeYO81j31uxteHmBqKiThNx9vpuzTIrf+qMtSl+CUqOoqVdxRw9o0mhvpGSEhd+tPwG/1ijVwZA6DirsUtD1+sDR9dQfUrDbz51yf52B/eFTGn8OOjE+z/k/fpbb3Crmdvm/UCYWxCNOsfLOX4Dy5w+pUGtjxWTmKQLzbD1LWQxiOXWf/RFUT5grCWIMqo2FnAyb31IftZ9pplv1L01I8vkpgeR9mO4I/3Bsr6j5QyMTZJzWuN8zdeYud/0kpuZTopuTd/M4uFiE2M4Z6nN9HTPMCR755b0vdeLOccb/zVCdpqurn3N7fccLZHdJyPDR8pJcoXxelXGpkYC/7Om+ffbWFywgV1+4tVdxfhJhznteI3IJZ1oPe0DNB0vIO1D5bgC8JqxmDJKE6haFMW1fsbmBwPn6n/V7qG6Wro/2DBz1Ir2ZLDmvtLOPmDC1w6G/47U1a9XM+FA23c9uRqKu6c/4wmPiWWtfcXM9w3yoX3gr8F7dk3mskqS/XrvqGLlVmSQt7qDKperg+rn+VIFTkpFgSn9zcQ5TPWfjjyxt427CpjsHskrKYwttV0EeWzgCwPX6zbf3ktSdkJ7P+TI3Q3he/S8raaLg5+4wwrtuct6MYlaQVJFG3Kou1MNxffD16odzX001nfR+U9wd+cbuvulQx0DGtfngBYtoE+OjTOuTebQjYd7GaVbMkhNS+RUz+6GOpSgOmtcs/2UPmhooDufb5QsQnR7Pr97ViUsfcPD9LTHH43kx7sHua1/32MlJwE7v3kpgVfPF5xay5JmfG89TdVDAVps7ZT++rxxURRsTP410KKt+aQtSKF43vqmNTK0ZuybAP93JvNjA1NsP4jK0JdyqJYlLFh1wou1/Z8sMw+lFpOdeEmHZsfC/1t8tILk3n0szsA+OEfvEdbTVeIK/qp8dEJ9v/pEUaHxnngv2xb1AZwUb4oVt9bxNjQOG99uSrgC4+udA1z7s1mVt9bvCQXKs2MLbsr6G29Qn0YnXFGomUZ6FNTFS+SU5EWcVPcZlr74RLSCpJ4529PhfT2dOMjE7RWd5FdlrqkU+puJL0omUf+2+3ExEfzwz84SNPJjpCvuHTO8faXq2iv7eXe/7j5psamkzLj2f7Eahrev8yZf2kKYJVTY/tu0rHp0aVbl1G2I5+0giSOfu88kxMaS1+sZRnozSc76G29woaPRmbv/CpfjI+dv76B/stTC09CpbW6i4mxySVZ6r8QGUXJfOwP72LFrbnUH7zE6R83fDCtMhSOvlhL7dst3PILqygPwKyqjbvKKNyQxYF/qKa37cqCvneu3SFP7q3j1L6LrLyzgNS8pZupFBVlbH9iNd2N/Zz84YUle1+vWZaBXvWjiySkxVK+yFtphZPCDVlU3lPEyR/UheQi4HD/6PR9Q5NJzgr+3OiFik2M4YHf3sbKO/Ppbb3C0ReXfsWlc47D3znLke/WUnlPUcD2C7Io495PbiLKZ7zxf04EpGfberqLyfFJtjxeEYAKF6Z8Rz7lO/I58t1ausPw2kckWHaB3tnQR9PxdtZ/dEVETVW8kR2/tIaYhGj2/88jS3pHIzfpOPvm1OZK/ky7CxUzo3B9Ftt+voKEtFjO/EsTNa81Lsk2xBPjkxz4WjXHvn+eNfcX86H/sAmLCtwK2qSsBHb++gYu1/ZwfM/NbX0w9eHcSWZpCpmlS7Ntw7Xu/HfriY7z8dbzJ3WBdBG8kWgLcPIHF4iO87HuwcibqjiXhNQ4PvJfb2WwZ4R9XzjE6ODSDCs0n+qkr22QlXfkE58SuyTveTMS0uLY/Fg5K7bn0nmxnyMvnqfjQm/QxtYHOob4/md/wqkfXWTDrhXc/YmNAQ3zqyruKqTirgKOvFhLe93izj6cc5x7qxkzlmRTtbkkpsVxx6+s4/K5Hg58rTrk1z0izbIK9IGOIc6/28raD5cQnxz+AbQQeaszePC3t9HdNMCP/uhw0EO9p+UKFw9fJnNFCrmVkXNh2aKMki05bHl8JbGJ0dS81sTp/Q30Xx4MyOs75+huGqD61UaO7aljpH+Uhz59C3f+yvqghPlVd/3aBhLT4nj9z48t6iytraab3tZBynfkhfx3Y9XOQjY+UsbpfRc58t3akNYSaZZVoFe9XA/AxofLQlpHsBRvyeH+/7yVzot9HP1+HT2tC7tQ5q+uxn5O//giCamxVN5duGSbcAVSclY8Wx9fSfmOPHpbr/CdT7/JW39TRX/7wu5POtQ7Qk/zAE0nOzi9v4H3vn6GU/su0tt2haKNWfzCF+9hxa3B30QtLjmGD39qK4M9I+z9g4MLmp/e3z7EhYOXSC9KIm9N4G/ovVBmxu2/tJbV9xVz9MVajr5Yq566n/zanMsLhvpGqHmtcfqGCAmhLidoynfkk5afxMv/4yBVL9dTsC6TFbfc3J3ar7p6Wl79SiOJGXFs3LWCmPjI/RGyKKNoUzbZK9MY6BzmzOuNnPmXRnIr01lxax6ZJSkkZsYRHetjbHCcof5Reluu0NMyQE/z1J/h/p+eCcWnxJJZmkJGcTJZK1KI8kURl7x0NxrPW53BR373VvZ98TB7//tBdv3edpKybvyz3tN6her9DcQk+Ki8pyhsPpzNjLv//UYmxiZ5/4VzdFzso2BtRsRsuhYqkfvbuEBHvlvLxNgkW3cv/dX7pZZZmsKWx1dSf+gSrdVdtNf1Uro1h7w1GYu+EDzQMcTbXzlF0/F2UvMSWf9QaUA+JMJBXFIMWx6vYMvjKzn7RhMXD1/m0DfP3LB9elEyK7bnkV6UzJXOIZIy4olJCP2vU+H6LD76u9v58R+/zwu/9xa3PbGGdQ+WXjfcMzk+yaWzPdQdbCMhJZYNu1YQl7R0Hz7+iIoy7vvNzWSXp3LwG2e4VNPNqrsLSS8Mj7UO4Sj0P4FLoLt5gJpXG1n3QElY35EokKJjfazaWUj+2gzqDrRRd6CNxmPtFKzPJG+1/6fV3c0DVO2tp/btZizKuONX1uHzWVDHg0MlOSuBWz5eyS0fr2Swe5i+y0MMdg8zMTZJbGI0cUkxpBYkkZAa+zM92fn26V5qhRuy+PgX7ubtr1Txk787TdXL9RRvzianMp3x4QkGOoY4vedG2YIAAAmuSURBVL+BsaFxUvMTWfdASdieaZkZmx4pJ2dlGq/86RGqXq4nZ1Ua5TvyiQ2DD9Bwsyz+RQ5+o4boOB/bfiF87xcaLMlZCWx6pIy+tkGaTnTQcKSdhiPtNJ3ooHhLDjkVaWQUJRMTH02Uz7jSNUzvpUHaqrtoPNZOZ30fvpgoKu8pYsvulaTkJIZdgAVDYkb8kuw5HiypeYk8/JnbqHu3ldq3Wzj7ZjOn9//0/1t6YRLF9xWRVpAUNsMsN5K/NpNtH19F07F2mk520t0wQNltueStyYiI+peK5wO9+WQHjUfbue3J1ct2A30zI60gibSCJIb6Rmg/30tv2+ANhxXMILcyg9ueWsPqe4uW7b9dJDOz6SmNhUyMTdB3aZDYpBgSUmI5+0ZgtwtYCr7oKFZszyNnVTrnf9JC7TutXDrbw8o78pd8//1w5elAH+4f5c2/PklqXiIbdpWFupywkJAaR+m2XNY+UMpw3yjtF3rpa7vC+Ogkk2OTJGbGkZKbSGZpSsinr0ng+GJ8ZBSHZrFQoCWmx7Hx4TLaa3u5cOgSx1+6QE5FGiXbpnahXM48G+hX7wYz1DvC45+/U1fHZxGfGkvJlhwIsz1YROZjZuRWppO5IoWmEx00V3XyT59+ky0/t5JNj5Uv2993zwZ61cv1NB5t545/u47s8vC/+bOILFx0rI+y7Xnkr8mgo76P9184x+lXGtj8WDnrHvDOTCx/eTLQa15t4L2v17Di1lw27IrsHRVFZH7xKbE8+Nu30FrdxdEXa3nvH2s49v3zVH6oiDX3l5BRlHzDi/lrH/DGViCeC/QTL9Vx8JtnKNk6tWpSV8BFlo+CdZkUfHYHbTVdVL1cz6l9F6naW09GcTKJGXGkFyaRnJ0QttM0b5Zn/qv6Lw/y7j9U03DkMivvKODe39zsmd0URWRh8tdmkr82k8HeEc6/00Lj0XZaTnXRfLITmFocFp8aS1xyDPEpMfhifaTkJJCSm0hielzErrPwK9DNbBfwvwAf8GXn3BeueT4O+AfgVqATeMI5Vx/YUq/nnKPzQh+177RQ/UoDFmXc9tQaNj1aTlSE/g8RkcBJTItj0yPlbHqknKqXLzDQMcRA5zBXOocZ7h+lp3mA0cFxGo789DaOUdFGclYCKbkJJGcnTgd9AsnZCaTkJJCQFr6BP2+gm5kPeA54CGgCDpnZHufc6RnNPgF0O+dWmdmTwB8BTwSj4K7GfpqOTy14aT/fS9+lwak7zd+ez46n1sy7d4WILE/RsT7SC5NJL/zZ1eKT45MUbspmoH2I/vYh+tsH6W8fYqB9iIvvX2K4b/Rn2vtioj4I9+Scqb/jUmKJS4wmNjGG2KQYYhOisaipFdVRM1ZWT4xOMD46QUJaXFC2nPanh74DqHXO1QGY2beA3cDMQN8N/D/TX78A/IWZmQvCFmnNJzo4+I0zJGXFk1WWyubHyim7PV9zpkVkUaKio2YN+qvGhsenevbtQ/R3DNF/+adfd1zo/ZkN2vy18xMbWBeEC7H+BHoR0DjjuAm4fa42zrlxM+sFsoCOmY3M7Gng6enDATObe6lieMnmmv+WCKCal04k1q2al871dX/zpl5vzql7/gT6bINF1/a8/WmDc+554Hk/3jOsmNlh59z2UNexEKp56URi3ap56Sxl3f5MA2kCSmYcFwMtc7Uxs2ggDegKRIEiIuIffwL9EFBpZuVmFgs8Cey5ps0e4Fenv/5F4LVgjJ+LiMjc5h1ymR4TfwbYx9S0xa86506Z2eeBw865PcBXgK+ZWS1TPfMng1l0CETcMBGqeSlFYt2qeeksWd2mjrSIiDdoKaWIiEco0EVEPEKBfgNmtsvMzphZrZk9G+p6/GFmJWb2uplVm9kpM/utUNfkLzPzmdlRM/tBqGvxh5mlm9kLZlYz/e99Z6hr8oeZ/Zfpn40qM/ummYXdXSHM7KtmdtnMqmY8lmlm+83s3PTf/t8cdwnMUfOXpn8+TpjZ98wsPZg1KNDnMGPLg4eB9cBTZrY+tFX5ZRz4tHNuHXAH8J8ipG6A3wKqQ13EAvwv4EfOubXAFiKgdjMrAj4FbHfObWRqokM4TmL4O2DXNY89C7zqnKsEXp0+Did/x/U17wc2Ouc2A2eBzwSzAAX63D7Y8sA5Nwpc3fIgrDnnWp1zR6a/7mcqZIpCW9X8zKwYeBT4cqhr8YeZpQIfYmqGF865UedcT2ir8ls0kDC9ZiSR69eVhJxz7k2uX8uyG/j76a//HvjYkhY1j9lqds792Dk3Pn14gKl1PEGjQJ/bbFsehH0wzmRmZcA24L3QVuKXPwN+D5gMdSF+Wgm0A387PUz0ZTNLCnVR83HONQN/DDQArUCvc+7Hoa3Kb3nOuVaY6rgAuSGuZ6F+HXg5mG+gQJ+bX9sZhCszSwa+C/y2c64v1PXciJk9Blx2zr0f6loWIBq4BfhL59w24ArhNwRwnelx591AOVAIJJnZL4e2Ku8zs88yNRz69WC+jwJ9bv5seRCWzCyGqTD/unPuxVDX44edwONmVs/U0NaHzewfQ1vSvJqAJufc1bOfF5gK+HD3IHDBOdfunBsDXgTuCnFN/rpkZgUA039fDnE9fjGzXwUeA34p2CvoFehz82fLg7BjU/fc+wpQ7Zz7k1DX4w/n3Gecc8XOuTKm/p1fc86Fda/ROdcGNJrZmumHHuBnt5QOVw3AHWaWOP2z8gARcDF32swtRn4V+OcQ1uKX6ZsD/T7wuHNuMNjvp0Cfw/SFjKtbHlQD33HOnQptVX7ZCfxbpnq5x6b/PBLqojzqPwNfN7MTwFbgv4e4nnlNn1G8ABwBTjKVAWG3pN7Mvgm8C6wxsyYz+wTwBeAhMzvH1A13vnCj11hqc9T8F0AKsH/6d/GvglqDlv6LiHiDeugiIh6hQBcR8QgFuoiIRyjQRUQ8QoEuIuIRCnRZ1szsJzd47r5I2flRBBTossw55yJllaTIvBTosqyZ2YBN+dL0/uAnzeyJGU1Sp/exPm1mf2Vm+p2RsDXvTaJFloGPM7XScwuQDRwyszenn9vB1H74F4EfTbd9IRRFisxHvQ0RuBv4pnNuwjl3CXgDuG36uYPTe+JPAN+cbisSlhToIrNvlXzVtXtjaK8MCVsKdBF4E3hi+p6mOUzdiejg9HM7pnfcjAKeAN4OVZEi81Ggy3LngO8BJ4DjwGvA701vjwtTu+d9AagCLky3FQlL2m1Rli0zywKOOOdWhLoWkUBQD12WJTMrZKr3/cehrkUkUNRDFxHxCPXQRUQ8QoEuIuIRCnQREY9QoIuIeIQCXUTEI/5/OVmtswWP5/8AAAAASUVORK5CYII=\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "sns.distplot(df_main['job'])\n", "# Looking at the graph we see that are multiple groups present in here, we have seen this and converted to categorical variable\n", "# lets print it from cached map `col`umnEncoders`\n", "print(columnEncoders['job'].classes_)" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "management\n" ] }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Relation between age, job to Term deposit\n", "sns.barplot('job','age',hue='Target',data=df_main,ci=None)\n", "print(columnEncoders['job'].classes_[4])\n", "\n", "# We can see that management people have opted for term deposit more" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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Targetnoyes
job
admin.4540631
blue-collar9024708
entrepreneur1364123
housemaid1131109
management81571301
retired1748516
self-employed1392187
services3785369
student669269
technician6757840
unemployed1101202
unknown25434
\n", "
" ], "text/plain": [ "Target no yes\n", "job \n", "admin. 4540 631\n", "blue-collar 9024 708\n", "entrepreneur 1364 123\n", "housemaid 1131 109\n", "management 8157 1301\n", "retired 1748 516\n", "self-employed 1392 187\n", "services 3785 369\n", "student 669 269\n", "technician 6757 840\n", "unemployed 1101 202\n", "unknown 254 34" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Finding relation between job and term deposit\n", "pd.crosstab(df_original['job'], df_original['Target'])\n", "\n", "# We here conclude that management, technician, blue-collar are some of the categories that tend to apply for term deposit.\n", "# This conclusion is based on that fact that their earning is on higher side. A general human assumption." ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['primary' 'secondary' 'tertiary' 'unknown']\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Lets analyse education-wise which category tend to apply more for term deposit respectively\n", "sns.countplot(x='education', hue='Target',data=df_main)\n", "print(columnEncoders['education'].classes_)\n", "\n", "# Here we conclude that the order of applyterm deposit secondary > tertiary > primary > unknown" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['divorced' 'married' 'single']\n" ] }, { "data": { "image/png": "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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Lets analyse marital-status-wise which category tend to apply more for term deposit respectively\n", "\n", "sns.countplot(x='marital', hue='Target',data=df_main)#\n", "\n", "print(columnEncoders['marital'].classes_)\n", "\n", "# Here we conclude that the order of applyterm deposit is in married > single > divorced" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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monthapraugdecfebjanjuljunmarmaynovoctsep
Target
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yes577688100441142627546248925403323269
\n", "
" ], "text/plain": [ "month apr aug dec feb jan jul jun mar may nov oct sep\n", "Target \n", "no 2355 5559 114 2208 1261 6268 4795 229 12841 3567 415 310\n", "yes 577 688 100 441 142 627 546 248 925 403 323 269" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pd.crosstab(df_original['Target'], df_original['month'])\n", "\n", "# We see here\n", "# - May has higher success and failure of Target values\n", "# - August has the second highest acceptanec value\n", "# But in terms of percentage acceptance august has higher value than may." ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Deposit by months visual\n", "sns.countplot(x='month', hue='Target',data=df_original)" ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Checking if people who have given contants have got term deposit\n", "sns.countplot(x='contact', hue='Target',data=df_original)\n", "\n", "# This indicated who have registerd cellular contacts have slightly higer rate of applying for term deposit\n", "# This again tell us people who are working in higher job profile like management, technicians" ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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agejobmaritaleducationdefaultbalancehousingloancontactdaymonthdurationcampaignpdayspreviouspoutcomeTarget
05841202143102584.351-1030
144921029102582.521-1030
23321102112581.271-1030
34711301506102581.531-1030
433112301002583.301-1030
\n", "
" ], "text/plain": [ " age job marital education default balance housing loan contact \\\n", "0 58 4 1 2 0 2143 1 0 2 \n", "1 44 9 2 1 0 29 1 0 2 \n", "2 33 2 1 1 0 2 1 1 2 \n", "3 47 1 1 3 0 1506 1 0 2 \n", "4 33 11 2 3 0 1 0 0 2 \n", "\n", " day month duration campaign pdays previous poutcome Target \n", "0 5 8 4.35 1 -1 0 3 0 \n", "1 5 8 2.52 1 -1 0 3 0 \n", "2 5 8 1.27 1 -1 0 3 0 \n", "3 5 8 1.53 1 -1 0 3 0 \n", "4 5 8 3.30 1 -1 0 3 0 " ] }, "execution_count": 30, "metadata": {}, "output_type": "execute_result" } ], "source": [ "\n", "# Converting duration in dataset which is in seconds to minutes upto decimal ot 2 digits\n", "decimal_points = 2\n", "df_main['duration'] = df_main['duration'] / 60\n", "df_main['duration'] = df_main['duration'].apply(lambda x: round(x, decimal_points))\n", "\n", "df_main.head()" ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [], "source": [ "# Balance colums seems to be dominating all other values lets scale it\n", "from sklearn.preprocessing import MinMaxScaler\n", "\n", "scaler = MinMaxScaler()\n", "\n", "df_main[['balance']] = scaler.fit_transform(df_main[['balance']])" ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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agejobmaritaleducationdefaultbalancehousingloancontactdaymonthdurationcampaignpdayspreviouspoutcomeTarget
05841200.092259102584.351-1030
14492100.073067102582.521-1030
23321100.072822112581.271-1030
34711300.086476102581.531-1030
433112300.072812002583.301-1030
\n", "
" ], "text/plain": [ " age job marital education default balance housing loan contact \\\n", "0 58 4 1 2 0 0.092259 1 0 2 \n", "1 44 9 2 1 0 0.073067 1 0 2 \n", "2 33 2 1 1 0 0.072822 1 1 2 \n", "3 47 1 1 3 0 0.086476 1 0 2 \n", "4 33 11 2 3 0 0.072812 0 0 2 \n", "\n", " day month duration campaign pdays previous poutcome Target \n", "0 5 8 4.35 1 -1 0 3 0 \n", "1 5 8 2.52 1 -1 0 3 0 \n", "2 5 8 1.27 1 -1 0 3 0 \n", "3 5 8 1.53 1 -1 0 3 0 \n", "4 5 8 3.30 1 -1 0 3 0 " ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Looking closely at our columns we see some columns prefixed with char `p`, reading from the problem\n", "# statement we came to know that these field are some kind of indicated of previous analysis or campaign \n", "# like poutcome is not neccessarily should be part of train data model becuase this is not an attribute on input\n", "# but a conclusion on the previous analysis/campaign\n", "\n", "# So we can even build our data model removing these `p{x}` columns\n", "df_main.head()" ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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countmeanstdmin25%50%75%max
age45211.040.93621010.61876218.033.00000039.00000048.00000095.00
job45211.04.3397623.2726570.01.0000004.0000007.00000011.00
marital45211.01.1677250.6082300.01.0000001.0000002.0000002.00
education45211.01.2248130.7479970.01.0000001.0000002.0000003.00
default45211.00.0180270.1330490.00.0000000.0000000.0000001.00
balance45211.00.0851710.0276430.00.0734570.0768710.0857681.00
housing45211.00.5558380.4968780.00.0000001.0000001.0000001.00
loan45211.00.1602260.3668200.00.0000000.0000000.0000001.00
contact45211.00.6402420.8979510.00.0000000.0000002.0000002.00
day45211.015.8064198.3224761.08.00000016.00000021.00000031.00
month45211.05.5230143.0069110.03.0000006.0000008.00000011.00
duration45211.04.3027294.2921320.01.7200003.0000005.32000081.97
campaign45211.02.7638413.0980211.01.0000002.0000003.00000063.00
pdays45211.040.197828100.128746-1.0-1.000000-1.000000-1.000000871.00
previous45211.00.5803232.3034410.00.0000000.0000000.000000275.00
poutcome45211.02.5599740.9890590.03.0000003.0000003.0000003.00
Target45211.00.1169850.3214060.00.0000000.0000000.0000001.00
\n", "
" ], "text/plain": [ " count mean std min 25% 50% \\\n", "age 45211.0 40.936210 10.618762 18.0 33.000000 39.000000 \n", "job 45211.0 4.339762 3.272657 0.0 1.000000 4.000000 \n", "marital 45211.0 1.167725 0.608230 0.0 1.000000 1.000000 \n", "education 45211.0 1.224813 0.747997 0.0 1.000000 1.000000 \n", "default 45211.0 0.018027 0.133049 0.0 0.000000 0.000000 \n", "balance 45211.0 0.085171 0.027643 0.0 0.073457 0.076871 \n", "housing 45211.0 0.555838 0.496878 0.0 0.000000 1.000000 \n", "loan 45211.0 0.160226 0.366820 0.0 0.000000 0.000000 \n", "contact 45211.0 0.640242 0.897951 0.0 0.000000 0.000000 \n", "day 45211.0 15.806419 8.322476 1.0 8.000000 16.000000 \n", "month 45211.0 5.523014 3.006911 0.0 3.000000 6.000000 \n", "duration 45211.0 4.302729 4.292132 0.0 1.720000 3.000000 \n", "campaign 45211.0 2.763841 3.098021 1.0 1.000000 2.000000 \n", "pdays 45211.0 40.197828 100.128746 -1.0 -1.000000 -1.000000 \n", "previous 45211.0 0.580323 2.303441 0.0 0.000000 0.000000 \n", "poutcome 45211.0 2.559974 0.989059 0.0 3.000000 3.000000 \n", "Target 45211.0 0.116985 0.321406 0.0 0.000000 0.000000 \n", "\n", " 75% max \n", "age 48.000000 95.00 \n", "job 7.000000 11.00 \n", "marital 2.000000 2.00 \n", "education 2.000000 3.00 \n", "default 0.000000 1.00 \n", "balance 0.085768 1.00 \n", "housing 1.000000 1.00 \n", "loan 0.000000 1.00 \n", "contact 2.000000 2.00 \n", "day 21.000000 31.00 \n", "month 8.000000 11.00 \n", "duration 5.320000 81.97 \n", "campaign 3.000000 63.00 \n", "pdays -1.000000 871.00 \n", "previous 0.000000 275.00 \n", "poutcome 3.000000 3.00 \n", "Target 0.000000 1.00 " ] }, "execution_count": 33, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_main.describe().transpose()" ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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agejobmaritaleducationdefaultbalancehousingloancontactdaymonthdurationcampaignpdayspreviouspoutcomeTarget
age1.000000-0.021868-0.403240-0.106807-0.0178790.097783-0.185513-0.0156550.026221-0.009120-0.042357-0.0046480.004760-0.0237580.0012880.0073670.025155
job-0.0218681.0000000.0620450.166707-0.0068530.018232-0.125363-0.033004-0.0820630.022856-0.0928700.0047470.006839-0.024455-0.0009110.0110100.040438
marital-0.4032400.0620451.0000000.108576-0.0070230.002122-0.016096-0.046893-0.039201-0.005261-0.0069910.011849-0.0089940.0191720.014973-0.0168500.045588
education-0.1068070.1667070.1085761.000000-0.0107180.064514-0.090790-0.048574-0.1109280.022671-0.0573040.0019360.0062550.0000520.017570-0.0193610.066241
default-0.017879-0.006853-0.007023-0.0107181.000000-0.066745-0.0060250.0772340.0154040.0094240.011486-0.0100210.016822-0.029979-0.0183290.034898-0.022419
balance0.0977830.0182320.0021220.064514-0.0667451.000000-0.068768-0.084350-0.0272730.0045030.0197770.021564-0.0145780.0034350.016674-0.0209670.052838
housing-0.185513-0.125363-0.016096-0.090790-0.006025-0.0687681.0000000.0413230.188123-0.0279820.2714810.005075-0.0235990.1241780.037076-0.099971-0.139173
loan-0.015655-0.033004-0.046893-0.0485740.077234-0.0843500.0413231.000000-0.0108730.0113700.022145-0.0124080.009980-0.022754-0.0110430.015458-0.068185
contact0.026221-0.082063-0.039201-0.1109280.015404-0.0272730.188123-0.0108731.000000-0.0279360.361145-0.0208380.019614-0.244816-0.1478110.272214-0.148395
day-0.0091200.022856-0.0052610.0226710.0094240.004503-0.0279820.011370-0.0279361.000000-0.006028-0.0302090.162490-0.093044-0.0517100.083460-0.028348
month-0.042357-0.092870-0.006991-0.0573040.0114860.0197770.2714810.0221450.361145-0.0060281.0000000.006311-0.1100310.0330650.022727-0.033038-0.024471
duration-0.0046480.0047470.0118490.001936-0.0100210.0215640.005075-0.012408-0.020838-0.0302090.0063111.000000-0.084569-0.0015690.0012050.0109260.394521
campaign0.0047600.006839-0.0089940.0062550.016822-0.014578-0.0235990.0099800.0196140.162490-0.110031-0.0845691.000000-0.088628-0.0328550.101588-0.073172
pdays-0.023758-0.0244550.0191720.000052-0.0299790.0034350.124178-0.022754-0.244816-0.0930440.033065-0.001569-0.0886281.0000000.454820-0.8583620.103621
previous0.001288-0.0009110.0149730.017570-0.0183290.0166740.037076-0.011043-0.147811-0.0517100.0227270.001205-0.0328550.4548201.000000-0.4897520.093236
poutcome0.0073670.011010-0.016850-0.0193610.034898-0.020967-0.0999710.0154580.2722140.083460-0.0330380.0109260.101588-0.858362-0.4897521.000000-0.077840
Target0.0251550.0404380.0455880.066241-0.0224190.052838-0.139173-0.068185-0.148395-0.028348-0.0244710.394521-0.0731720.1036210.093236-0.0778401.000000
\n", "
" ], "text/plain": [ " age job marital education default balance \\\n", "age 1.000000 -0.021868 -0.403240 -0.106807 -0.017879 0.097783 \n", "job -0.021868 1.000000 0.062045 0.166707 -0.006853 0.018232 \n", "marital -0.403240 0.062045 1.000000 0.108576 -0.007023 0.002122 \n", "education -0.106807 0.166707 0.108576 1.000000 -0.010718 0.064514 \n", "default -0.017879 -0.006853 -0.007023 -0.010718 1.000000 -0.066745 \n", "balance 0.097783 0.018232 0.002122 0.064514 -0.066745 1.000000 \n", "housing -0.185513 -0.125363 -0.016096 -0.090790 -0.006025 -0.068768 \n", "loan -0.015655 -0.033004 -0.046893 -0.048574 0.077234 -0.084350 \n", "contact 0.026221 -0.082063 -0.039201 -0.110928 0.015404 -0.027273 \n", "day -0.009120 0.022856 -0.005261 0.022671 0.009424 0.004503 \n", "month -0.042357 -0.092870 -0.006991 -0.057304 0.011486 0.019777 \n", "duration -0.004648 0.004747 0.011849 0.001936 -0.010021 0.021564 \n", "campaign 0.004760 0.006839 -0.008994 0.006255 0.016822 -0.014578 \n", "pdays -0.023758 -0.024455 0.019172 0.000052 -0.029979 0.003435 \n", "previous 0.001288 -0.000911 0.014973 0.017570 -0.018329 0.016674 \n", "poutcome 0.007367 0.011010 -0.016850 -0.019361 0.034898 -0.020967 \n", "Target 0.025155 0.040438 0.045588 0.066241 -0.022419 0.052838 \n", "\n", " housing loan contact day month duration \\\n", "age -0.185513 -0.015655 0.026221 -0.009120 -0.042357 -0.004648 \n", "job -0.125363 -0.033004 -0.082063 0.022856 -0.092870 0.004747 \n", "marital -0.016096 -0.046893 -0.039201 -0.005261 -0.006991 0.011849 \n", "education -0.090790 -0.048574 -0.110928 0.022671 -0.057304 0.001936 \n", "default -0.006025 0.077234 0.015404 0.009424 0.011486 -0.010021 \n", "balance -0.068768 -0.084350 -0.027273 0.004503 0.019777 0.021564 \n", "housing 1.000000 0.041323 0.188123 -0.027982 0.271481 0.005075 \n", "loan 0.041323 1.000000 -0.010873 0.011370 0.022145 -0.012408 \n", "contact 0.188123 -0.010873 1.000000 -0.027936 0.361145 -0.020838 \n", "day -0.027982 0.011370 -0.027936 1.000000 -0.006028 -0.030209 \n", "month 0.271481 0.022145 0.361145 -0.006028 1.000000 0.006311 \n", "duration 0.005075 -0.012408 -0.020838 -0.030209 0.006311 1.000000 \n", "campaign -0.023599 0.009980 0.019614 0.162490 -0.110031 -0.084569 \n", "pdays 0.124178 -0.022754 -0.244816 -0.093044 0.033065 -0.001569 \n", "previous 0.037076 -0.011043 -0.147811 -0.051710 0.022727 0.001205 \n", "poutcome -0.099971 0.015458 0.272214 0.083460 -0.033038 0.010926 \n", "Target -0.139173 -0.068185 -0.148395 -0.028348 -0.024471 0.394521 \n", "\n", " campaign pdays previous poutcome Target \n", "age 0.004760 -0.023758 0.001288 0.007367 0.025155 \n", "job 0.006839 -0.024455 -0.000911 0.011010 0.040438 \n", "marital -0.008994 0.019172 0.014973 -0.016850 0.045588 \n", "education 0.006255 0.000052 0.017570 -0.019361 0.066241 \n", "default 0.016822 -0.029979 -0.018329 0.034898 -0.022419 \n", "balance -0.014578 0.003435 0.016674 -0.020967 0.052838 \n", "housing -0.023599 0.124178 0.037076 -0.099971 -0.139173 \n", "loan 0.009980 -0.022754 -0.011043 0.015458 -0.068185 \n", "contact 0.019614 -0.244816 -0.147811 0.272214 -0.148395 \n", "day 0.162490 -0.093044 -0.051710 0.083460 -0.028348 \n", "month -0.110031 0.033065 0.022727 -0.033038 -0.024471 \n", "duration -0.084569 -0.001569 0.001205 0.010926 0.394521 \n", "campaign 1.000000 -0.088628 -0.032855 0.101588 -0.073172 \n", "pdays -0.088628 1.000000 0.454820 -0.858362 0.103621 \n", "previous -0.032855 0.454820 1.000000 -0.489752 0.093236 \n", "poutcome 0.101588 -0.858362 -0.489752 1.000000 -0.077840 \n", "Target -0.073172 0.103621 0.093236 -0.077840 1.000000 " ] }, "execution_count": 34, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_main.corr()" ] }, { "cell_type": "code", "execution_count": 35, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 35, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "corr = df_main.corr()\n", "plt.figure(figsize = (12,12))\n", "\n", "sns.heatmap(corr, annot=True, linewidths=.5, cmap=\"YlGnBu\")#, cbar=False)\n", "\n", "# Here we can simply reduce poutcome, pdays, previous, campaig" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Training constants and general imports" ] }, { "cell_type": "code", "execution_count": 36, "metadata": {}, "outputs": [], "source": [ "\n", "# Training constants and general imports\n", "\n", "from sklearn.tree import DecisionTreeClassifier\n", "from sklearn.ensemble import RandomForestClassifier\n", "from sklearn.ensemble import AdaBoostClassifier\n", "from sklearn.ensemble import BaggingClassifier\n", "from sklearn.ensemble import GradientBoostingClassifier\n", "\n", "\n", "\n", "from sklearn import metrics\n", "from sklearn.metrics import classification_report\n", "\n", "# taking 70:30 training and test set\n", "test_size = 0.30 \n", "\n", "# Random number seeding for reapeatability of the code\n", "seed = 2 # spirit and opportunity Mars exploration rovers\n", "\n", "\n", "def isqrt(n):\n", " x = n\n", " y = (x + 1) // 2\n", " while y < x:\n", " x = y\n", " y = (x + n // x) // 2\n", " return x" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Data Preparation" ] }, { "cell_type": "code", "execution_count": 37, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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agejobmaritaleducationdefaultbalancehousingloancontactdaymonthcampaignpdaysprevious
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" ], "text/plain": [ " age job marital education default balance housing loan contact \\\n", "0 58 4 1 2 0 0.092259 1 0 2 \n", "1 44 9 2 1 0 0.073067 1 0 2 \n", "2 33 2 1 1 0 0.072822 1 1 2 \n", "3 47 1 1 3 0 0.086476 1 0 2 \n", "4 33 11 2 3 0 0.072812 0 0 2 \n", "\n", " day month campaign pdays previous \n", "0 5 8 1 -1 0 \n", "1 5 8 1 -1 0 \n", "2 5 8 1 -1 0 \n", "3 5 8 1 -1 0 \n", "4 5 8 1 -1 0 " ] }, "execution_count": 37, "metadata": {}, "output_type": "execute_result" } ], "source": [ "## Prepare input columns\n", "df_main_x = df_main.copy()\n", "\n", "# Colums we are dropping which are mostly related to previous campaign\n", "df_main_x = df_main_x.drop(['poutcome', 'duration', 'Target'], axis = 1) \n", "\n", "# df_main_x_ary = np.asarray(df_main_x)\n", "\n", "df_main_y = df_original['Target']\n", "\n", "\n", "df_main_x.head()\n" ] }, { "cell_type": "code", "execution_count": 38, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0 no\n", "1 no\n", "2 no\n", "3 no\n", "4 no\n", "Name: Target, dtype: object" ] }, "execution_count": 38, "metadata": {}, "output_type": "execute_result" } ], "source": [ "## Target column seperate\n", "df_main_y.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Training" ] }, { "cell_type": "code", "execution_count": 39, "metadata": {}, "outputs": [], "source": [ "\n", "from sklearn.model_selection import train_test_split\n", "\n", "X_train, X_test, y_train, y_test = \\\n", " train_test_split(np.asarray(df_main_x), np.asarray(df_main_y), test_size=test_size, random_state=seed) \n" ] }, { "cell_type": "code", "execution_count": 40, "metadata": {}, "outputs": [], "source": [ "# Holder class for data from different classifiers\n", "class EnsembleTechnique:\n", " def __init__(self, score, prediction, accuracy, confusion_matrix, classification_report, n_estimators):\n", " self.score = score\n", " self.prediction = prediction\n", " self.accuracy = accuracy\n", " self.confusion_matrix = confusion_matrix\n", " self.classification_report = classification_report\n", " self.n_estimators = n_estimators" ] }, { "cell_type": "code", "execution_count": 41, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Total rows 45211\n", "Limit till we find Nestimators 212\n" ] } ], "source": [ "rows = df_main_x.shape[0]\n", "print(\"Total rows {}\".format(rows))\n", "maxLimit = isqrt(rows)\n", "print(\"Limit till we find Nestimators {}\".format(maxLimit))\n", "\n", "# Result map to hold name and score for each model\n", "results = {}\n", " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Decision Tree using `entropy model`" ] }, { "cell_type": "code", "execution_count": 42, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Prediction: ['no' 'no' 'no' ... 'no' 'no' 'no']\n", "Score: 0.8261574756708936\n", "Accuracy 0.8261574756708936\n", "Confusion metrix\n", "[[10738 1261]\n", " [ 1097 468]]\n", " precision recall f1-score support\n", "\n", " no 0.91 0.89 0.90 11999\n", " yes 0.27 0.30 0.28 1565\n", "\n", " accuracy 0.83 13564\n", " macro avg 0.59 0.60 0.59 13564\n", "weighted avg 0.83 0.83 0.83 13564\n", "\n" ] } ], "source": [ "#Init\n", "decisionTreeClassifier = DecisionTreeClassifier(criterion = 'entropy')\n", "\n", "#fit data\n", "decisionTreeClassifier.fit(X_train, y_train)\n", "\n", "#Predict\n", "dtc_y_pred = decisionTreeClassifier.predict(X_test)\n", "\n", "# Model score\n", "dtc_model_score = decisionTreeClassifier.score(X_test , y_test)\n", "\n", "# Accuracy\n", "dtc_model_accuracy = metrics.accuracy_score(y_test, dtc_y_pred)\n", "\n", "\n", "print(\"Prediction: {}\".format(dtc_y_pred))\n", "print(\"Score: {}\".format(dtc_model_score))\n", "print(\"Accuracy {}\".format(dtc_model_accuracy))\n", "print(\"Confusion metrix\")\n", "print(metrics.confusion_matrix(y_test, dtc_y_pred))\n", "print(classification_report(y_test,dtc_y_pred))\n", "\n", "results['Decision Tree'] = dtc_model_score" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Random Forest Classifier" ] }, { "cell_type": "code", "execution_count": 43, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Prediction: ['no' 'no' 'no' ... 'no' 'no' 'no']\n", "Score: 0.8882335594219994\n", "Accuracy 0.8882335594219994\n", "n estimators : 128\n", "Confusion metrix\n", "[[11775 224]\n", " [ 1292 273]]\n", " precision recall f1-score support\n", "\n", " no 0.90 0.98 0.94 11999\n", " yes 0.55 0.17 0.26 1565\n", "\n", " accuracy 0.89 13564\n", " macro avg 0.73 0.58 0.60 13564\n", "weighted avg 0.86 0.89 0.86 13564\n", "\n" ] } ], "source": [ "\n", "# determining n_estimators here, we should proceed with approach 2^n\n", "# before stopping at the best outcome we should compare the result of previous outcome\n", "previous = EnsembleTechnique(0.0, 0.0, 0.0, None, None, 0)\n", "\n", "counter = 1;\n", "estimator = 0\n", "while(estimator previous.score:\n", " previous = EnsembleTechnique(rfc_model_score, rfc_y_pred, rfc_model_accuracy,\n", " metrics.confusion_matrix(y_test, rfc_y_pred),\n", " classification_report(y_test,rfc_y_pred), \n", " estimator)\n", " \n", " \n", " \n", " \n", "\n", "print(\"Prediction: {}\".format(previous.prediction))\n", "print(\"Score: {}\".format(previous.score))\n", "print(\"Accuracy {}\".format(previous.accuracy))\n", "print(\"n estimators : {}\".format(previous.n_estimators))\n", "print(\"Confusion metrix\")\n", "print(previous.confusion_matrix)\n", "print(previous.classification_report)\n", "\n", "results['Random Forest'] = previous.score" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Adaboost Classifier" ] }, { "cell_type": "code", "execution_count": 44, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Prediction: ['no' 'no' 'no' ... 'no' 'no' 'no']\n", "Score: 0.8882335594219994\n", "Accuracy 0.8882335594219994\n", "n estimators : 256\n", "Confusion metrix\n", "[[11847 152]\n", " [ 1364 201]]\n", " precision recall f1-score support\n", "\n", " no 0.90 0.99 0.94 11999\n", " yes 0.57 0.13 0.21 1565\n", "\n", " accuracy 0.89 13564\n", " macro avg 0.73 0.56 0.57 13564\n", "weighted avg 0.86 0.89 0.86 13564\n", "\n" ] } ], "source": [ "\n", "previous = EnsembleTechnique(0.0, 0.0, 0.0, None, None, 0)\n", "\n", "counter = 1;\n", "estimator = 0\n", "while(estimator previous.score:\n", " previous = EnsembleTechnique(abc_model_score, abc_y_pred, abc_model_accuracy,\n", " metrics.confusion_matrix(y_test, abc_y_pred),\n", " classification_report(y_test, abc_y_pred), \n", " estimator)\n", " \n", " \n", " \n", " \n", "\n", "print(\"Prediction: {}\".format(previous.prediction))\n", "print(\"Score: {}\".format(previous.score))\n", "print(\"Accuracy {}\".format(previous.accuracy))\n", "print(\"n estimators : {}\".format(previous.n_estimators))\n", "print(\"Confusion metrix\")\n", "print(previous.confusion_matrix)\n", "print(previous.classification_report)\n", "\n", "results['Adaboost Classifier'] = previous.score" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Bagging Classifier" ] }, { "cell_type": "code", "execution_count": 45, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.8773223237982896 for n estimator 2\n", "0.8773960483633146 for n estimator 4\n", "0.8824093187850192 for n estimator 8\n", "0.8850634031259216 for n estimator 16\n", "0.8866116189914479 for n estimator 32\n", "0.8859480979062223 for n estimator 64\n", "0.8872014155116484 for n estimator 128\n", "0.8858006487761723 for n estimator 256\n", "Prediction: ['no' 'no' 'no' ... 'no' 'no' 'no']\n", "Score: 0.8872014155116484\n", "Accuracy 0.8872014155116484\n", "n estimators : 128\n", "Confusion metrix\n", "[[11684 315]\n", " [ 1215 350]]\n", " precision recall f1-score support\n", "\n", " no 0.91 0.97 0.94 11999\n", " yes 0.53 0.22 0.31 1565\n", "\n", " accuracy 0.89 13564\n", " macro avg 0.72 0.60 0.63 13564\n", "weighted avg 0.86 0.89 0.87 13564\n", "\n" ] } ], "source": [ "\n", "\n", "previous = EnsembleTechnique(0.0, 0.0, 0.0, None, None, 0)\n", "\n", "counter = 1;\n", "estimator = 0\n", "while(estimator previous.score:\n", " previous = EnsembleTechnique(bc_model_score, bc_y_pred, bc_model_accuracy,\n", " metrics.confusion_matrix(y_test, bc_y_pred),\n", " classification_report(y_test, bc_y_pred), \n", " estimator) \n", "\n", " \n", " \n", " \n", "results['Bagging Classifier'] = previous.score \n", "print(\"Prediction: {}\".format(previous.prediction))\n", "print(\"Score: {}\".format(previous.score))\n", "print(\"Accuracy {}\".format(previous.accuracy))\n", "print(\"n estimators : {}\".format(previous.n_estimators))\n", "print(\"Confusion metrix\")\n", "print(previous.confusion_matrix)\n", "print(previous.classification_report)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Gradient Boost Classifier" ] }, { "cell_type": "code", "execution_count": 46, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/ashish/installed_apps/anaconda3/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n", " _warn_prf(average, modifier, msg_start, len(result))\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Prediction: ['no' 'no' 'no' ... 'no' 'no' 'no']\n", "Score: 0.8904452963727514\n", "Accuracy 0.8904452963727514\n", "n estimators : 256\n", "Confusion metrix\n", "[[11804 195]\n", " [ 1291 274]]\n", " precision recall f1-score support\n", "\n", " no 0.90 0.98 0.94 11999\n", " yes 0.58 0.18 0.27 1565\n", "\n", " accuracy 0.89 13564\n", " macro avg 0.74 0.58 0.61 13564\n", "weighted avg 0.86 0.89 0.86 13564\n", "\n" ] } ], "source": [ "\n", "previous = EnsembleTechnique(0.0, 0.0, 0.0, None, None, 0)\n", "\n", "counter = 1;\n", "estimator = 0\n", "while(estimator previous.score:\n", " previous = EnsembleTechnique(gb_model_score, gb_y_pred, gb_model_accuracy,\n", " metrics.confusion_matrix(y_test,gb_y_pred),\n", " classification_report(y_test, gb_y_pred), \n", " estimator) \n", "\n", "\n", "results['Gradient Boost Classifier'] = previous.score \n", "print(\"Prediction: {}\".format(previous.prediction))\n", "print(\"Score: {}\".format(previous.score))\n", "print(\"Accuracy {}\".format(previous.accuracy))\n", "print(\"n estimators : {}\".format(previous.n_estimators))\n", "print(\"Confusion metrix\")\n", "print(previous.confusion_matrix)\n", "print(previous.classification_report)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Analysis Result" ] }, { "cell_type": "code", "execution_count": 47, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Model score are \n", "{'Decision Tree': 0.8261574756708936, 'Random Forest': 0.8882335594219994, 'Adaboost Classifier': 0.8882335594219994, 'Bagging Classifier': 0.8872014155116484, 'Gradient Boost Classifier': 0.8904452963727514}\n" ] }, { "data": { "text/markdown": [ "**Gradient Boost Classifier** : has best score with accuracy **0.8904452963727514** " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "\n", "print(\"Model score are \")\n", "print(results)\n", "\n", "best_score = max(results, key=results.get);\n", "\n", "resultString = \" has best score with accuracy **{}** \".format(results[best_score])\n", "\n", "printTextAsMarkdown(best_score, resultString, color=\"blue\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Analysis Report" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Recall: Is the total number of \"Yes\" in the label column of the dataset. So how many \"Yes\" labels does our model detect.\n", "\n", "Precision: Means how sure is the prediction of our model that the actual label is a \"Yes\".\n", "\n", "Decision tree will not yield the best result as it is based on all the individual attributes where as Random Forest would random pic the colums and would aggregate result. **Random forest** would always perform best in accuracy. But **Gradient Boost Classifier** accuracy is incremental. Each new tree would be better than the previous one. In terms of performance, Random Forest beats Gradient Boost CLassifier due to parallel nature of execution where in Gradient Boost work sequentially.\n", "\n", "For the analysis it is clear that Gradient Boost Classifier give the best model score. We have also seen that number of trees also should be in certain range too less or many would not yield proper result." ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.7" } }, "nbformat": 4, "nbformat_minor": 2 }