diff --git a/22-05-21-Decision_Tree/Decision_Tree_Solution_2.ipynb b/22-05-21-Decision_Tree/Decision_Tree_Solution_2.ipynb new file mode 100644 index 0000000..469e932 --- /dev/null +++ b/22-05-21-Decision_Tree/Decision_Tree_Solution_2.ipynb @@ -0,0 +1,7666 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "view-in-github", + "colab_type": "text" + }, + "source": [ + "\"Open" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "_cell_guid": "e033a865-17a2-466a-8cde-a39cc6752deb", + "_uuid": "c9e2806b423b6385a8d876545ab668324b6bb451", + "id": "EbKrWuThjSNy" + }, + "source": [ + "# Bank Marketing Data - A Decision Tree Approach" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "_cell_guid": "459ad1ed-0e10-4dd1-a4f1-893ba5368175", + "_uuid": "08bb7d7c3677ca15a39a3569cef5d2071e9b015e", + "id": "AOo0cVvFjSN1" + }, + "source": [ + "## Aim:\n", + "The aim of this attempt is to predict if the client will subscribe (yes/no) to a term deposit, by building a classification model using Decision Tree.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "_cell_guid": "9e92f1a3-3e81-4ec6-a567-890827b0555c", + "_uuid": "e5338e8c3fa6dc8f410d8b868aa78cb54621780b", + "collapsed": true, + "id": "RRTIuD1KjSN2" + }, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "import plotly.express as px\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn import tree\n", + "from sklearn import metrics" + ] + }, + { + "cell_type": "code", + "source": [ + "" + ], + "metadata": { + "id": "OVLyUP7vey5e" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "link for DAta set https://raw.githubusercontent.com/Ramanand-Yadav/DataSet/main/bank.csv" + ], + "metadata": { + "id": "f88mo_5Bez6C" + } + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "_cell_guid": "52a87df3-04c2-4515-994a-9a3d0b85d3c3", + "_uuid": "9700cff3c818070f8f202bc964e2a5ffc8c72aa5", + "collapsed": true, + "colab": { + "base_uri": "https://localhost:8080/", + "height": 206 + }, + "id": "HX-ZHZpkjSN4", + "outputId": "cd4f8c6a-06e4-4420-ea9f-5f8fbaa0d116" + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "\n", + "
\n", + "
\n", + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
agejobmaritaleducationdefaultbalancehousingloancontactdaymonthdurationcampaignpdayspreviouspoutcomedeposit
059admin.marriedsecondaryno2343yesnounknown5may10421-10unknownyes
156admin.marriedsecondaryno45nonounknown5may14671-10unknownyes
241technicianmarriedsecondaryno1270yesnounknown5may13891-10unknownyes
355servicesmarriedsecondaryno2476yesnounknown5may5791-10unknownyes
454admin.marriedtertiaryno184nonounknown5may6732-10unknownyes
\n", + "
\n", + " \n", + " \n", + " \n", + "\n", + " \n", + "
\n", + "
\n", + " " + ], + "text/plain": [ + " age job marital education ... pdays previous poutcome deposit\n", + "0 59 admin. married secondary ... -1 0 unknown yes\n", + "1 56 admin. married secondary ... -1 0 unknown yes\n", + "2 41 technician married secondary ... -1 0 unknown yes\n", + "3 55 services married secondary ... -1 0 unknown yes\n", + "4 54 admin. married tertiary ... -1 0 unknown yes\n", + "\n", + "[5 rows x 17 columns]" + ] + }, + "metadata": {}, + "execution_count": 115 + } + ], + "source": [ + "# Load data file\n", + "bank=pd.read_csv('https://raw.githubusercontent.com/Ramanand-Yadav/DataSet/main/bank.csv')\n", + "bank.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "_cell_guid": "a727fd41-6e6e-424f-b65f-18c723b289a9", + "_uuid": "de29791be07bcac0927cccfb6e858044d1b882ca", + "id": "Pzth4W1YjSN5" + }, + "source": [ + "## Summay of data\n", + "\n", + "### Categorical Variables :\n", + "**[1] job :** admin,technician, services, management, retired, blue-collar, unemployed, entrepreneur,\n", + " housemaid, unknown, self-employed, student\n", + "
**[2] marital :** married, single, divorced\n", + "
**[3] education:** secondary, tertiary, primary, unknown\n", + "
**[4] default :** yes, no\n", + "
**[5] housing :** yes, no\n", + "
**[6] loan :** yes, no \n", + "
**[7] deposit :** yes, no ** (Dependent Variable)**\n", + "
**[8] contact :** unknown, cellular, telephone\n", + "
**[9] month :** jan, feb, mar, apr, may, jun, jul, aug, sep, oct, nov, dec\n", + "
**[10] poutcome:** unknown, other, failure, success\n", + "\n", + "### Numerical Variables:\n", + "**[1] age \n", + "
[2] balance\n", + "
[3] day\n", + "
[4] duration\n", + "
[5] campaign\n", + "
[6] pdays\n", + "
[7] previous**" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "_cell_guid": "142f9bce-cf27-45c6-a776-0ad6e8c660c9", + "_uuid": "f928d98a7f2c9fda54ee20d38b3d03101339e451", + "collapsed": true, + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "iyvtY2nEjSN6", + "outputId": "81c366db-d02c-4cb6-d1f1-b57947125bf9" + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "age 0\n", + "job 0\n", + "marital 0\n", + "education 0\n", + "default 0\n", + "balance 0\n", + "housing 0\n", + "loan 0\n", + "contact 0\n", + "day 0\n", + "month 0\n", + "duration 0\n", + "campaign 0\n", + "pdays 0\n", + "previous 0\n", + "poutcome 0\n", + "deposit 0\n", + "dtype: int64" + ] + }, + "metadata": {}, + "execution_count": 116 + } + ], + "source": [ + "# Check if the data set contains any null values - Nothing found!\n", + "bank[bank.isnull().any(axis=1)].count()" + ] + }, + { + "cell_type": "code", + "source": [ + "bank.shape" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "FjwUYyOTvzLz", + "outputId": "3f3b6bfe-5a53-4e96-d395-fb742f3759e5" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "(11162, 17)" + ] + }, + "metadata": {}, + "execution_count": 117 + } + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "_cell_guid": "a484827c-4ca4-42ce-895a-8228f4b86c31", + "_uuid": "8d03c75ae5115ebc97a26ac95408240177c7fc3f", + "collapsed": true, + "colab": { + "base_uri": "https://localhost:8080/", + "height": 300 + }, + "id": "65JuHMiUjSN6", + "outputId": "612d2540-25d2-479b-96be-5922f5e979b5" + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "\n", + "
\n", + "
\n", + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
agebalancedaydurationcampaignpdaysprevious
count11162.00000011162.00000011162.00000011162.00000011162.00000011162.00000011162.000000
mean41.2319481528.53852415.658036371.9938182.50842151.3304070.832557
std11.9133693225.4133268.420740347.1283862.722077108.7582822.292007
min18.000000-6847.0000001.0000002.0000001.000000-1.0000000.000000
25%32.000000122.0000008.000000138.0000001.000000-1.0000000.000000
50%39.000000550.00000015.000000255.0000002.000000-1.0000000.000000
75%49.0000001708.00000022.000000496.0000003.00000020.7500001.000000
max95.00000081204.00000031.0000003881.00000063.000000854.00000058.000000
\n", + "
\n", + " \n", + " \n", + " \n", + "\n", + " \n", + "
\n", + "
\n", + " " + ], + "text/plain": [ + " age balance ... pdays previous\n", + "count 11162.000000 11162.000000 ... 11162.000000 11162.000000\n", + "mean 41.231948 1528.538524 ... 51.330407 0.832557\n", + "std 11.913369 3225.413326 ... 108.758282 2.292007\n", + "min 18.000000 -6847.000000 ... -1.000000 0.000000\n", + "25% 32.000000 122.000000 ... -1.000000 0.000000\n", + "50% 39.000000 550.000000 ... -1.000000 0.000000\n", + "75% 49.000000 1708.000000 ... 20.750000 1.000000\n", + "max 95.000000 81204.000000 ... 854.000000 58.000000\n", + "\n", + "[8 rows x 7 columns]" + ] + }, + "metadata": {}, + "execution_count": 118 + } + ], + "source": [ + "bank.describe()" + ] + }, + { + "cell_type": "markdown", + "source": [ + "###Numerical Data" + ], + "metadata": { + "id": "tMHl09z3qdPL" + } + }, + { + "cell_type": "code", + "source": [ + "numCol = ['age','balance','day','duration','campaign','pdays','previous']\n", + "fig, axe = plt.subplots(7, 2, figsize=(18, 18))\n", + "for index, col in enumerate(numCol):\n", + " # print(index, type(col))\n", + " sns.boxplot(ax=axe[index, 0], x=bank[col])\n", + " sns.distplot(ax=axe[index, 1], x=bank[col])\n", + " axe[index, 0].set_title(col)\n", + " axe[index, 1].set_title(col)\n", + " " + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + }, + "id": "ZtUPYSCq6ztk", + "outputId": "47f0806d-2f87-4389-825e-c123640d36be" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning:\n", + "\n", + "`distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n", + "\n", + "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning:\n", + "\n", + "`distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n", + "\n", + "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning:\n", + "\n", + "`distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n", + "\n", + "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning:\n", + "\n", + "`distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n", + "\n", + "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning:\n", + "\n", + "`distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n", + "\n", + "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning:\n", + "\n", + "`distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n", + "\n", + "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning:\n", + "\n", + "`distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n", + "\n" + ] + }, + { + "output_type": "display_data", + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + } + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "_cell_guid": "f8e3b055-7d8e-4723-9e57-4a1e454ef194", + "_uuid": "d8cf9f54203152a76a7e8a03c3003644eb59525f", + "id": "wzMnrcIxjSN8" + }, + "source": [ + "### categorical data" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "_cell_guid": "353340bc-f258-4ca4-96ae-ce3dd6da9e4f", + "_uuid": "fa62612c19f6f510de6155f1f279d6aa812b9d22", + "collapsed": true, + "id": "zkei7Ke1jSN9" + }, + "outputs": [], + "source": [ + "# Make a copy for parsing\n", + "bank_data = bank.copy()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "_cell_guid": "2aaa6278-7388-461b-b0a6-7d0965872ca2", + "_uuid": "753b4f0d672266ac598f1afe35952245951ac8b1", + "id": "dHfxJN1YjSN9" + }, + "source": [ + "#### job " + ] + }, + { + "cell_type": "code", + "source": [ + "px.histogram(bank_data['job'])" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 542 + }, + "id": "jS9J9AbCFziZ", + "outputId": "55c83307-5d7a-4fe9-f376-1c4ce8f1793a" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "
\n", + "
\n", + "\n", + "" + ] + }, + "metadata": {} + } + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "_cell_guid": "29ebb749-1a14-4ea7-85bb-e148945ed328", + "_uuid": "757d1a8bfab2c64c59bcd78a12eb43721e198758", + "collapsed": true, + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "u_KCWvYGjSN9", + "outputId": "21d37870-e34c-4d81-ee61-3e073660f07d" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "management : 1301\n", + "blue-collar : 708\n", + "technician : 840\n", + "admin. : 631\n", + "services : 369\n", + "retired : 516\n", + "self-employed : 187\n", + "student : 269\n", + "unemployed : 202\n", + "entrepreneur : 123\n", + "housemaid : 109\n", + "unknown : 34\n" + ] + } + ], + "source": [ + "# Explore People who made a deposit Vs Job category\n", + "jobs = ['management','blue-collar','technician','admin.','services','retired','self-employed','student',\\\n", + " 'unemployed','entrepreneur','housemaid','unknown']\n", + "\n", + "for j in jobs:\n", + " print(\"{:} : {:}\". format(j, len(bank_data[(bank_data.deposit == \"yes\") & (bank_data.job ==j)])))" + ] + }, + { + "cell_type": "code", + "source": [ + "px.histogram(bank_data, x='job', color = 'deposit')" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 542 + }, + "id": "HaUUtoLdHAaR", + "outputId": "608a7bda-164e-421d-904c-c3eea34f4b18" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "
\n", + "
\n", + "\n", + "" + ] + }, + "metadata": {} + } + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "_cell_guid": "cf54d19e-ab5b-4b16-8c17-a1d441675e4e", + "_uuid": "6c5d8eb98fc23a74d8e235d1816758e760ae7222", + "collapsed": true, + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "WyOwz-VvjSN-", + "outputId": "ea9207fb-0b49-4612-b6f3-89c265a84822" + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "management 2566\n", + "blue-collar 1944\n", + "technician 1823\n", + "admin. 1334\n", + "services 923\n", + "retired 778\n", + "self-employed 405\n", + "student 360\n", + "unemployed 357\n", + "entrepreneur 328\n", + "housemaid 274\n", + "unknown 70\n", + "Name: job, dtype: int64" + ] + }, + "metadata": {}, + "execution_count": 141 + } + ], + "source": [ + "# Different types of job categories and their counts\n", + "bank_data.job.value_counts()" + ] + }, + { + "cell_type": "code", + "source": [ + "" + ], + "metadata": { + "id": "htUuo6d9D3ta" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "_cell_guid": "fdcd5741-12b8-4b48-99b6-bd6e834f0da9", + "_uuid": "58840f83e0b4fb1ea28c8aaebc109359f21e86e1", + "collapsed": true, + "id": "kSNoXW42jSN-" + }, + "outputs": [], + "source": [ + "# Combine similar jobs into categiroes\n", + "bank_data['job'] = bank_data['job'].replace(['management', 'admin.'], 'white-collar')\n", + "bank_data['job'] = bank_data['job'].replace(['services','housemaid'], 'pink-collar')\n", + "bank_data['job'] = bank_data['job'].replace(['retired', 'student', 'unemployed', 'unknown'], 'other')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "_cell_guid": "6c44a280-708c-472e-8a9d-454130ebd29f", + "_uuid": "60184753637b373ef973a5c38cd15bf51a1e58ac", + "collapsed": true, + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "kFNhQiQ5jSN_", + "outputId": "638aae7f-0c6a-4777-fa48-53cdd5fb1904" + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "white-collar 3900\n", + "blue-collar 1944\n", + "technician 1823\n", + "other 1565\n", + "pink-collar 1197\n", + "self-employed 405\n", + "entrepreneur 328\n", + "Name: job, dtype: int64" + ] + }, + "metadata": {}, + "execution_count": 143 + } + ], + "source": [ + "# New value counts\n", + "bank_data.job.value_counts()" + ] + }, + { + "cell_type": "code", + "source": [ + "px.histogram(bank_data, x='job')" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 542 + }, + "id": "F30bzEG8HZ3F", + "outputId": "fced5260-7a5e-40f9-9341-baf4ab27b9e3" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "
\n", + "
\n", + "\n", + "" + ] + }, + "metadata": {} + } + ] + }, + { + "cell_type": "code", + "source": [ + "px.histogram(bank_data, x='job', color = 'deposit')" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 542 + }, + "id": "nmv81FWzHm3U", + "outputId": "e31676f0-fc0b-4ab4-969a-dd9d09377618" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "
\n", + "
\n", + "\n", + "" + ] + }, + "metadata": {} + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "_cell_guid": "c446c4b5-d0ce-48e1-8ceb-6431e49ad97e", + "_uuid": "36fcf9b5b0dc9374f7540f918be453ece6eec86e", + "id": "7tdtMFRCjSN_" + }, + "source": [ + "#### poutcome" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "_cell_guid": "6516214b-7dab-4c4b-9abe-dd53db689a79", + "_uuid": "791b50d069b7a06e57095974f5c9c0e0d0c9b55d", + "collapsed": true, + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "6Jg8ioI6jSN_", + "outputId": "7eb6e3d1-d1a2-40c1-b82e-3b11e943de93" + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "unknown 8326\n", + "failure 1228\n", + "success 1071\n", + "other 537\n", + "Name: poutcome, dtype: int64" + ] + }, + "metadata": {}, + "execution_count": 146 + } + ], + "source": [ + "bank_data.poutcome.value_counts()" + ] + }, + { + "cell_type": "code", + "source": [ + "sns.countplot(bank_data['poutcome'])" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 386 + }, + "id": "A9AjqcIyH4hJ", + "outputId": "0113d73a-9d4f-41b4-c5b0-f341312450b3" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning:\n", + "\n", + "Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n", + "\n" + ] + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" + ] + }, + "metadata": {}, + "execution_count": 147 + }, + { + "output_type": "display_data", + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYsAAAEGCAYAAACUzrmNAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAaTklEQVR4nO3df7hdVX3n8fdHIlJtSwLcZjChE1pTHbQVMQWs1qrYELE12FrEaWuKTGNbams7TovtM0MLMiNjHSq20qESDdYRkUpJrVOaCYKMU4EbQH5KSflRkvLj1gD+esQGv/PHXlcO4V72Ae+59yZ5v57nPmfvtdfaZ52dm3yy195n7VQVkiQ9kafNdQckSfOfYSFJ6mVYSJJ6GRaSpF6GhSSp14K57sAoHHDAAbVs2bK57oYk7VI2b978L1U1NtW23TIsli1bxvj4+Fx3Q5J2KUnumm6bw1CSpF6GhSSpl2EhSeplWEiSehkWkqRehoUkqZdhIUnqZVhIknoZFpKkXrvlN7iH8eL/dN5cd2He2PyeN891FyTNc55ZSJJ6GRaSpF6GhSSpl2EhSeo10rBI8ltJbkpyY5KPJdknycFJrkyyJcnHk+zd6j6jrW9p25cN7OedrfzWJEePss+SpMcbWVgkWQL8BrCiql4A7AUcD5wBnFlVzwEeAE5sTU4EHmjlZ7Z6JDmktXs+sAr4QJK9RtVvSdLjjXoYagHwXUkWAM8E7gFeBVzYtq8Hjm3Lq9s6bftRSdLKz6+qh6vqDmALcPiI+y1JGjCysKiqbcAfAf9EFxIPAZuBB6tqR6u2FVjSlpcAd7e2O1r9/QfLp2jzbUnWJhlPMj4xMTHzH0iS9mCjHIZaRHdWcDDwbOBZdMNII1FV51TViqpaMTY25SNkJUlP0SiHoV4N3FFVE1X1r8AngZcCC9uwFMBSYFtb3gYcBNC27wt8abB8ijaSpFkwyrD4J+DIJM9s1x6OAm4GPgO8odVZA1zclje0ddr2S6uqWvnx7W6pg4HlwFUj7LckaScjmxuqqq5MciFwDbADuBY4B/gb4Pwk72pl57Ym5wIfSbIF2E53BxRVdVOSC+iCZgdwUlU9Mqp+S5Ieb6QTCVbVKcApOxXfzhR3M1XVN4Cfm2Y/pwOnz3gHJUlD8RvckqRehoUkqZdhIUnqZVhIknoZFpKkXoaFJKmXYSFJ6mVYSJJ6GRaSpF6GhSSpl2EhSeplWEiSehkWkqRehoUkqZdhIUnqZVhIknqNLCySPDfJdQM/X07y9iT7JdmY5Lb2uqjVT5KzkmxJcn2Swwb2tabVvy3JmunfVZI0CiMLi6q6taoOrapDgRcDXwcuAk4GNlXVcmBTWwd4Dd3ztZcDa4GzAZLsR/e0vSPonrB3ymTASJJmx2wNQx0F/GNV3QWsBta38vXAsW15NXBedT4PLExyIHA0sLGqtlfVA8BGYNUs9VuSxOyFxfHAx9ry4qq6py3fCyxuy0uAuwfabG1l05U/RpK1ScaTjE9MTMxk3yVpjzfysEiyN/A64BM7b6uqAmom3qeqzqmqFVW1YmxsbCZ2KUlqZuPM4jXANVV1X1u/rw0v0V7vb+XbgIMG2i1tZdOVS5JmyWyExZt4dAgKYAMweUfTGuDigfI3t7uijgQeasNVlwArkyxqF7ZXtjJJ0ixZMMqdJ3kW8JPAWweK3w1ckORE4C7guFb+aeAYYAvdnVMnAFTV9iSnAVe3eqdW1fZR9luS9FgjDYuq+hqw/05lX6K7O2rnugWcNM1+1gHrRtFHSVI/v8EtSeplWEiSehkWkqRehoUkqZdhIUnqZVhIknoZFpKkXoaFJKmXYSFJ6mVYSJJ6GRaSpF6GhSSpl2EhSeplWEiSehkWkqReIw2LJAuTXJjki0luSfKSJPsl2Zjktva6qNVNkrOSbElyfZLDBvazptW/Lcma6d9RkjQKoz6zeB/wt1X1POCFwC3AycCmqloObGrr0D2re3n7WQucDZBkP+AU4AjgcOCUyYCRJM2OkYVFkn2BlwPnAlTVN6vqQWA1sL5VWw8c25ZXA+dV5/PAwiQHAkcDG6tqe1U9AGwEVo2q35KkxxvlmcXBwATwoSTXJvlgeyb34qq6p9W5F1jclpcAdw+039rKpiuXJM2SUYbFAuAw4OyqehHwNR4dcgK+/dztmok3S7I2yXiS8YmJiZnYpSSpGWVYbAW2VtWVbf1CuvC4rw0v0V7vb9u3AQcNtF/ayqYrf4yqOqeqVlTVirGxsRn9IJK0pxtZWFTVvcDdSZ7bio4CbgY2AJN3NK0BLm7LG4A3t7uijgQeasNVlwArkyxqF7ZXtjJJ0ixZMOL9vw34aJK9gduBE+gC6oIkJwJ3Ace1up8GjgG2AF9vdamq7UlOA65u9U6tqu0j7rckacBIw6KqrgNWTLHpqCnqFnDSNPtZB6yb2d5JkoblN7glSb0MC0lSL8NCktTLsJAk9TIsJEm9DAtJUi/DQpLUy7CQJPUyLCRJvQwLSVIvw0KS1MuwkCT1MiwkSb0MC0lSL8NCktTLsJAk9RppWCS5M8kNSa5LMt7K9kuyMclt7XVRK0+Ss5JsSXJ9ksMG9rOm1b8tyZrp3k+SNBqzcWbxyqo6tKomn5h3MrCpqpYDm9o6wGuA5e1nLXA2dOECnAIcARwOnDIZMJKk2TEXw1CrgfVteT1w7ED5edX5PLAwyYHA0cDGqtpeVQ8AG4FVs91pSdqTjTosCvi7JJuTrG1li6vqnrZ8L7C4LS8B7h5ou7WVTVf+GEnWJhlPMj4xMTGTn0GS9ngLRrz/l1XVtiTfB2xM8sXBjVVVSWom3qiqzgHOAVixYsWM7FOS1BnpmUVVbWuv9wMX0V1zuK8NL9Fe72/VtwEHDTRf2sqmK5ckzZKRhUWSZyX5nsllYCVwI7ABmLyjaQ1wcVveALy53RV1JPBQG666BFiZZFG7sL2ylUmSZskoh6EWAxclmXyf/1VVf5vkauCCJCcCdwHHtfqfBo4BtgBfB04AqKrtSU4Drm71Tq2q7SPstyRpJ0OFRZJNVXVUX9mgqrodeOEU5V8CHteuqgo4aZp9rQPWDdNXSdLMe8KwSLIP8EzggDYElLbpe5nijiRJ0u6p78zircDbgWcDm3k0LL4M/MkI+yVJmkeeMCyq6n3A+5K8rareP0t9kiTNM0Nds6iq9yf5MWDZYJuqOm9E/ZIkzSPDXuD+CPCDwHXAI624AMNCkvYAw946uwI4pN2xJEnawwz7pbwbgX8zyo5IkuavYc8sDgBuTnIV8PBkYVW9biS9kiTNK8OGxR+MshOSpPlt2LuhLh91RyRJ89ewd0N9he7uJ4C9gacDX6uq7x1VxyRJ88ewZxbfM7mcbmbA1cCRo+qUJGl+edJTlLfHnv4V3eNOJUl7gGGHoX5mYPVpdN+7+MZIeiRJmneGvRvqpweWdwB30g1FSZL2AMNeszhh1B2RJM1fQ12zSLI0yUVJ7m8/f5lk6ZBt90pybZJPtfWDk1yZZEuSjyfZu5U/o61vaduXDezjna381iReK5GkWTbsBe4P0T0j+9nt569b2TB+E7hlYP0M4Myqeg7wAHBiKz8ReKCVn9nqkeQQ4Hjg+cAq4ANJ9hryvSVJM2DYsBirqg9V1Y7282FgrK9RO/t4LfDBth7gVcCFrcp64Ni2vLqt07YfNXCb7vlV9XBV3UH3jO7Dh+y3JGkGDBsWX0ryC21Iaa8kvwB8aYh2fwz8DvCttr4/8GBV7WjrW3n08axLgLsB2vaHWv1vl0/R5tuSrE0ynmR8YmJiyI8lSRrGsGHxFuA44F7gHuANwC89UYMkPwXcX1Wbv5MODquqzqmqFVW1Ymys96RHkvQkDHvr7KnAmqp6ACDJfsAf0YXIdF4KvC7JMcA+wPcC7wMWJlnQzh6WAtta/W3AQcDWJAuAfenOXibLJw22kSTNgmHPLH5kMigAqmo78KInalBV76yqpVW1jO4C9aVV9fPAZ+jOTADWABe35Q1tnbb90vawpQ3A8e1uqYOB5cBVQ/ZbkjQDhj2zeFqSRTudWQzbdme/C5yf5F3AtcC5rfxc4CNJtgDb6QKGqropyQXAzXRfCDypqh55/G4lSaMy7D/47wX+Pskn2vrPAacP+yZVdRlwWVu+nSnuZqqqb7T9TtX+9CfzfpKkmTXsN7jPSzJOd9srwM9U1c2j65YkaT4ZeiiphYMBIUl7oCc9Rbkkac9jWEiSehkWkqRehoUkqZdhIUnqZVhIknoZFpKkXoaFJKmXYSFJ6mVYSJJ6GRaSpF6GhSSpl2EhSeplWEiSeo0sLJLsk+SqJF9IclOSP2zlBye5MsmWJB9Psncrf0Zb39K2LxvY1ztb+a1Jjh5VnyVJUxvlmcXDwKuq6oXAocCqJEcCZwBnVtVzgAeAE1v9E4EHWvmZrR5JDqF7xOrzgVXAB5LsNcJ+S5J2MrKwqM5X2+rT20/RPW3vwla+Hji2La9u67TtRyVJKz+/qh6uqjuALUzxWFZJ0uiM9JpFkr2SXAfcD2wE/hF4sKp2tCpbgSVteQlwN0Db/hCw/2D5FG0G32ttkvEk4xMTE6P4OJK0xxppWFTVI1V1KLCU7mzgeSN8r3OqakVVrRgbGxvV20jSHmlW7oaqqgeBzwAvARYmmXz291JgW1veBhwE0LbvC3xpsHyKNpKkWTDKu6HGkixsy98F/CRwC11ovKFVWwNc3JY3tHXa9kurqlr58e1uqYOB5cBVo+q3JOnxFvRXecoOBNa3O5eeBlxQVZ9KcjNwfpJ3AdcC57b65wIfSbIF2E53BxRVdVOSC4CbgR3ASVX1yAj7LUnaycjCoqquB140RfntTHE3U1V9A/i5afZ1OnD6TPdRkjQcv8EtSeplWEiSehkWkqRehoUkqZdhIUnqZVhIknoZFpKkXoaFJKmXYSFJ6mVYSJJ6GRaSpF6GhSSpl2EhSeplWEiSehkWkqRehoUkqdcoH6t6UJLPJLk5yU1JfrOV75dkY5Lb2uuiVp4kZyXZkuT6JIcN7GtNq39bkjXTvackaTRGeWaxA/iPVXUIcCRwUpJDgJOBTVW1HNjU1gFeQ/d87eXAWuBs6MIFOAU4gu4Je6dMBowkaXaMLCyq6p6quqYtfwW4BVgCrAbWt2rrgWPb8mrgvOp8HliY5EDgaGBjVW2vqgeAjcCqUfVbkvR4s3LNIskyuudxXwksrqp72qZ7gcVteQlw90Czra1suvKd32NtkvEk4xMTEzPaf0na0408LJJ8N/CXwNur6suD26qqgJqJ96mqc6pqRVWtGBsbm4ldSpKakYZFkqfTBcVHq+qTrfi+NrxEe72/lW8DDhpovrSVTVcuSZolo7wbKsC5wC1V9T8GNm0AJu9oWgNcPFD+5nZX1JHAQ2246hJgZZJF7cL2ylYmSZolC0a475cCvwjckOS6VvZ7wLuBC5KcCNwFHNe2fRo4BtgCfB04AaCqtic5Dbi61Tu1qraPsN+SpJ2MLCyq6v8CmWbzUVPUL+Ckafa1Dlg3c72TJD0ZfoNbktTLsJAk9TIsJEm9DAtJUi/DQpLUy7CQJPUyLCRJvQwLSVIvw0KS1MuwkCT1MiwkSb0MC0lSL8NCktRrlFOUS3oKXvr+l851F+aNz73tc3PdBTWeWUiSehkWkqReo3ys6rok9ye5caBsvyQbk9zWXhe18iQ5K8mWJNcnOWygzZpW/7Yka6Z6L0nSaI3ymsWHgT8BzhsoOxnYVFXvTnJyW/9d4DXA8vZzBHA2cESS/YBTgBVAAZuTbKiqB0bYb0m7kctf/hNz3YV54yc+e/lTbjuyM4uq+iyw87OyVwPr2/J64NiB8vOq83lgYZIDgaOBjVW1vQXERmDVqPosSZrabF+zWFxV97Tle4HFbXkJcPdAva2tbLryx0myNsl4kvGJiYmZ7bUk7eHm7AJ3VRXd0NJM7e+cqlpRVSvGxsZmareSJGY/LO5rw0u01/tb+TbgoIF6S1vZdOWSpFk022GxAZi8o2kNcPFA+ZvbXVFHAg+14apLgJVJFrU7p1a2MknSLBrZ3VBJPga8AjggyVa6u5reDVyQ5ETgLuC4Vv3TwDHAFuDrwAkAVbU9yWnA1a3eqVW180VzSdKIjSwsqupN02w6aoq6BZw0zX7WAetmsGuSpCfJb3BLknoZFpKkXoaFJKmXYSFJ6mVYSJJ6+fAjfcf+6dQfnusuzBvf/19umOsuSCPhmYUkqZdhIUnqZVhIknoZFpKkXoaFJKmXYSFJ6mVYSJJ6GRaSpF6GhSSpl2EhSeq1y4RFklVJbk2yJcnJc90fSdqT7BJhkWQv4E+B1wCHAG9Kcsjc9kqS9hy7RFgAhwNbqur2qvomcD6weo77JEl7jHSPv57fkrwBWFVV/6Gt/yJwRFX9+kCdtcDatvpc4NZZ7+iTdwDwL3Pdid2Ix3NmeTxnzq5yLP9tVY1NtWG3maK8qs4BzpnrfjwZScarasVc92N34fGcWR7PmbM7HMtdZRhqG3DQwPrSViZJmgW7SlhcDSxPcnCSvYHjgQ1z3CdJ2mPsEsNQVbUjya8DlwB7Aeuq6qY57tZM2KWGzXYBHs+Z5fGcObv8sdwlLnBLkubWrjIMJUmaQ4aFJKmXYTECSX4pyZ/MdT92N0kWJvm1gfVXJPnUXPZpvkvyG0luSfLRabavSHJWW/b3VtPaJS5wS81C4NeAD8zEzpIsqKodM7GveezXgFdX1dapNlbVODD+VHa8hxw/NZ5ZDCHJsiQ3Dqy/I8kfJLksyRlJrkryD0l+fIq2r03y90kOSPLhJGcl+X9Jbm/fTCed9yS5MckNSd7Yyv80yeva8kVJ1rXltyQ5vfXrliR/nuSmJH+X5Ltm56iMXpLfbsfkxiRvB94N/GCS65K8p1X77iQXJvliko8mSWv74iSXJ9mc5JIkB7byy5L8cZJx4Dfn5pPNjiR/BvwA8L+T/G77Pby2/f49t9WZ8uys/a6+YWD9qwP1r0iyAbg5yV7td/fqJNcneessfbyRS/KsJH+T5Avtd/CNSe5MckDbviLJZW35u5N8qP39vT7Jz7byVUmuafvYNLDfde3fjWuTrG7lz29l17V9LJ+qD3N0ODyzmAELqurwJMcApwCvntyQ5PXAbwPHVNUD7d+xA4GXAc+j+67IhcDPAIcCL6SbFuDqJJ8FrgB+vNVb0trSys5vy8uBN1XVLye5APhZ4C9G93FnR5IXAycARwABrgR+AXhBVR3a6rwCeBHwfOCfgc8BL01yJfB+YHVVTbS/YKcDb2m733tX/zbtMKrqV5KsAl4JfBN4b7sN/dXAf6X7XXkqDqP7c7gj3TQ7D1XVjyZ5BvC5JH9XVXfMyIeYW6uAf66q1wIk2Rc4Y5q6/5nuOPxwq7soyRjw58DL27Har9X9feDSqnpLkoXAVUn+D/ArwPuq6qPpvk+2F3DMFH2YE4bFd+6T7XUzsGyg/FXACmBlVX15oPyvqupbdP8rW9zKXgZ8rKoeAe5Lcjnwo3Rh8fZ0M+zeDCxq/0N+CfAbwP7AHVV13TR92JW9DLioqr4GkOSTdCG5s6smh1iSXEf3+R8EXgBsbAG9F3DPQJuPj67b89a+wPoky4ECnv4d7OuqgTBYCfzIwFnIvnT/gdkdwuIG4L1JzgA+VVVXtN+nqbya7svCALT/HP408NnJY1VV29vmlcDrkryjre8DfD/w98DvJ1kKfLKqbkvyuD7M8GccmmExnB08dshun4Hlh9vrIzz2eP4j3RDAD/HYMeGHB5an/c0DqKpt7X8eq4DPAvsBxwFfraqvJNl/p/09Auw2w1BD2vnzL6A7rjdV1UumafO1kfdq/jkN+ExVvT7JMuCynvrf/p1P8jRg74Ftg8cvwNuq6pIZ6+k8UVX/kOQwuv/dv6sNIw3+W7DPtI2fWICfraqdJzu9pZ0Vvxb4dJK3VtWlO/ehqk59iu/7HfGaxXDuA74vyf7tVPunhmhzF91p/nlJnt9T9wrgjW38dwx4OXBV2/Z54O10YXEF8I72uru7Ajg2yTOTPAt4Pd0w0/cM0fZWYCzJSwCSPH2IP4Pd3b48Op/aLw1R/07gxW35dUx/JnIJ8KtJng6Q5Ifan9cuL8mzga9X1V8A76EbfruTR4/L4DDeRuCkgbaL6P7uvjzJwa1schjqEuBtybevr72ovf4AcHtVnQVcTHfGNlUf5oRhMYSq+lfgVLp/wDcCXxyy3ReBnwc+keQHn6DqRcD1wBeAS4Hfqap727Yr6K6LbAGuoTu72O3DoqquAT5Md8yvBD5YVZvpxsRvzKMXuKdq+03gDcAZSb4AXAf82Oh7Pa/9d+C/JbmW4UYU/hz4iXb8XsL0Z2MfpBsivSbdTSD/c8j97wp+mO56wnV01yPfBfwh8L50N0g8MlD3XXTDxDe2Y/bKqpqge2zCJ1vZ5PDnaXThe32Sm9o6dKMGN7b3ewFw3jR9mBNO9yFJ6uWZhSSpl2EhSeplWEiSehkWkqRehoUkqZdhIY1Im0dpT79lV7sJw0IanVfg9zu0mzAspJ2km813chbbW9LNavvMJEe1WUJvaLOGPqPVf9xMpG1KjV8BfqvNIvrjSRanmz34C+3nx1qbnWfXHezDh9PNaPzRJK9O8rkktyU5vNWbcgZTaaYZFtLUngt8oKr+HfBlutmDPwy8sc0sugD41ekaV9WdwJ8BZ1bVoW0CuLOAy6vqhXTTNty00+y6RwK/PDn9A/Ac4L10MxQ/D/j3dBMsvgP4vVZncgbTw+lml33P7jLdhuYXw0Ka2t1V9bm2/BfAUXQz/P5DK1tPN4fXk/Eq4GyAqnqkqh5iYHbdqvoq3SzGk7Pr3lFVN7RZim8CNlU35cINPDq78Erg5DYdxGU8OoOpNKN2lzlcpJm28zw4D9JNCT+VmZiJdCqDM+p+a2D9Wzz6d3e6GUylGeWZhTS175+ctZZu+GccWJbkOa3sF4HL2/KdTD0T6Vd47Cy5m2hDV22G4X2ZenbdJzNR5JQzmEozzbCQpnYrcFKSW4BFwJl01xY+0R5I8y26axIw/Uykfw28fvICN91jXF/Z2m8GDplmdt1rn0Q/p5vBVJpRzjor7aTdyfSpqnrBHHdFmjc8s5Ak9fLMQpLUyzMLSVIvw0KS1MuwkCT1MiwkSb0MC0lSr/8PMGPTl/v5sRYAAAAASUVORK5CYII=\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + } + } + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "_cell_guid": "b827ea76-4adb-4472-9def-ebd268d5586f", + "_uuid": "dd9bc0417c255963d08c7eb721dde66b32d406dc", + "collapsed": true, + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "T4Sbh6U4jSOA", + "outputId": "841a9bdc-aca7-4be0-a226-bb3184beee75" + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "unknown 8863\n", + "failure 1228\n", + "success 1071\n", + "Name: poutcome, dtype: int64" + ] + }, + "metadata": {}, + "execution_count": 148 + } + ], + "source": [ + "# Combine 'unknown' and 'other' as 'other' isn't really match with either 'success' or 'failure'\n", + "bank_data['poutcome'] = bank_data['poutcome'].replace(['other'] , 'unknown')\n", + "bank_data.poutcome.value_counts()" + ] + }, + { + "cell_type": "code", + "source": [ + "sns.countplot(bank_data['poutcome'])" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 386 + }, + "id": "0q5EJsgVKW-S", + "outputId": "cc5d9853-ad11-4a45-f540-0ef60d4032ae" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning:\n", + "\n", + "Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n", + "\n" + ] + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" + ] + }, + "metadata": {}, + "execution_count": 149 + }, + { + "output_type": "display_data", + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + } + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "_cell_guid": "643362de-18ca-45c2-95a9-6ca2c19f0144", + "_uuid": "154cad1a97e082dea7fefef0084301c391b78bf9", + "id": "QuXz1WcojSOA" + }, + "source": [ + "#### contact" + ] + }, + { + "cell_type": "code", + "source": [ + "bank_data.contact.value_counts()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "qt5xaWmxKhBl", + "outputId": "7bdc5898-e9e4-4ca1-e879-04e5b011dffe" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "cellular 8042\n", + "unknown 2346\n", + "telephone 774\n", + "Name: contact, dtype: int64" + ] + }, + "metadata": {}, + "execution_count": 154 + } + ] + }, + { + "cell_type": "code", + "source": [ + "sns.countplot(bank_data['contact'])" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 386 + }, + "id": "Y8g5bX3rLSQD", + "outputId": "33765089-b007-43e6-bf37-d6edc147d994" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning:\n", + "\n", + "Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n", + "\n" + ] + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" + ] + }, + "metadata": {}, + "execution_count": 155 + }, + { + "output_type": "display_data", + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + } + } + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "_cell_guid": "edf8d6c3-f66a-4521-bb82-13de0e175244", + "_uuid": "7b3cd6910378c11f5edacde330f5a312d0f58d16", + "collapsed": true, + "id": "Y3O7BGy5jSOB" + }, + "outputs": [], + "source": [ + "# Drop 'contact', as every participant has been contacted. \n", + "bank_data.drop('contact', axis=1, inplace=True)" + ] + }, + { + "cell_type": "code", + "source": [ + "bank_data.head()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 206 + }, + "id": "dUzQlsN3oAu4", + "outputId": "981de64f-8d00-4264-a3fd-34089ad41177" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "\n", + "
\n", + "
\n", + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
agejobmaritaleducationdefaultbalancehousingloandaymonthdurationcampaignpdayspreviouspoutcomedeposit
059white-collarmarriedsecondaryno2343yesno5may10421-10unknownyes
156white-collarmarriedsecondaryno45nono5may14671-10unknownyes
241technicianmarriedsecondaryno1270yesno5may13891-10unknownyes
355pink-collarmarriedsecondaryno2476yesno5may5791-10unknownyes
454white-collarmarriedtertiaryno184nono5may6732-10unknownyes
\n", + "
\n", + " \n", + " \n", + " \n", + "\n", + " \n", + "
\n", + "
\n", + " " + ], + "text/plain": [ + " age job marital education ... pdays previous poutcome deposit\n", + "0 59 white-collar married secondary ... -1 0 unknown yes\n", + "1 56 white-collar married secondary ... -1 0 unknown yes\n", + "2 41 technician married secondary ... -1 0 unknown yes\n", + "3 55 pink-collar married secondary ... -1 0 unknown yes\n", + "4 54 white-collar married tertiary ... -1 0 unknown yes\n", + "\n", + "[5 rows x 16 columns]" + ] + }, + "metadata": {}, + "execution_count": 157 + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "_cell_guid": "167de291-a2df-4371-837b-25cc84121fc9", + "_uuid": "8d519172290ca18fd70843a45677e0524b460a8c", + "id": "Ti0iUujFjSOB" + }, + "source": [ + "#### default" + ] + }, + { + "cell_type": "code", + "source": [ + "bank_data['default'].value_counts()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "w4sEHNguSbDc", + "outputId": "e8a3b862-8066-4d22-c681-6a20660e509c" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "no 10994\n", + "yes 168\n", + "Name: default, dtype: int64" + ] + }, + "metadata": {}, + "execution_count": 169 + } + ] + }, + { + "cell_type": "code", + "source": [ + "bank_data['default'].value_counts().plot(kind='pie', autopct='%.2f')" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 265 + }, + "id": "1aI-eM2uLmnk", + "outputId": "50b1be20-6ec9-4bbf-c880-166304487967" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" + ] + }, + "metadata": {}, + "execution_count": 168 + }, + { + "output_type": "display_data", + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {} + } + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "_cell_guid": "9445241c-8d2f-4552-a879-f35bd08155ee", + "_uuid": "35a4bc625d393ae0b38076c7bdb88e12be134acf", + "collapsed": true, + "id": "MPbcLqBEjSOB" + }, + "outputs": [], + "source": [ + "# values for \"default\" : yes/no\n", + "bank_data[\"default\"]\n", + "bank_data['default_cat'] = bank_data['default'].map( {'yes':1, 'no':0} )\n", + "bank_data.drop('default', axis=1,inplace = True)\n" + ] + }, + { + "cell_type": "code", + "source": [ + "bank_data.head()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 206 + }, + "id": "_rzwJZgDobOd", + "outputId": "3b1359f9-de6b-4142-a9d7-b1470e96a6e3" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "\n", + "
\n", + "
\n", + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
agejobmaritaleducationbalancehousingloandaymonthdurationcampaignpdayspreviouspoutcomedepositdefault_cat
059white-collarmarriedsecondary2343yesno5may10421-10unknownyes0
156white-collarmarriedsecondary45nono5may14671-10unknownyes0
241technicianmarriedsecondary1270yesno5may13891-10unknownyes0
355pink-collarmarriedsecondary2476yesno5may5791-10unknownyes0
454white-collarmarriedtertiary184nono5may6732-10unknownyes0
\n", + "
\n", + " \n", + " \n", + " \n", + "\n", + " \n", + "
\n", + "
\n", + " " + ], + "text/plain": [ + " age job marital ... poutcome deposit default_cat\n", + "0 59 white-collar married ... unknown yes 0\n", + "1 56 white-collar married ... unknown yes 0\n", + "2 41 technician married ... unknown yes 0\n", + "3 55 pink-collar married ... unknown yes 0\n", + "4 54 white-collar married ... unknown yes 0\n", + "\n", + "[5 rows x 16 columns]" + ] + }, + "metadata": {}, + "execution_count": 28 + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "_cell_guid": "e69fa9cf-4b44-450e-9c11-52edad2336c0", + "_uuid": "71c97178c06dfb20561a6f4813eaf48f16270ed2", + "id": "Lx4MVHu2jSOC" + }, + "source": [ + "#### housing" + ] + }, + { + "cell_type": "code", + "source": [ + "bank_data['housing'].value_counts()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "uCckpA0KSoQZ", + "outputId": "5239d7ae-b55f-4e91-de95-2eb178f33555" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "no 5881\n", + "yes 5281\n", + "Name: housing, dtype: int64" + ] + }, + "metadata": {}, + "execution_count": 171 + } + ] + }, + { + "cell_type": "code", + "source": [ + "bank_data['housing'].value_counts().plot(kind='pie', autopct='%.2f')" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 265 + }, + "id": "z_e6OrCSSmG1", + "outputId": "2f9696dd-1d7c-4460-8334-316ca4621ffe" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" + ] + }, + "metadata": {}, + "execution_count": 173 + }, + { + "output_type": "display_data", + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {} + } + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "_cell_guid": "9f33486b-6c4c-4a12-9174-54be5f788091", + "_uuid": "5625dd9f23af740ce738cb5f1d45ca18dc4d3fe6", + "collapsed": true, + "id": "LNQ4UaG6jSOC" + }, + "outputs": [], + "source": [ + "# values for \"housing\" : yes/no\n", + "bank_data[\"housing_cat\"]=bank_data['housing'].map({'yes':1, 'no':0})\n", + "bank_data.drop('housing', axis=1,inplace = True)" + ] + }, + { + "cell_type": "code", + "source": [ + "bank_data.head()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 206 + }, + "id": "PcSsNAayojM1", + "outputId": "00cd28fb-faf4-4031-d471-90edeb7d4f2d" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "\n", + "
\n", + "
\n", + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
agejobmaritaleducationbalanceloandaymonthdurationcampaignpdayspreviouspoutcomedepositdefault_cathousing_cat
059white-collarmarriedsecondary2343no5may10421-10unknownyes01
156white-collarmarriedsecondary45no5may14671-10unknownyes00
241technicianmarriedsecondary1270no5may13891-10unknownyes01
355pink-collarmarriedsecondary2476no5may5791-10unknownyes01
454white-collarmarriedtertiary184no5may6732-10unknownyes00
\n", + "
\n", + " \n", + " \n", + " \n", + "\n", + " \n", + "
\n", + "
\n", + " " + ], + "text/plain": [ + " age job marital ... deposit default_cat housing_cat\n", + "0 59 white-collar married ... yes 0 1\n", + "1 56 white-collar married ... yes 0 0\n", + "2 41 technician married ... yes 0 1\n", + "3 55 pink-collar married ... yes 0 1\n", + "4 54 white-collar married ... yes 0 0\n", + "\n", + "[5 rows x 16 columns]" + ] + }, + "metadata": {}, + "execution_count": 31 + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "_cell_guid": "f4be5aa2-9702-4b64-a732-b473ac696631", + "_uuid": "dfae48f26206aa2f27803205e071841fa877307c", + "id": "0gZW1UaujSOC" + }, + "source": [ + "#### loan" + ] + }, + { + "cell_type": "code", + "source": [ + "bank_data['loan'].value_counts()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "dtusL89GS1ZS", + "outputId": "b7ec4e88-03e4-4e18-bde2-d70e90b9fab1" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "no 9702\n", + "yes 1460\n", + "Name: loan, dtype: int64" + ] + }, + "metadata": {}, + "execution_count": 175 + } + ] + }, + { + "cell_type": "code", + "source": [ + "bank_data['loan'].value_counts().plot(kind='pie', autopct='%.2f')" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 265 + }, + "id": "mtcmU4ySS4dS", + "outputId": "7aefbfe1-1238-4f6e-96f9-27dc5f411dcd" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" + ] + }, + "metadata": {}, + "execution_count": 176 + }, + { + "output_type": "display_data", + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {} + } + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "_cell_guid": "c1b2dc72-ee07-4995-aa5e-e5bbd0a541b8", + "_uuid": "557f3471b7552919b437e703005830585a715e56", + "collapsed": true, + "id": "RveCNESJjSOC" + }, + "outputs": [], + "source": [ + "# values for \"loan\" : yes/no\n", + "bank_data[\"loan_cat\"] = bank_data['loan'].map({'yes':1, 'no':0})\n", + "bank_data.drop('loan', axis=1, inplace=True)" + ] + }, + { + "cell_type": "code", + "source": [ + "bank_data.head()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 250 + }, + "id": "hb4a6wRBothv", + "outputId": "b35f432d-c24e-4518-8c43-60cfd435dcd3" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "\n", + "
\n", + "
\n", + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
agejobmaritaleducationbalancedaymonthdurationcampaignpdayspreviouspoutcomedepositdefault_cathousing_catloan_cat
059white-collarmarriedsecondary23435may10421-10unknownyes010
156white-collarmarriedsecondary455may14671-10unknownyes000
241technicianmarriedsecondary12705may13891-10unknownyes010
355pink-collarmarriedsecondary24765may5791-10unknownyes010
454white-collarmarriedtertiary1845may6732-10unknownyes000
\n", + "
\n", + " \n", + " \n", + " \n", + "\n", + " \n", + "
\n", + "
\n", + " " + ], + "text/plain": [ + " age job marital ... default_cat housing_cat loan_cat\n", + "0 59 white-collar married ... 0 1 0\n", + "1 56 white-collar married ... 0 0 0\n", + "2 41 technician married ... 0 1 0\n", + "3 55 pink-collar married ... 0 1 0\n", + "4 54 white-collar married ... 0 0 0\n", + "\n", + "[5 rows x 16 columns]" + ] + }, + "metadata": {}, + "execution_count": 178 + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "_cell_guid": "422662b8-94f4-4b1f-a61c-71dca5627c7c", + "_uuid": "f94605bb3c4f22900794ad6b1d066d3808d9d5b1", + "id": "huql5gAajSOC" + }, + "source": [ + "#### month, day " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "_cell_guid": "a004d399-5d98-4767-a629-4008e834a501", + "_uuid": "24b3d3f4534ea7b714743123dc7f1186d6c6165a", + "collapsed": true, + "id": "ny9n4xjcjSOD" + }, + "outputs": [], + "source": [ + "# day : last contact day of the month\n", + "# month: last contact month of year\n", + "# Drop 'month' and 'day' \n", + "bank_data.drop('month', axis=1, inplace=True)\n", + "bank_data.drop('day', axis=1, inplace=True)" + ] + }, + { + "cell_type": "code", + "source": [ + "bank_data.head()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 206 + }, + "id": "3w49soWIo3Uy", + "outputId": "bbf35cd6-69ee-49bf-dcb1-14c23fbe2def" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "\n", + "
\n", + "
\n", + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
agejobmaritaleducationbalancedurationcampaignpdayspreviouspoutcomedepositdefault_cathousing_catloan_cat
059white-collarmarriedsecondary234310421-10unknownyes010
156white-collarmarriedsecondary4514671-10unknownyes000
241technicianmarriedsecondary127013891-10unknownyes010
355pink-collarmarriedsecondary24765791-10unknownyes010
454white-collarmarriedtertiary1846732-10unknownyes000
\n", + "
\n", + " \n", + " \n", + " \n", + "\n", + " \n", + "
\n", + "
\n", + " " + ], + "text/plain": [ + " age job marital ... default_cat housing_cat loan_cat\n", + "0 59 white-collar married ... 0 1 0\n", + "1 56 white-collar married ... 0 0 0\n", + "2 41 technician married ... 0 1 0\n", + "3 55 pink-collar married ... 0 1 0\n", + "4 54 white-collar married ... 0 0 0\n", + "\n", + "[5 rows x 14 columns]" + ] + }, + "metadata": {}, + "execution_count": 180 + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "_cell_guid": "3114fee4-f7f6-4bb3-9e9e-30080deacae8", + "_uuid": "082214ade8f57c636812167b6b6f11a291209234", + "id": "j3rFMDx0jSOD" + }, + "source": [ + "#### deposit " + ] + }, + { + "cell_type": "code", + "source": [ + "bank_data['deposit'].value_counts()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "9uOE6mPPTJNH", + "outputId": "d042f3d9-211a-4483-a35f-eb7c382e78e9" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "no 5873\n", + "yes 5289\n", + "Name: deposit, dtype: int64" + ] + }, + "metadata": {}, + "execution_count": 181 + } + ] + }, + { + "cell_type": "code", + "source": [ + "bank_data['deposit'].value_counts().plot(kind='pie', autopct='%.2f')" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 265 + }, + "id": "nF-vlFx8TNXo", + "outputId": "83547163-8d99-4e2c-e5a6-384504438eb4" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" + ] + }, + "metadata": {}, + "execution_count": 182 + }, + { + "output_type": "display_data", + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {} + } + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "_cell_guid": "824acb7c-9ebb-4ec3-abc2-91b3a8dfbba8", + "_uuid": "8983691ec985826964e224d851c47fa53d5189a3", + "collapsed": true, + "id": "KCXAige3jSOD" + }, + "outputs": [], + "source": [ + "# values for \"deposit\" : yes/no\n", + "bank_data[\"deposit_cat\"] = bank_data['deposit'].map({'yes':1, 'no':0})\n", + "bank_data.drop('deposit', axis=1, inplace=True)" + ] + }, + { + "cell_type": "code", + "source": [ + "bank_data.head()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 206 + }, + "id": "Nxr19rhupBhD", + "outputId": "a1d1a23d-4586-4d0a-d561-b6f1adfe75ff" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "\n", + "
\n", + "
\n", + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
agejobmaritaleducationbalancedurationcampaignpdayspreviouspoutcomedefault_cathousing_catloan_catdeposit_cat
059white-collarmarriedsecondary234310421-10unknown0101
156white-collarmarriedsecondary4514671-10unknown0001
241technicianmarriedsecondary127013891-10unknown0101
355pink-collarmarriedsecondary24765791-10unknown0101
454white-collarmarriedtertiary1846732-10unknown0001
\n", + "
\n", + " \n", + " \n", + " \n", + "\n", + " \n", + "
\n", + "
\n", + " " + ], + "text/plain": [ + " age job marital ... housing_cat loan_cat deposit_cat\n", + "0 59 white-collar married ... 1 0 1\n", + "1 56 white-collar married ... 0 0 1\n", + "2 41 technician married ... 1 0 1\n", + "3 55 pink-collar married ... 1 0 1\n", + "4 54 white-collar married ... 0 0 1\n", + "\n", + "[5 rows x 14 columns]" + ] + }, + "metadata": {}, + "execution_count": 184 + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "_cell_guid": "6a07560d-d8d6-44a7-bc40-e3fefe56c07a", + "_uuid": "ae91146f0a733b46f2ffc929e6c15c637b5a5464", + "id": "o8-RUiIhjSOD" + }, + "source": [ + "#### pdays" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "_cell_guid": "a0a43966-0a0a-4c87-9fbb-98753440475e", + "_uuid": "a9285460820d03a8a46b00efa4ab5c0b7bfdfdba", + "collapsed": true, + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "_MnWo1CejSOD", + "outputId": "738d05d2-484f-4bff-d4ff-dea5a5508222" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Customers that have not been contacted before: 8324\n", + "Maximum values on padys : 854\n" + ] + } + ], + "source": [ + "# pdays: number of days that passed by after the client was last contacted from a previous campaign\n", + "# -1 means client was not previously contacted\n", + "\n", + "print(\"Customers that have not been contacted before:\", len(bank_data[bank_data.pdays==-1]))\n", + "print(\"Maximum values on padys :\", bank_data['pdays'].max())" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "_cell_guid": "27c4a40d-a6e7-455f-85ef-db661c2f3bf6", + "_uuid": "4a5ce52f6514b2c6a98a05496f464958bb08e6d1", + "collapsed": true, + "id": "MQmZyxsHjSOE" + }, + "outputs": [], + "source": [ + "# Map pdays=-1 into a large value (10000 is used) to indicate that it is so far in the past that it has no effect\n", + "bank_data.loc[bank_data['pdays'] == -1, 'pdays'] = 10000" + ] + }, + { + "cell_type": "code", + "source": [ + "bank_data.head()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 206 + }, + "id": "KywPgIrcpYlU", + "outputId": "df9050cf-75e9-4ec1-a82f-2c27633a8165" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "\n", + "
\n", + "
\n", + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
agejobmaritaleducationbalancedurationcampaignpdayspreviouspoutcomedefault_cathousing_catloan_catdeposit_cat
059white-collarmarriedsecondary234310421100000unknown0101
156white-collarmarriedsecondary4514671100000unknown0001
241technicianmarriedsecondary127013891100000unknown0101
355pink-collarmarriedsecondary24765791100000unknown0101
454white-collarmarriedtertiary1846732100000unknown0001
\n", + "
\n", + " \n", + " \n", + " \n", + "\n", + " \n", + "
\n", + "
\n", + " " + ], + "text/plain": [ + " age job marital ... housing_cat loan_cat deposit_cat\n", + "0 59 white-collar married ... 1 0 1\n", + "1 56 white-collar married ... 0 0 1\n", + "2 41 technician married ... 1 0 1\n", + "3 55 pink-collar married ... 1 0 1\n", + "4 54 white-collar married ... 0 0 1\n", + "\n", + "[5 rows x 14 columns]" + ] + }, + "metadata": {}, + "execution_count": 189 + } + ] + }, + { + "cell_type": "code", + "source": [ + "1/bank_data.pdays" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "cQOgRyPTTz3a", + "outputId": "a35d7732-dfd3-421c-a3ad-843027a873c2" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "0 0.000100\n", + "1 0.000100\n", + "2 0.000100\n", + "3 0.000100\n", + "4 0.000100\n", + " ... \n", + "11157 0.000100\n", + "11158 0.000100\n", + "11159 0.000100\n", + "11160 0.005814\n", + "11161 0.000100\n", + "Name: pdays, Length: 11162, dtype: float64" + ] + }, + "metadata": {}, + "execution_count": 195 + } + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "_cell_guid": "dc334f4c-7756-42d0-884e-c87c8c205b72", + "_uuid": "32aed7514950db77534edf31950599dd2de13174", + "collapsed": true, + "id": "E4zzaRhgjSOE" + }, + "outputs": [], + "source": [ + "# Create a new column: recent_pdays \n", + "bank_data['recent_pdays'] = np.where(bank_data['pdays'], 1/bank_data.pdays, 1/bank_data.pdays)\n", + "\n", + "# Drop 'pdays'\n", + "bank_data.drop('pdays', axis=1, inplace = True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "_cell_guid": "d081cff8-2b59-449e-aa9d-c47b95bf9f3b", + "_uuid": "0fd651329be4e215dfe12e7d0dcd825081fc22bb", + "collapsed": true, + "colab": { + "base_uri": "https://localhost:8080/", + "height": 250 + }, + "id": "-56l68DNjSOE", + "outputId": "bdef22da-4791-4b50-86f1-50cef251ccfb" + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "\n", + "
\n", + "
\n", + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
agejobmaritaleducationbalancedurationcampaignpreviouspoutcomedefault_cathousing_catloan_catdeposit_catrecent_pdays
1115733blue-collarsingleprimary125710unknown01000.000100
1115839pink-collarmarriedsecondary7338340unknown00000.000100
1115932techniciansinglesecondary2915620unknown00000.000100
1116043technicianmarriedsecondary0925failure00100.005814
1116134technicianmarriedsecondary062810unknown00000.000100
\n", + "
\n", + " \n", + " \n", + " \n", + "\n", + " \n", + "
\n", + "
\n", + " " + ], + "text/plain": [ + " age job marital ... loan_cat deposit_cat recent_pdays\n", + "11157 33 blue-collar single ... 0 0 0.000100\n", + "11158 39 pink-collar married ... 0 0 0.000100\n", + "11159 32 technician single ... 0 0 0.000100\n", + "11160 43 technician married ... 1 0 0.005814\n", + "11161 34 technician married ... 0 0 0.000100\n", + "\n", + "[5 rows x 14 columns]" + ] + }, + "metadata": {}, + "execution_count": 198 + } + ], + "source": [ + "bank_data.tail()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "_cell_guid": "675e8d8f-2e23-4976-a7e9-fdb0d4983393", + "_uuid": "6c0309015c140816d4fc83ce6af0d47daa6ac0f0", + "id": "2xP7i2LBjSOF" + }, + "source": [ + "### Convert to dummy values " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "_cell_guid": "65a1ee10-aa81-4e44-b159-88f426ad0ae3", + "_uuid": "63ac123483934cbc0e3703950e9cd7860184e985", + "collapsed": true, + "colab": { + "base_uri": "https://localhost:8080/", + "height": 288 + }, + "id": "eam_AkO4jSOF", + "outputId": "4510be08-9135-4658-de14-bc3ce46ef180" + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "\n", + "
\n", + "
\n", + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
agebalancedurationcampaignpreviousdefault_cathousing_catloan_catdeposit_catrecent_pdaysjob_blue-collarjob_entrepreneurjob_otherjob_pink-collarjob_self-employedjob_technicianjob_white-collarmarital_divorcedmarital_marriedmarital_singleeducation_primaryeducation_secondaryeducation_tertiaryeducation_unknownpoutcome_failurepoutcome_successpoutcome_unknown
059234310421001010.000100000010100100001
1564514671000010.000100000010100100001
241127013891001010.000100000100100100001
35524765791001010.000100010000100100001
4541846732000010.000100000010100010001
\n", + "
\n", + " \n", + " \n", + " \n", + "\n", + " \n", + "
\n", + "
\n", + " " + ], + "text/plain": [ + " age balance duration ... poutcome_failure poutcome_success poutcome_unknown\n", + "0 59 2343 1042 ... 0 0 1\n", + "1 56 45 1467 ... 0 0 1\n", + "2 41 1270 1389 ... 0 0 1\n", + "3 55 2476 579 ... 0 0 1\n", + "4 54 184 673 ... 0 0 1\n", + "\n", + "[5 rows x 27 columns]" + ] + }, + "metadata": {}, + "execution_count": 199 + } + ], + "source": [ + "# Convert categorical variables to dummies\n", + "bank_with_dummies = pd.get_dummies(data=bank_data, columns = ['job', 'marital', 'education', 'poutcome'], \\\n", + " prefix = ['job', 'marital', 'education', 'poutcome'])\n", + "bank_with_dummies.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "_cell_guid": "dc46d20e-db1f-41cc-a0c4-bea3f800235e", + "_uuid": "5fae26f3d9d4c343d75d90163ebb54f64c4d798e", + "collapsed": true, + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "t6jdyBnxjSOF", + "outputId": "605b712c-c72a-4e2e-8a20-f93ea0f391c0" + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "(11162, 27)" + ] + }, + "metadata": {}, + "execution_count": 200 + } + ], + "source": [ + "bank_with_dummies.shape" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "_cell_guid": "8daac786-1fcd-4dd5-b789-8dbb96011c44", + "_uuid": "b9018d84ec2eae5368860f237e47e00473deeb05", + "collapsed": true, + "colab": { + "base_uri": "https://localhost:8080/", + "height": 382 + }, + "id": "BcTsdw9_jSOF", + "outputId": "a45a5f6f-202a-45f4-c107-fc1a2e9ea9ad" + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "\n", + "
\n", + "
\n", + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
agebalancedurationcampaignpreviousdefault_cathousing_catloan_catdeposit_catrecent_pdaysjob_blue-collarjob_entrepreneurjob_otherjob_pink-collarjob_self-employedjob_technicianjob_white-collarmarital_divorcedmarital_marriedmarital_singleeducation_primaryeducation_secondaryeducation_tertiaryeducation_unknownpoutcome_failurepoutcome_successpoutcome_unknown
count11162.00000011162.00000011162.00000011162.00000011162.00000011162.00000011162.00000011162.00000011162.00000011162.00000011162.00000011162.00000011162.00000011162.00000011162.00000011162.00000011162.00000011162.00000011162.00000011162.00000011162.00000011162.00000011162.00000011162.00000011162.00000011162.00000011162.000000
mean41.2319481528.538524371.9938182.5084210.8325570.0150510.4731230.1308010.4738400.0031240.1741620.0293850.1402080.1072390.0362840.1633220.3494000.1158390.5689840.3151760.1343850.4905930.3304960.0445260.1100160.0959510.794033
std11.9133693225.413326347.1283862.7220772.2920070.1217610.4992990.3371980.4993380.0306860.3792660.1688920.3472180.3094310.1870040.3696760.4768020.3200470.4952410.4646070.3410800.4999340.4704130.2062700.3129240.2945370.404424
min18.000000-6847.0000002.0000001.0000000.0000000.0000000.0000000.0000000.0000000.0001000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.000000
25%32.000000122.000000138.0000001.0000000.0000000.0000000.0000000.0000000.0000000.0001000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000001.000000
50%39.000000550.000000255.0000002.0000000.0000000.0000000.0000000.0000000.0000000.0001000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000001.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000001.000000
75%49.0000001708.000000496.0000003.0000001.0000000.0000001.0000000.0000001.0000000.0019190.0000000.0000000.0000000.0000000.0000000.0000001.0000000.0000001.0000001.0000000.0000001.0000001.0000000.0000000.0000000.0000001.000000
max95.00000081204.0000003881.00000063.00000058.0000001.0000001.0000001.0000001.0000001.0000001.0000001.0000001.0000001.0000001.0000001.0000001.0000001.0000001.0000001.0000001.0000001.0000001.0000001.0000001.0000001.0000001.000000
\n", + "
\n", + " \n", + " \n", + " \n", + "\n", + " \n", + "
\n", + "
\n", + " " + ], + "text/plain": [ + " age balance ... poutcome_success poutcome_unknown\n", + "count 11162.000000 11162.000000 ... 11162.000000 11162.000000\n", + "mean 41.231948 1528.538524 ... 0.095951 0.794033\n", + "std 11.913369 3225.413326 ... 0.294537 0.404424\n", + "min 18.000000 -6847.000000 ... 0.000000 0.000000\n", + "25% 32.000000 122.000000 ... 0.000000 1.000000\n", + "50% 39.000000 550.000000 ... 0.000000 1.000000\n", + "75% 49.000000 1708.000000 ... 0.000000 1.000000\n", + "max 95.000000 81204.000000 ... 1.000000 1.000000\n", + "\n", + "[8 rows x 27 columns]" + ] + }, + "metadata": {}, + "execution_count": 201 + } + ], + "source": [ + "bank_with_dummies.describe()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "_cell_guid": "e1cc517d-cc97-4eee-a258-8c554ed95332", + "_uuid": "beef0817aff3dbd8c920a4cd91bc6e56f00ecd5a", + "id": "Y_KjIwWtjSOG" + }, + "source": [ + "### Observations on whole population" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "_cell_guid": "e49e7895-a6ac-4b92-9536-f341621d4604", + "_uuid": "3ace943aed1952f64db8239a3e3a5ebdb39dfdc3", + "collapsed": true, + "colab": { + "base_uri": "https://localhost:8080/", + "height": 279 + }, + "id": "VhtBIQAZjSOG", + "outputId": "ad0d2c4c-a930-4f8c-84ca-5fd37d7a5ffd" + }, + "outputs": [ + { + "output_type": "display_data", + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + } + } + ], + "source": [ + "# Scatterplot showing age and balance\n", + "bank_with_dummies.plot(kind='scatter', x='age', y='balance');\n", + "\n", + "# Across all ages, majority of people have savings of less than 20000." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "_cell_guid": "46f48fc7-340c-48bb-8f52-7f9a43d03430", + "_uuid": "f5bccfdf19b6efdaccf2663c114f85ab910fa4a4", + "id": "fwi0C0L9jSOG" + }, + "source": [ + "#### Analysis on people who sign up for a term deposite" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "_cell_guid": "52d4b887-1b35-434d-adf2-243edd1b3347", + "_uuid": "0be3fa7ed1542d993bb5c8bc36b36d6ed7ca8092", + "collapsed": true, + "colab": { + "base_uri": "https://localhost:8080/", + "height": 382 + }, + "id": "u_p522EUjSOG", + "outputId": "d692072d-97cf-4e24-a97b-9068446f2e91" + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "\n", + "
\n", + "
\n", + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
agebalancedurationcampaignpreviousdefault_cathousing_catloan_catdeposit_catrecent_pdaysjob_blue-collarjob_entrepreneurjob_otherjob_pink-collarjob_self-employedjob_technicianjob_white-collarmarital_divorcedmarital_marriedmarital_singleeducation_primaryeducation_secondaryeducation_tertiaryeducation_unknownpoutcome_failurepoutcome_successpoutcome_unknown
count5289.0000005289.0000005289.0000005289.0000005289.0000005289.0000005289.0000005289.0000005289.05289.0000005289.0000005289.0000005289.0000005289.0000005289.0000005289.0000005289.0000005289.0000005289.0000005289.0000005289.0000005289.0000005289.0000005289.0000005289.0000005289.0000005289.000000
mean41.6700701804.267915537.2945742.1410471.1703540.0098320.3658540.0915111.00.0042380.1338630.0232560.1930420.0903760.0353560.1588200.3652860.1176030.5208920.3615050.1117410.4632260.3773870.0476460.1168460.1849120.698242
std13.4977813501.104777392.5252621.9218262.5532720.0986760.4817140.2883610.00.0356860.3405370.1507290.3947230.2867470.1846960.3655430.4815560.3221670.4996110.4804820.3150780.4986930.4847790.2130360.3212670.3882630.459064
min18.000000-3058.0000008.0000001.0000000.0000000.0000000.0000000.0000001.00.0001000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.000000
25%31.000000210.000000244.0000001.0000000.0000000.0000000.0000000.0000001.00.0001000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.000000
50%38.000000733.000000426.0000002.0000000.0000000.0000000.0000000.0000001.00.0001000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000001.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000001.000000
75%50.0000002159.000000725.0000003.0000001.0000000.0000001.0000000.0000001.00.0051280.0000000.0000000.0000000.0000000.0000000.0000001.0000000.0000001.0000001.0000000.0000001.0000001.0000000.0000000.0000000.0000001.000000
max95.00000081204.0000003881.00000032.00000058.0000001.0000001.0000001.0000001.01.0000001.0000001.0000001.0000001.0000001.0000001.0000001.0000001.0000001.0000001.0000001.0000001.0000001.0000001.0000001.0000001.0000001.000000
\n", + "
\n", + " \n", + " \n", + " \n", + "\n", + " \n", + "
\n", + "
\n", + " " + ], + "text/plain": [ + " age balance ... poutcome_success poutcome_unknown\n", + "count 5289.000000 5289.000000 ... 5289.000000 5289.000000\n", + "mean 41.670070 1804.267915 ... 0.184912 0.698242\n", + "std 13.497781 3501.104777 ... 0.388263 0.459064\n", + "min 18.000000 -3058.000000 ... 0.000000 0.000000\n", + "25% 31.000000 210.000000 ... 0.000000 0.000000\n", + "50% 38.000000 733.000000 ... 0.000000 1.000000\n", + "75% 50.000000 2159.000000 ... 0.000000 1.000000\n", + "max 95.000000 81204.000000 ... 1.000000 1.000000\n", + "\n", + "[8 rows x 27 columns]" + ] + }, + "metadata": {}, + "execution_count": 204 + } + ], + "source": [ + "# People who sign up to a term deposite\n", + "bank_with_dummies[bank_data.deposit_cat == 1].describe()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "_cell_guid": "25c8616f-805c-4abf-b792-4b2e8f39cfcf", + "_uuid": "3d999f6b3775dcc7b25993fcd6ccad1838d8a239", + "collapsed": true, + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "ix0iu-GWjSOH", + "outputId": "4ffb96cf-65b0-4b99-bc9c-9a1d519c59ab" + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "265" + ] + }, + "metadata": {}, + "execution_count": 205 + } + ], + "source": [ + "# People signed up to a term deposite having a personal loan (loan_cat) and housing loan (housing_cat)\n", + "len(bank_with_dummies[(bank_with_dummies.deposit_cat == 1) & (bank_with_dummies.loan_cat) & (bank_with_dummies.housing_cat)])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "_cell_guid": "ecb235b0-9f6b-4a35-9a71-efd7525b4836", + "_uuid": "33866e95d069281a68c5f0bcfaa272603e665390", + "collapsed": true, + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "uUGjyL4hjSOH", + "outputId": "e947e93e-edbc-49f2-a782-710e43e03b96" + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "52" + ] + }, + "metadata": {}, + "execution_count": 206 + } + ], + "source": [ + "# People signed up to a term deposite with a credit default \n", + "len(bank_with_dummies[(bank_with_dummies.deposit_cat == 1) & (bank_with_dummies.default_cat ==1)])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "_cell_guid": "fae049f5-fa22-4db2-9e65-2b0088ed6c2c", + "_uuid": "037a420ecb420ad6b97029219d36d4f9147d52a6", + "collapsed": true, + "colab": { + "base_uri": "https://localhost:8080/", + "height": 405 + }, + "id": "MWb3Oax-jSOH", + "outputId": "351d9998-8f9f-4e2c-d266-23c9615d4f23" + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" + ] + }, + "metadata": {}, + "execution_count": 207 + }, + { + "output_type": "display_data", + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + } + } + ], + "source": [ + "# Bar chart of job Vs deposite\n", + "plt.figure(figsize = (10,6))\n", + "sns.barplot(x='job', y = 'deposit_cat', data = bank_data)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "_cell_guid": "1a35a8e5-3df3-470c-9bf1-71a476f587e6", + "_uuid": "7f5cb673253553269fcb783d941d7b37499b9a3e", + "collapsed": true, + "colab": { + "base_uri": "https://localhost:8080/", + "height": 405 + }, + "id": "_DIK-kl5jSOH", + "outputId": "871b5cd6-3191-4c02-e4d1-caf93b51cae1" + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" + ] + }, + "metadata": {}, + "execution_count": 208 + }, + { + "output_type": "display_data", + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + } + } + ], + "source": [ + "# Bar chart of \"previous outcome\" Vs \"call duration\"\n", + "\n", + "plt.figure(figsize = (10,6))\n", + "sns.barplot(x='poutcome', y = 'duration', data = bank_data)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "_cell_guid": "24492c44-5b2a-4878-9236-df9297680902", + "_uuid": "d583dd9fbc7df31d950d78bcb62f2156a6c14ed3", + "id": "LBf4DIGEjSOI" + }, + "source": [ + "> ## Classification" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "_cell_guid": "9365fde6-e602-4996-a79a-2d7e4ec3bee2", + "_uuid": "4482103b89dd40dbb5ac9bbafee6e85da23c27c3", + "collapsed": true, + "id": "N3604bVcjSOI" + }, + "outputs": [], + "source": [ + "# make a copy\n", + "bankcl = bank_with_dummies" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "_cell_guid": "edf85d97-fec6-4f90-a3d0-02a1ec094a08", + "_uuid": "ff08aff610ab1676acc902da63dc35ae1c321673", + "collapsed": true, + "colab": { + "base_uri": "https://localhost:8080/", + "height": 977 + }, + "id": "BPeiKPbEjSOI", + "outputId": "63503e02-67e8-4379-9423-837932279bee" + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "\n", + "
\n", + "
\n", + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
agebalancedurationcampaignpreviousdefault_cathousing_catloan_catdeposit_catrecent_pdaysjob_blue-collarjob_entrepreneurjob_otherjob_pink-collarjob_self-employedjob_technicianjob_white-collarmarital_divorcedmarital_marriedmarital_singleeducation_primaryeducation_secondaryeducation_tertiaryeducation_unknownpoutcome_failurepoutcome_successpoutcome_unknown
age1.0000000.1123000.000189-0.0052780.020169-0.011425-0.168700-0.0314180.0349010.019102-0.0665670.0241760.296418-0.027942-0.023163-0.082716-0.0801220.1863490.318436-0.4677990.231150-0.094400-0.1013720.077761-0.0080710.062114-0.038992
balance0.1123001.0000000.022436-0.0138940.030805-0.060954-0.077092-0.0845890.081129-0.004379-0.0462200.0050390.050744-0.0410630.0202640.0038020.013780-0.0175860.025431-0.014994-0.000673-0.0706090.0691280.0145960.0016950.045603-0.034524
duration0.0001890.0224361.000000-0.041557-0.026716-0.0097600.035051-0.0019140.451919-0.0148680.029986-0.0009080.0106800.0053450.013506-0.010440-0.0319800.021364-0.0361790.0238470.0134050.003820-0.006813-0.015887-0.033966-0.0225780.042725
campaign-0.005278-0.013894-0.0415571.000000-0.0496990.0309750.0066600.034722-0.128081-0.0262960.0055220.013883-0.0502120.0119580.0017760.0217380.001944-0.0068280.047722-0.0461650.019915-0.013834-0.0054270.012976-0.080188-0.0918070.128907
previous0.0201690.030805-0.026716-0.0496991.000000-0.035273-0.000840-0.0226680.1398670.122076-0.039939-0.0224700.031191-0.028623-0.0023380.0020350.034929-0.026566-0.0051760.023817-0.024852-0.0046200.028146-0.0118980.3358700.325477-0.496921
default_cat-0.011425-0.060954-0.0097600.030975-0.0352731.0000000.0110760.076434-0.040680-0.0112900.0227790.022060-0.018130-0.0071730.0074930.003109-0.0134250.019633-0.006819-0.0062550.013858-0.000618-0.0117680.005421-0.024650-0.0402720.048403
housing_cat-0.168700-0.0770920.0350510.006660-0.0008400.0110761.0000000.076761-0.203888-0.0293500.1898480.011492-0.2333090.043884-0.0169030.006551-0.0121110.0074300.036305-0.0438170.0170020.118514-0.114955-0.0531910.087741-0.1362990.031375
loan_cat-0.031418-0.084589-0.0019140.034722-0.0226680.0764340.0767611.000000-0.110580-0.0126970.0579560.042631-0.0961960.0149690.0042990.006864-0.0078710.0264630.044148-0.0652880.0068540.079583-0.067513-0.0502490.006264-0.0803700.053686
deposit_cat0.0349010.0811290.451919-0.1280810.139867-0.040680-0.203888-0.1105801.0000000.034457-0.100840-0.0344430.144408-0.051717-0.004707-0.0115570.0316210.005228-0.0921570.094632-0.063002-0.0519520.0945980.0143550.0207140.286642-0.224785
recent_pdays0.019102-0.004379-0.014868-0.0262960.122076-0.011290-0.029350-0.0126970.0344571.000000-0.0185140.0062510.024356-0.001183-0.008226-0.0074120.004516-0.0202530.0095830.003736-0.007034-0.0171290.0173460.0135900.0514220.119598-0.126890
job_blue-collar-0.066567-0.0462200.0299860.005522-0.0399390.0227790.1898480.057956-0.100840-0.0185141.000000-0.079905-0.185447-0.159162-0.089107-0.202896-0.336538-0.0562400.109188-0.0776450.2997370.076687-0.298548-0.000640-0.018022-0.0774220.070330
job_entrepreneur0.0241760.005039-0.0009080.013883-0.0224700.0220600.0114920.042631-0.0344430.006251-0.0799051.000000-0.070264-0.060305-0.033762-0.076875-0.1275110.0066380.050746-0.058665-0.004788-0.0211320.026612-0.001555-0.001840-0.0350720.026966
job_other0.2964180.0507440.010680-0.0502120.031191-0.018130-0.233309-0.0961960.1444080.024356-0.185447-0.0702641.000000-0.139958-0.078356-0.178415-0.2959330.032824-0.0309820.0104130.114003-0.020532-0.1103830.112986-0.0108650.099733-0.064228
job_pink-collar-0.027942-0.0410630.0053450.011958-0.028623-0.0071730.0438840.014969-0.051717-0.001183-0.159162-0.060305-0.1399581.000000-0.067250-0.153127-0.2539880.0256400.007558-0.0257180.0561500.137129-0.184418-0.004629-0.010816-0.0303310.030459
job_self-employed-0.0231630.0202640.0135060.001776-0.0023380.007493-0.0169030.004299-0.004707-0.008226-0.089107-0.033762-0.078356-0.0672501.000000-0.085728-0.142196-0.011849-0.0081640.016864-0.037121-0.0600800.097929-0.016336-0.010039-0.0013990.008786
job_technician-0.0827160.003802-0.0104400.0217380.0020350.0031090.0065510.006864-0.011557-0.007412-0.202896-0.076875-0.178415-0.153127-0.0857281.000000-0.323778-0.005434-0.0524920.059696-0.1449480.152542-0.041988-0.0342760.005763-0.0147440.006279
job_white-collar-0.0801220.013780-0.0319800.0019440.034929-0.013425-0.012111-0.0078710.0316210.004516-0.336538-0.127511-0.295933-0.253988-0.142196-0.3237781.0000000.010701-0.0432700.038752-0.229245-0.2222610.422261-0.0452330.0293870.033044-0.046804
marital_divorced0.186349-0.0175860.021364-0.006828-0.0265660.0196330.0074300.0264630.005228-0.020253-0.0562400.0066380.0328240.025640-0.011849-0.0054340.0107011.000000-0.415878-0.2455560.0248210.009891-0.024597-0.008920-0.026169-0.0181200.033445
marital_married0.3184360.025431-0.0361790.047722-0.005176-0.0068190.0363050.044148-0.0921570.0095830.1091880.050746-0.0309820.007558-0.008164-0.052492-0.043270-0.4158781.000000-0.7794550.1302320.001536-0.0984490.0054510.007682-0.0100630.001384
marital_single-0.467799-0.0149940.023847-0.0461650.023817-0.006255-0.043817-0.0652880.0946320.003736-0.077645-0.0586650.010413-0.0257180.0168640.0596960.038752-0.245556-0.7794551.000000-0.155917-0.0084500.1218840.0003340.0098380.023208-0.024514
education_primary0.231150-0.0006730.0134050.019915-0.0248520.0138580.0170020.006854-0.063002-0.0070340.299737-0.0047880.1140030.056150-0.037121-0.144948-0.2292450.0248210.130232-0.1559171.000000-0.386670-0.276834-0.085057-0.026044-0.0498790.056477
education_secondary-0.094400-0.0706090.003820-0.013834-0.004620-0.0006180.1185140.079583-0.051952-0.0171290.076687-0.021132-0.0205320.137129-0.0600800.152542-0.2222610.0098910.001536-0.008450-0.3866701.000000-0.689501-0.2118490.010625-0.0294660.013238
education_tertiary-0.1013720.069128-0.006813-0.0054270.028146-0.011768-0.114955-0.0675130.0945980.017346-0.2985480.026612-0.110383-0.1844180.097929-0.0419880.422261-0.024597-0.0984490.121884-0.276834-0.6895011.000000-0.1516720.0122650.059518-0.052836
education_unknown0.0777610.014596-0.0158870.012976-0.0118980.005421-0.053191-0.0502490.0143550.013590-0.000640-0.0015550.112986-0.004629-0.016336-0.034276-0.045233-0.0089200.0054510.000334-0.085057-0.211849-0.1516721.000000-0.0106580.018158-0.004978
poutcome_failure-0.0080710.001695-0.033966-0.0801880.335870-0.0246500.0877410.0062640.0207140.051422-0.018022-0.001840-0.010865-0.010816-0.0100390.0057630.029387-0.0261690.0076820.009838-0.0260440.0106250.012265-0.0106581.000000-0.114542-0.690332
poutcome_success0.0621140.045603-0.022578-0.0918070.325477-0.040272-0.136299-0.0803700.2866420.119598-0.077422-0.0350720.099733-0.030331-0.001399-0.0147440.033044-0.018120-0.0100630.023208-0.049879-0.0294660.0595180.018158-0.1145421.000000-0.639659
poutcome_unknown-0.038992-0.0345240.0427250.128907-0.4969210.0484030.0313750.053686-0.224785-0.1268900.0703300.026966-0.0642280.0304590.0087860.006279-0.0468040.0334450.001384-0.0245140.0564770.013238-0.052836-0.004978-0.690332-0.6396591.000000
\n", + "
\n", + " \n", + " \n", + " \n", + "\n", + " \n", + "
\n", + "
\n", + " " + ], + "text/plain": [ + " age balance ... poutcome_success poutcome_unknown\n", + "age 1.000000 0.112300 ... 0.062114 -0.038992\n", + "balance 0.112300 1.000000 ... 0.045603 -0.034524\n", + "duration 0.000189 0.022436 ... -0.022578 0.042725\n", + "campaign -0.005278 -0.013894 ... -0.091807 0.128907\n", + "previous 0.020169 0.030805 ... 0.325477 -0.496921\n", + "default_cat -0.011425 -0.060954 ... -0.040272 0.048403\n", + "housing_cat -0.168700 -0.077092 ... -0.136299 0.031375\n", + "loan_cat -0.031418 -0.084589 ... -0.080370 0.053686\n", + "deposit_cat 0.034901 0.081129 ... 0.286642 -0.224785\n", + "recent_pdays 0.019102 -0.004379 ... 0.119598 -0.126890\n", + "job_blue-collar -0.066567 -0.046220 ... -0.077422 0.070330\n", + "job_entrepreneur 0.024176 0.005039 ... -0.035072 0.026966\n", + "job_other 0.296418 0.050744 ... 0.099733 -0.064228\n", + "job_pink-collar -0.027942 -0.041063 ... -0.030331 0.030459\n", + "job_self-employed -0.023163 0.020264 ... -0.001399 0.008786\n", + "job_technician -0.082716 0.003802 ... -0.014744 0.006279\n", + "job_white-collar -0.080122 0.013780 ... 0.033044 -0.046804\n", + "marital_divorced 0.186349 -0.017586 ... -0.018120 0.033445\n", + "marital_married 0.318436 0.025431 ... -0.010063 0.001384\n", + "marital_single -0.467799 -0.014994 ... 0.023208 -0.024514\n", + "education_primary 0.231150 -0.000673 ... -0.049879 0.056477\n", + "education_secondary -0.094400 -0.070609 ... -0.029466 0.013238\n", + "education_tertiary -0.101372 0.069128 ... 0.059518 -0.052836\n", + "education_unknown 0.077761 0.014596 ... 0.018158 -0.004978\n", + "poutcome_failure -0.008071 0.001695 ... -0.114542 -0.690332\n", + "poutcome_success 0.062114 0.045603 ... 1.000000 -0.639659\n", + "poutcome_unknown -0.038992 -0.034524 ... -0.639659 1.000000\n", + "\n", + "[27 rows x 27 columns]" + ] + }, + "metadata": {}, + "execution_count": 210 + } + ], + "source": [ + "# The Correltion matrix\n", + "corr = bankcl.corr()\n", + "corr" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "_cell_guid": "2eb696c8-d468-4642-b7e6-ef3deca070ac", + "_uuid": "0f56d9c3946d4a50e0048c89ae8373e2204bc61a", + "collapsed": true, + "colab": { + "base_uri": "https://localhost:8080/", + "height": 624 + }, + "id": "QiVzvS2SjSOI", + "outputId": "2753b00b-57c6-4d22-8fed-d35c42098a73" + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "Text(0.5, 1.0, 'Heatmap of Correlation Matrix')" + ] + }, + "metadata": {}, + "execution_count": 213 + }, + { + "output_type": "display_data", + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + } + } + ], + "source": [ + "# Heatmap\n", + "plt.figure(figsize = (10,10))\n", + "cmap = sns.diverging_palette(220, 10, as_cmap=True)\n", + "sns.heatmap(corr, xticklabels=corr.columns.values, yticklabels=corr.columns.values, cmap=cmap, vmax=.3, center=0, square=True, linewidths=.5, cbar_kws={\"shrink\": .82})\n", + "plt.title('Heatmap of Correlation Matrix')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "_cell_guid": "73994c65-9028-4ddc-98a1-5b7cdb10a113", + "_uuid": "ff6ca40f9fd1e7710aa3b0c0ad020ba73aa34a5e", + "collapsed": true, + "colab": { + "base_uri": "https://localhost:8080/", + "height": 865 + }, + "id": "eAzYBXGqjSOJ", + "outputId": "f5c58f5d-34b5-4cab-a678-7b6d7882c29d" + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "\n", + "
\n", + "
\n", + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
deposit_cat
duration0.451919
poutcome_success0.286642
job_other0.144408
previous0.139867
marital_single0.094632
education_tertiary0.094598
balance0.081129
age0.034901
recent_pdays0.034457
job_white-collar0.031621
poutcome_failure0.020714
education_unknown0.014355
marital_divorced0.005228
job_self-employed-0.004707
job_technician-0.011557
job_entrepreneur-0.034443
default_cat-0.040680
job_pink-collar-0.051717
education_secondary-0.051952
education_primary-0.063002
marital_married-0.092157
job_blue-collar-0.100840
loan_cat-0.110580
campaign-0.128081
housing_cat-0.203888
poutcome_unknown-0.224785
\n", + "
\n", + " \n", + " \n", + " \n", + "\n", + " \n", + "
\n", + "
\n", + " " + ], + "text/plain": [ + " deposit_cat\n", + "duration 0.451919\n", + "poutcome_success 0.286642\n", + "job_other 0.144408\n", + "previous 0.139867\n", + "marital_single 0.094632\n", + "education_tertiary 0.094598\n", + "balance 0.081129\n", + "age 0.034901\n", + "recent_pdays 0.034457\n", + "job_white-collar 0.031621\n", + "poutcome_failure 0.020714\n", + "education_unknown 0.014355\n", + "marital_divorced 0.005228\n", + "job_self-employed -0.004707\n", + "job_technician -0.011557\n", + "job_entrepreneur -0.034443\n", + "default_cat -0.040680\n", + "job_pink-collar -0.051717\n", + "education_secondary -0.051952\n", + "education_primary -0.063002\n", + "marital_married -0.092157\n", + "job_blue-collar -0.100840\n", + "loan_cat -0.110580\n", + "campaign -0.128081\n", + "housing_cat -0.203888\n", + "poutcome_unknown -0.224785" + ] + }, + "metadata": {}, + "execution_count": 214 + } + ], + "source": [ + "# Extract the deposte_cat column (the dependent variable)\n", + "corr_deposite = pd.DataFrame(corr['deposit_cat'].drop('deposit_cat'))\n", + "corr_deposite.sort_values(by = 'deposit_cat', ascending = False)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "_cell_guid": "c281ea25-423f-4477-bab5-e1c262d20931", + "_uuid": "0394776ec1c21f2c30fc66a1ce7a22c7a5e9df5f", + "id": "PkC-G_rfjSOJ" + }, + "source": [ + "> ## Build the Data Model" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "_cell_guid": "5675ce89-28f8-409d-9387-ce4641bc4a7b", + "_uuid": "3b14efa859cd38fce1e118ceae7144d8563dc014", + "collapsed": true, + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "emXrBWQwjSOJ", + "outputId": "2617badf-d078-412b-91ed-97e231bca3ac" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:2: FutureWarning:\n", + "\n", + "In a future version of pandas all arguments of DataFrame.drop except for the argument 'labels' will be keyword-only\n", + "\n" + ] + } + ], + "source": [ + "# Train-Test split: 20% test data\n", + "data_drop_deposite = bankcl.drop('deposit_cat', 1)\n", + "label = bankcl.deposit_cat\n", + "data_train, data_test, label_train, label_test = train_test_split(data_drop_deposite, label, test_size = 0.2, random_state = 50)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "_cell_guid": "7b8c8772-a2ec-4399-807e-73ccc4951dd1", + "_uuid": "4a48b73d67f4f57349d3dbc75c4d02cfeb1d82f9", + "collapsed": true, + "scrolled": true, + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "UdYHY7sFjSOK", + "outputId": "4705992f-0371-443a-c24a-4bf5ba5a3495" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Training score: 0.7285250307985217\n", + "Testing score: 0.7268248992386923\n" + ] + } + ], + "source": [ + "# Decision tree with depth = 2\n", + "dt2 = tree.DecisionTreeClassifier(random_state=1, max_depth=2)\n", + "dt2.fit(data_train, label_train)\n", + "dt2_score_train = dt2.score(data_train, label_train)\n", + "print(\"Training score: \",dt2_score_train)\n", + "dt2_score_test = dt2.score(data_test, label_test)\n", + "print(\"Testing score: \",dt2_score_test)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "_cell_guid": "14b771bd-b87e-4ac4-b3ec-64da8bbb7b68", + "_uuid": "c828f56ae83bf3eac076c3642ad26e51f660d643", + "collapsed": true, + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "k3muNWcxjSOK", + "outputId": "31726294-07b8-4669-ac77-0b2a481c13d7" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Training score: 0.770411020271027\n", + "Testing score: 0.7572772055530677\n" + ] + } + ], + "source": [ + "# Decision tree with depth = 3\n", + "dt3 = tree.DecisionTreeClassifier(random_state=1, max_depth=3)\n", + "dt3.fit(data_train, label_train)\n", + "dt3_score_train = dt3.score(data_train, label_train)\n", + "print(\"Training score: \",dt3_score_train)\n", + "dt3_score_test = dt3.score(data_test, label_test)\n", + "print(\"Testing score: \",dt3_score_test)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "_cell_guid": "60190973-9780-4b6a-9762-7e43d442db6b", + "_uuid": "f45e7da9e4052b837a3c363f03de439d16cca6d0", + "collapsed": true, + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "Sz_KKDSgjSOK", + "outputId": "5a0ea4d7-d8eb-424c-eb59-d8a54cfb917f" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Training score: 0.7885541494008288\n", + "Testing score: 0.774294670846395\n" + ] + } + ], + "source": [ + "# Decision tree with depth = 4\n", + "dt4 = tree.DecisionTreeClassifier(random_state=1, max_depth=4)\n", + "dt4.fit(data_train, label_train)\n", + "dt4_score_train = dt4.score(data_train, label_train)\n", + "print(\"Training score: \",dt4_score_train)\n", + "dt4_score_test = dt4.score(data_test, label_test)\n", + "print(\"Testing score: \",dt4_score_test)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "_cell_guid": "0f61abaa-9d1e-4fa7-83e7-23f11bdf90e8", + "_uuid": "a3b51e1daf328cc5157234c3e4bf0be5174ce62f", + "collapsed": true, + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "HY5r1BWajSOK", + "outputId": "570eab5b-7d84-4e53-c372-dc1721bb0a61" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Training score: 0.8080412140217269\n", + "Testing score: 0.7796686072548141\n" + ] + } + ], + "source": [ + "# Decision tree with depth = 6\n", + "dt6 = tree.DecisionTreeClassifier(random_state=1, max_depth=6)\n", + "dt6.fit(data_train, label_train)\n", + "dt6_score_train = dt6.score(data_train, label_train)\n", + "print(\"Training score: \",dt6_score_train)\n", + "dt6_score_test = dt6.score(data_test, label_test)\n", + "print(\"Testing score: \",dt6_score_test)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "_cell_guid": "32a4d8a7-b285-47c2-988c-8aa6a66933e2", + "_uuid": "cabee45b6dd75bd0aac571320e300c5d2c94c045", + "collapsed": true, + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "26B_n7VFjSOL", + "outputId": "57396820-fddd-4b09-95bd-257edb34b376" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Training score: 1.0\n", + "Testing score: 0.7366771159874608\n" + ] + } + ], + "source": [ + "# Decision tree: To the full depth\n", + "dt1 = tree.DecisionTreeClassifier()\n", + "dt1.fit(data_train, label_train)\n", + "dt1_score_train = dt1.score(data_train, label_train)\n", + "print(\"Training score: \", dt1_score_train)\n", + "dt1_score_test = dt1.score(data_test, label_test)\n", + "print(\"Testing score: \", dt1_score_test)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "_cell_guid": "12a029ef-2d45-452c-927d-2f826b5f9a68", + "_uuid": "e1c1dad3c3e239f3f4b19d4b15da9949c2bd9ec2", + "id": "kgKWFZj9jSOL" + }, + "source": [ + "#### Compare Training and Testing scores for various tree depths used" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "_cell_guid": "6fb20c58-b5dc-4944-9b78-258d2f2e0da2", + "_uuid": "92eee54d59182aeed9b828fe2a57818ffcfcbb31", + "collapsed": true, + "scrolled": true, + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "2y2E0riKjSOL", + "outputId": "6f6266fb-4877-47d8-cb95-a6ac12a8fc48" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "depth Training score Testing score \n", + "----- -------------- ------------- \n", + "2 0.7285250307985217 0.7268248992386923\n", + "3 0.770411020271027 0.7572772055530677\n", + "4 0.7885541494008288 0.774294670846395\n", + "6 0.8080412140217269 0.7796686072548141\n", + "max 1.0 0.7366771159874608\n" + ] + } + ], + "source": [ + "print('{:10} {:20} {:20}'.format('depth', 'Training score','Testing score'))\n", + "print('{:10} {:20} {:20}'.format('-----', '--------------','-------------'))\n", + "print('{:1} {:>25} {:>20}'.format(2, dt2_score_train, dt2_score_test))\n", + "print('{:1} {:>25} {:>20}'.format(3, dt3_score_train, dt3_score_test))\n", + "print('{:1} {:>25} {:>20}'.format(4, dt4_score_train, dt4_score_test))\n", + "print('{:1} {:>25} {:>20}'.format(6, dt6_score_train, dt6_score_test))\n", + "print('{:1} {:>23} {:>20}'.format(\"max\", dt1_score_train, dt1_score_test))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "_cell_guid": "a2ee959f-14f5-4236-8d7f-bfe781d37f49", + "_uuid": "9262f9c49fb675e6b7a107e66ea959868b9176b4", + "id": "LcDhekTOjSOL" + }, + "source": [ + "It could be seen that, higher the depth, training score increases and matches perfects with the training data set. However higher the depth the tree goes, it overfit to the training data set. So it's no use keep increasing the tree depth. According to above observations, tree with a depth of 2 seems more reasonable as both training and test scores are reasonably high." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "_cell_guid": "868f9549-40a9-45be-b91a-b1b185a882c8", + "_uuid": "051771a3c38a6c2880611ac28c74b3c513baf7bd", + "collapsed": true, + "colab": { + "base_uri": "https://localhost:8080/", + "height": 575 + }, + "id": "SWt3rXrbjSOM", + "outputId": "ed42e508-5259-41e3-ac46-04061826d37a" + }, + "outputs": [ + { + "output_type": "display_data", + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + } + } + ], + "source": [ + "# Let's generate the decision tree for depth = 2\n", + "# Create a feature vector\n", + "# from sklearn.tree import export_graphviz\n", + "# features = bankcl.columns.tolist()\n", + "# features\n", + "plt.figure(figsize=(18, 10))\n", + "from sklearn import tree\n", + "tree.plot_tree(dt2, max_depth=2, filled=True)\n", + "plt.show()\n", + "# Uncomment below to generate the digraph Tree.\n", + "# tree.export_graphviz(dt2, out_file='tree_depth_2.dot', feature_names=features)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "_cell_guid": "11e301f0-fd01-4411-bb0d-4dd26c0333ce", + "_uuid": "7780912e57e3e5a3f862b3ba3006c64a7c084249", + "id": "evnOESiLjSOM" + }, + "source": [ + "Based on the decision tree results, it could be seen that higher the \"duration\", bank is able to sign up more people to term deposites." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "_cell_guid": "eafef089-0ec3-4915-8db1-783a06c77eec", + "_uuid": "ba242f2e1caef4966ebe0f0e7eec41dcc7daebc0", + "collapsed": true, + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "B4VqyzwyjSOM", + "outputId": "afe8915c-9509-4c0d-c121-d45eaf91dc62" + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "array([0, 1])" + ] + }, + "metadata": {}, + "execution_count": 228 + } + ], + "source": [ + "# Two classes: 0 = not signed up, 1 = signed up\n", + "dt2.classes_" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "_cell_guid": "2a4d1e56-ec88-471b-b530-57f9c420e447", + "_uuid": "e047c485745e224de5e9a76f656dca9f54760a86", + "collapsed": true, + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "1dHt8-znjSON", + "outputId": "d0ec158b-0f9b-4640-9955-a0818de0c95b" + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "['age',\n", + " 'balance',\n", + " 'duration',\n", + " 'campaign',\n", + " 'previous',\n", + " 'default_cat',\n", + " 'housing_cat',\n", + " 'loan_cat',\n", + " 'recent_pdays',\n", + " 'job_blue-collar',\n", + " 'job_entrepreneur',\n", + " 'job_other',\n", + " 'job_pink-collar',\n", + " 'job_self-employed',\n", + " 'job_technician',\n", + " 'job_white-collar',\n", + " 'marital_divorced',\n", + " 'marital_married',\n", + " 'marital_single',\n", + " 'education_primary',\n", + " 'education_secondary',\n", + " 'education_tertiary',\n", + " 'education_unknown',\n", + " 'poutcome_failure',\n", + " 'poutcome_success',\n", + " 'poutcome_unknown']" + ] + }, + "metadata": {}, + "execution_count": 229 + } + ], + "source": [ + "# Create a feature vector\n", + "features = data_drop_deposite.columns.tolist()\n", + "\n", + "features" + ] + }, + { + "cell_type": "markdown", + "source": [ + "##HyperParameter Tunning \n", + "**GridSearch CV**" + ], + "metadata": { + "id": "SFP6SmeHdByv" + } + }, + { + "cell_type": "code", + "source": [ + "from sklearn.tree import DecisionTreeClassifier\n", + "from sklearn.model_selection import GridSearchCV\n", + "\n", + "def dtree_grid_search(X,y,nfolds):\n", + " #create a dictionary of all values we want to test\n", + " param_grid = { 'criterion':['gini','entropy'],'max_depth': np.arange(1, 10)}\n", + " # decision tree model\n", + " dtree_model=DecisionTreeClassifier()\n", + " #use gridsearch to test all values\n", + " dtree_gscv = GridSearchCV(dtree_model, param_grid, cv=nfolds)\n", + " #fit model to data\n", + " dtree_gscv.fit(X, y)\n", + " return dtree_gscv.best_params_" + ], + "metadata": { + "id": "bu3i4DhqdBKG" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "# data_train, data_test, label_train, label_test\n", + "print(dtree_grid_search(data_train, label_train, 5))" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "GW5WRx_CeU9i", + "outputId": "0ccdb083-01ef-4241-e724-45e415d0ce46" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "{'criterion': 'entropy', 'max_depth': 6}\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "hDt = DecisionTreeClassifier(criterion='entropy', max_depth=6)\n", + "hDt.fit(data_train, label_train)\n" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "Vu5yQFNFizwc", + "outputId": "0dfdb918-2605-4ff2-eb7b-c1f0d9bf94c4" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "DecisionTreeClassifier(criterion='entropy', max_depth=6)" + ] + }, + "metadata": {}, + "execution_count": 242 + } + ] + }, + { + "cell_type": "code", + "source": [ + "hDt_score_train = hDt.score(data_train, label_train)\n", + "print(\"Training score: \", hDt_score_train)\n", + "hDt_score_test = hDt.score(data_test, label_test)\n", + "print(\"Testing score: \", hDt_score_test)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "WGbnu9ibjODS", + "outputId": "7e2abb1b-3cf1-462d-cded-101cd680b7a5" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Training score: 0.8072572516519207\n", + "Testing score: 0.7814599193909538\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "fi = hDt.feature_importances_\n", + "\n", + "l = len(features)\n", + "for i in range(0, len(features)):\n", + " print('{:.<20} {:3} '.format(features[i], fi[i]))" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "ywLRzyXVj0VJ", + "outputId": "fd608351-94ca-43b3-e7c1-6d0c24b6cb03" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "age................. 0.0322194077868486 \n", + "balance............. 0.034645817621518214 \n", + "duration............ 0.5850070911505163 \n", + "campaign............ 0.017328135495228898 \n", + "previous............ 0.015113434294132418 \n", + "default_cat......... 0.0 \n", + "housing_cat......... 0.103014050777686 \n", + "loan_cat............ 0.0019087945046288106 \n", + "recent_pdays........ 0.015399467520187043 \n", + "job_blue-collar..... 0.0 \n", + "job_entrepreneur.... 0.0 \n", + "job_other........... 0.0 \n", + "job_pink-collar..... 0.0 \n", + "job_self-employed... 0.0 \n", + "job_technician...... 0.0 \n", + "job_white-collar.... 0.0008448795174435656 \n", + "marital_divorced.... 0.0 \n", + "marital_married..... 0.004593648690515507 \n", + "marital_single...... 0.00452510545265586 \n", + "education_primary... 0.0 \n", + "education_secondary. 0.0 \n", + "education_tertiary.. 0.0 \n", + "education_unknown... 0.0 \n", + "poutcome_failure.... 0.0005306766764535086 \n", + "poutcome_success.... 0.1848694905121852 \n", + "poutcome_unknown.... 0.0 \n" + ] + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "_cell_guid": "3eba0270-7d71-4254-b1b9-03d25eae3a23", + "_uuid": "caf2f2bfd1e3549d35359927dea802c3e8f3e40e", + "id": "cHUmtaLGjSON" + }, + "source": [ + "## Predictions" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "_cell_guid": "7d43908a-0eef-4719-90a2-96e486ee3743", + "_uuid": "34edc756644878ec7c1cdbe10310485dc9b386a7", + "collapsed": true, + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "psb3agu8jSON", + "outputId": "13d255b8-9207-4baf-b5cb-1e7dbe3257c1" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Mean duration : 371.99381831213043\n", + "Maximun duration: 3881\n", + "Minimum duration: 2\n" + ] + } + ], + "source": [ + "# According to feature importance results, most importtant feature is the \"Duration\"\n", + "# Let's calculte statistics on Duration\n", + "print(\"Mean duration : \", data_drop_deposite.duration.mean())\n", + "print(\"Maximun duration: \", data_drop_deposite.duration.max())\n", + "print(\"Minimum duration: \", data_drop_deposite.duration.min())" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "_cell_guid": "44c20639-552b-4941-98ca-492aaaf987e3", + "_uuid": "f90cc3423d1135a1f0fb8f4850088935de2cc7d3", + "collapsed": true, + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "MtZ5I4SDjSOO", + "outputId": "997f4103-292d-40e6-bbee-27e92dd9c61e" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "[[0.48515568 0.51484432]]\n", + "[1]\n" + ] + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "/usr/local/lib/python3.7/dist-packages/sklearn/base.py:451: UserWarning:\n", + "\n", + "X does not have valid feature names, but DecisionTreeClassifier was fitted with feature names\n", + "\n", + "/usr/local/lib/python3.7/dist-packages/sklearn/base.py:451: UserWarning:\n", + "\n", + "X does not have valid feature names, but DecisionTreeClassifier was fitted with feature names\n", + "\n" + ] + } + ], + "source": [ + "# Predict: Successful deposite with a call duration = 371 sec\n", + "\n", + "print(dt2.predict_proba(np.array([0, 0, 371, 0, 0, 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0]).reshape(1, -1)))\n", + "print(dt2.predict(np.array([0, 0, 371, 0, 0, 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0]).reshape(1, -1)))\n", + "# column 0: probability for class 0 (not signed for term deposite) & column 1: probability for class 1\n", + "# Probability of Successful deposite = 0.51484432" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "_cell_guid": "45ce9752-9a0e-49ae-8ae6-62f39e984fc1", + "_uuid": "01b7620cb265ebae4ec3289e20b49405324d25f0", + "collapsed": true, + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "0gH4KGSJjSOO", + "outputId": "325b62c9-5e79-44c9-a6b5-ff1f6b97e58a" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "[[0.01219512 0.98780488]]\n", + "[1]\n" + ] + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "/usr/local/lib/python3.7/dist-packages/sklearn/base.py:451: UserWarning:\n", + "\n", + "X does not have valid feature names, but DecisionTreeClassifier was fitted with feature names\n", + "\n", + "/usr/local/lib/python3.7/dist-packages/sklearn/base.py:451: UserWarning:\n", + "\n", + "X does not have valid feature names, but DecisionTreeClassifier was fitted with feature names\n", + "\n" + ] + } + ], + "source": [ + "# Predict: Successful deposite with a maximun call duration = 3881 sec\n", + "\n", + "print(hDt.predict_proba(np.array([0, 0, 3881, 0, 0, 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0]).reshape(1, -1)))\n", + "print(hDt.predict(np.array([0, 0, 3881, 0, 0, 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0]).reshape(1, -1)))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "_cell_guid": "49cc5af7-e45e-465e-8496-f6e64ddffa06", + "_uuid": "0f0d54a2141b6a7906a93fbc0cf73b2cfe1dfecc", + "collapsed": true, + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "PUyWIDH_jSOO", + "outputId": "04fbe96f-4dc2-454b-e05b-cef66b84b76b" + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "age 46.000000\n", + "balance 3354.000000\n", + "duration 522.000000\n", + "campaign 1.000000\n", + "previous 1.000000\n", + "default_cat 0.000000\n", + "housing_cat 1.000000\n", + "loan_cat 0.000000\n", + "recent_pdays 0.005747\n", + "job_blue-collar 0.000000\n", + "job_entrepreneur 0.000000\n", + "job_other 1.000000\n", + "job_pink-collar 0.000000\n", + "job_self-employed 0.000000\n", + "job_technician 0.000000\n", + "job_white-collar 0.000000\n", + "marital_divorced 1.000000\n", + "marital_married 0.000000\n", + "marital_single 0.000000\n", + "education_primary 0.000000\n", + "education_secondary 1.000000\n", + "education_tertiary 0.000000\n", + "education_unknown 0.000000\n", + "poutcome_failure 0.000000\n", + "poutcome_success 1.000000\n", + "poutcome_unknown 0.000000\n", + "Name: 985, dtype: float64" + ] + }, + "metadata": {}, + "execution_count": 252 + } + ], + "source": [ + "# Get a row with poutcome_success = 1\n", + "#bank_with_dummies[(bank_with_dummies.poutcome_success == 1)]\n", + "data_drop_deposite.iloc[985]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "_cell_guid": "2e1045dd-ab80-4ebd-8daa-9b51c217f1cc", + "_uuid": "735a8e7498209fd60fd7ece2d610e0cd12793715", + "collapsed": true, + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "Tect9bKejSOP", + "outputId": "2cae1189-b832-4ae5-ecc1-d00bfaec85bb" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "[[0.19295499 0.80704501]]\n", + "[[0. 1.]]\n" + ] + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "/usr/local/lib/python3.7/dist-packages/sklearn/base.py:451: UserWarning:\n", + "\n", + "X does not have valid feature names, but DecisionTreeClassifier was fitted with feature names\n", + "\n", + "/usr/local/lib/python3.7/dist-packages/sklearn/base.py:451: UserWarning:\n", + "\n", + "X does not have valid feature names, but DecisionTreeClassifier was fitted with feature names\n", + "\n" + ] + } + ], + "source": [ + "# Predict: Probability for above\n", + "\n", + "print(dt2.predict_proba(np.array([46,3354,522,1,1,0,1,0,0.005747,0,0,1,0,0,0,0,1,0,0,0,1,0,0,0,1,0]).reshape(1, -1)))\n", + "print(hDt.predict_proba(np.array([46,3354,522,1,1,0,1,0,0.005747,0,0,1,0,0,0,0,1,0,0,0,1,0,0,0,1,0]).reshape(1, -1)))" + ] + }, + { + "cell_type": "code", + "source": [ + "# Make predictions on the test set\n", + "preds = hDt.predict(data_test)\n", + "\n", + "# Calculate accuracy\n", + "print(\"\\nAccuracy score: \\n{}\".format(metrics.accuracy_score(label_test, preds)))\n", + "\n", + "# Make predictions on the test set using predict_proba\n", + "probs = hDt.predict_proba(data_test)[:,1]\n", + "\n", + "# Calculate the AUC metric\n", + "print(\"\\nArea Under Curve: \\n{}\".format(metrics.roc_auc_score(label_test, probs)))" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "JQnPz3Yum367", + "outputId": "7954ca5d-a856-4666-fbc3-24939172af55" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\n", + "Accuracy score: \n", + "0.7814599193909538\n", + "\n", + "Area Under Curve: \n", + "0.8568148916662646\n" + ] + } + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "_cell_guid": "1a0663b5-bb57-4bee-83cb-3713cd1bfd49", + "_uuid": "7b6c6107fab5f61fedc06523adb4457b7521001d", + "collapsed": true, + "scrolled": true, + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "yuRpx2ajjSOP", + "outputId": "bb25e8a1-b2c8-446d-d411-7623bf8cd770" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\n", + "Accuracy score: \n", + "0.7268248992386923\n", + "\n", + "Area Under Curve: \n", + "0.7880265888143609\n" + ] + } + ], + "source": [ + "# Make predictions on the test set\n", + "preds = dt2.predict(data_test)\n", + "\n", + "# Calculate accuracy\n", + "print(\"\\nAccuracy score: \\n{}\".format(metrics.accuracy_score(label_test, preds)))\n", + "\n", + "# Make predictions on the test set using predict_proba\n", + "probs = dt2.predict_proba(data_test)[:,1]\n", + "\n", + "# Calculate the AUC metric\n", + "print(\"\\nArea Under Curve: \\n{}\".format(metrics.roc_auc_score(label_test, probs)))" + ] + }, + { + "cell_type": "markdown", + "source": [ + "##confusion matrix " + ], + "metadata": { + "id": "rvK-VZ-yqFMH" + } + }, + { + "cell_type": "code", + "source": [ + "from sklearn.metrics import confusion_matrix\n" + ], + "metadata": { + "id": "fdXY8FJTnNgW" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "y_predTrain = hDt.predict(data_train)\n", + "confusion_matrix(label_train, y_predTrain)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "oUe-35K8cbn_", + "outputId": "31bf8d4c-bf32-4380-e8c0-291f99945e9f" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "array([[3791, 909],\n", + " [ 812, 3417]])" + ] + }, + "metadata": {}, + "execution_count": 273 + } + ] + }, + { + "cell_type": "code", + "source": [ + "ypredTest = hDt.predict(data_test)" + ], + "metadata": { + "id": "BbdruFUWoKlc" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "confusion_matrix(label_test, ypredTest)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "OSe5OzoToliE", + "outputId": "be34cc53-fec6-489b-d110-92d26a1ff23d" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "array([[912, 261],\n", + " [227, 833]])" + ] + }, + "metadata": {}, + "execution_count": 275 + } + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.4" + }, + "colab": { + "name": "Solution Decision Tree.ipynb", + "provenance": [], + "collapsed_sections": [ + "7tdtMFRCjSN_", + "QuXz1WcojSOA", + "Ti0iUujFjSOB", + "Lx4MVHu2jSOC", + "0gZW1UaujSOC", + "huql5gAajSOC", + "j3rFMDx0jSOD", + "o8-RUiIhjSOD", + "2xP7i2LBjSOF", + "Y_KjIwWtjSOG", + "fwi0C0L9jSOG", + "LBf4DIGEjSOI", + "PkC-G_rfjSOJ", + "kgKWFZj9jSOL", + "SFP6SmeHdByv", + "cHUmtaLGjSON", + "rvK-VZ-yqFMH" + ], + "include_colab_link": true + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} \ No newline at end of file