From 97e0dd0ec0a2fa60ddb8e4fb3eff3ed9f1bb867e Mon Sep 17 00:00:00 2001 From: Nileena <45648156+nil-pc@users.noreply.github.com> Date: Fri, 25 Oct 2019 19:27:24 +0530 Subject: [PATCH 1/4] Initial commit --- README.md | 2 ++ 1 file changed, 2 insertions(+) create mode 100644 README.md diff --git a/README.md b/README.md new file mode 100644 index 0000000..a9a3858 --- /dev/null +++ b/README.md @@ -0,0 +1,2 @@ +# Pattern-Recognition---Assignment +Pattern Recognition Assignment to predict client subscription for a bank From 968bb1af1248d0b1789c013826061b89f562f847 Mon Sep 17 00:00:00 2001 From: Nileena <45648156+nil-pc@users.noreply.github.com> Date: Fri, 25 Oct 2019 19:28:16 +0530 Subject: [PATCH 2/4] Created using Colaboratory --- pr.ipynb | 3700 ++++++++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 3700 insertions(+) create mode 100644 pr.ipynb diff --git a/pr.ipynb b/pr.ipynb new file mode 100644 index 0000000..bcdd62e --- /dev/null +++ b/pr.ipynb @@ -0,0 +1,3700 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "name": "pr.ipynb", + "provenance": [], + "collapsed_sections": [], + "include_colab_link": true + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + } + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "view-in-github", + "colab_type": "text" + }, + "source": [ + "\"Open" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "VBPnw_-NjzB8", + "colab_type": "code", + "colab": {} + }, + "source": [ + "#just a file to try all codes!!!!!!!!!!!!!!" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "QTGLd5-XKAFF", + "colab_type": "code", + "colab": {} + }, + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "from sklearn import preprocessing\n", + "import matplotlib.pyplot as plt \n", + "plt.rc(\"font\", size=14)\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.pipeline import make_pipeline\n", + "from sklearn import metrics\n", + "import seaborn as sns\n", + "sns.set(style=\"white\")\n", + "sns.set(style=\"whitegrid\", color_codes=True)\n", + "from sklearn import datasets, linear_model" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "teIvuYPCLFPN", + "colab_type": "code", + "outputId": "1bd0e159-658c-41d9-edce-aaebf3706228", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 73 + } + }, + "source": [ + "data=pd.read_csv('bank-additional-full.csv',sep=';')\n", + "data=data.dropna()\n", + "print(data.shape)\n", + "print(list(data.columns))" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "stream", + "text": [ + "(41188, 21)\n", + "['age', 'job', 'marital', 'education', 'default', 'housing', 'loan', 'contact', 'month', 'day_of_week', 'duration', 'campaign', 'pdays', 'previous', 'poutcome', 'emp.var.rate', 'cons.price.idx', 'cons.conf.idx', 'euribor3m', 'nr.employed', 'y']\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "euv_tB5xU27f", + "colab_type": "code", + "outputId": "5a4b11d7-33fd-4fa0-e33e-b287e64065f7", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 73 + } + }, + "source": [ + "dat=pd.read_csv('bank-additional.csv',sep=';')\n", + "dat=dat.dropna()\n", + "print(dat.shape)\n", + "print(list(dat.columns))" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "stream", + "text": [ + "(4119, 21)\n", + "['age', 'job', 'marital', 'education', 'default', 'housing', 'loan', 'contact', 'month', 'day_of_week', 'duration', 'campaign', 'pdays', 'previous', 'poutcome', 'emp.var.rate', 'cons.price.idx', 'cons.conf.idx', 'euribor3m', 'nr.employed', 'y']\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "k1OuPD7TP3Ta", + "colab_type": "code", + "outputId": "30c72d43-03e8-4233-d3e6-47ae3b2014fb", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 255 + } + }, + "source": [ + "data.head()" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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agejobmaritaleducationdefaulthousingloancontactmonthday_of_week...campaignpdayspreviouspoutcomeemp.var.ratecons.price.idxcons.conf.idxeuribor3mnr.employedy
056housemaidmarriedbasic.4ynononotelephonemaymon...19990nonexistent1.193.994-36.44.8575191.0no
157servicesmarriedhigh.schoolunknownnonotelephonemaymon...19990nonexistent1.193.994-36.44.8575191.0no
237servicesmarriedhigh.schoolnoyesnotelephonemaymon...19990nonexistent1.193.994-36.44.8575191.0no
340admin.marriedbasic.6ynononotelephonemaymon...19990nonexistent1.193.994-36.44.8575191.0no
456servicesmarriedhigh.schoolnonoyestelephonemaymon...19990nonexistent1.193.994-36.44.8575191.0no
\n", + "

5 rows × 21 columns

\n", + "
" + ], + "text/plain": [ + " age job marital education default housing loan contact \\\n", + "0 56 housemaid married basic.4y no no no telephone \n", + "1 57 services married high.school unknown no no telephone \n", + "2 37 services married high.school no yes no telephone \n", + "3 40 admin. married basic.6y no no no telephone \n", + "4 56 services married high.school no no yes telephone \n", + "\n", + " month day_of_week ... campaign pdays previous poutcome emp.var.rate \\\n", + "0 may mon ... 1 999 0 nonexistent 1.1 \n", + "1 may mon ... 1 999 0 nonexistent 1.1 \n", + "2 may mon ... 1 999 0 nonexistent 1.1 \n", + "3 may mon ... 1 999 0 nonexistent 1.1 \n", + "4 may mon ... 1 999 0 nonexistent 1.1 \n", + "\n", + " cons.price.idx cons.conf.idx euribor3m nr.employed y \n", + "0 93.994 -36.4 4.857 5191.0 no \n", + "1 93.994 -36.4 4.857 5191.0 no \n", + "2 93.994 -36.4 4.857 5191.0 no \n", + "3 93.994 -36.4 4.857 5191.0 no \n", + "4 93.994 -36.4 4.857 5191.0 no \n", + "\n", + "[5 rows x 21 columns]" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 84 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "hLB3v50fQZ7I", + "colab_type": "code", + "outputId": "71313220-8260-4d5d-8d5c-59d29df2f0e9", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 72 + } + }, + "source": [ + "data['education'].unique()" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "array(['basic.4y', 'high.school', 'basic.6y', 'basic.9y',\n", + " 'professional.course', 'unknown', 'university.degree',\n", + " 'illiterate'], dtype=object)" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 85 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "jJw_uhRtSzdh", + "colab_type": "code", + "colab": {} + }, + "source": [ + "data['education']=np.where(data['education'] =='basic.9y', 'Basic', data['education'])\n", + "data['education']=np.where(data['education'] =='basic.6y', 'Basic', data['education'])\n", + "data['education']=np.where(data['education'] =='basic.4y', 'Basic', data['education'])" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "K85vf6CrQyPV", + "colab_type": "code", + "outputId": "991c1b1f-ae1f-4daf-c0b2-0804f8d92f1f", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 53 + } + }, + "source": [ + "data['education'].unique()" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "array(['Basic', 'high.school', 'professional.course', 'unknown',\n", + " 'university.degree', 'illiterate'], dtype=object)" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 87 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "U-uG7W_ni_un", + "colab_type": "code", + "outputId": "41e8ddb9-5578-4e53-f2a6-c706079f4719", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 72 + } + }, + "source": [ + "data['y'].value_counts()" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "no 36548\n", + "yes 4640\n", + "Name: y, dtype: int64" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 88 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "lqnQ-BP2je3B", + "colab_type": "code", + "outputId": "8704118f-6ebb-4d0f-cc47-bfe49bcccf01", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 436 + } + }, + "source": [ + "sns.countplot(x='y',data=data,palette='hls')\n", + "plt.show()\n", + "plt.savefig('count_plot')" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "stream", + "text": [ + "/usr/local/lib/python3.6/dist-packages/seaborn/categorical.py:1428: FutureWarning: remove_na is deprecated and is a private function. Do not use.\n", + " stat_data = remove_na(group_data)\n" + ], + "name": "stderr" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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" + ] + }, + "metadata": { + "tags": [] + } + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "V12fzEAljqdo", + "colab_type": "code", + "outputId": "dde4d6b5-1adf-414a-f5c0-dcdd0dead3de", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 53 + } + }, + "source": [ + "count_no_sub = len(data[data['y']=='no'])\n", + "count_sub = len(data[data['y']=='yes'])\n", + "pct_of_no_sub = count_no_sub/(count_no_sub+count_sub)\n", + "print(\"percentage of no subscription is\", pct_of_no_sub*100)\n", + "pct_of_sub = count_sub/(count_no_sub+count_sub)\n", + "print(\"percentage of subscription\", pct_of_sub*100)" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "stream", + "text": [ + "percentage of no subscription is 88.73458288821988\n", + "percentage of subscription 11.265417111780131\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "FT_C0qFR9ygQ", + "colab_type": "code", + "outputId": "7cea1fc5-cc4f-4af7-f4b3-fb58fff119d2", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 143 + } + }, + "source": [ + "data.groupby('y').mean()" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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agedurationcampaignpdayspreviousemp.var.ratecons.price.idxcons.conf.idxeuribor3mnr.employed
y
no39.911185220.8448072.633085984.1138780.1323740.24887593.603757-40.5930973.8114915176.166600
yes40.913147553.1911642.051724792.0355600.492672-1.23344893.354386-39.7897842.1231355095.115991
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" + ], + "text/plain": [ + " age duration campaign pdays previous emp.var.rate \\\n", + "y \n", + "no 39.911185 220.844807 2.633085 984.113878 0.132374 0.248875 \n", + "yes 40.913147 553.191164 2.051724 792.035560 0.492672 -1.233448 \n", + "\n", + " cons.price.idx cons.conf.idx euribor3m nr.employed \n", + "y \n", + "no 93.603757 -40.593097 3.811491 5176.166600 \n", + "yes 93.354386 -39.789784 2.123135 5095.115991 " + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 91 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "sQfO5BJOkB5D", + "colab_type": "code", + "outputId": "8e65ffe2-7024-4130-f955-8a20ae045fc7", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 494 + } + }, + "source": [ + "data.groupby('job').mean()" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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agedurationcampaignpdayspreviousemp.var.ratecons.price.idxcons.conf.idxeuribor3mnr.employed
job
admin.38.187296254.3121282.623489954.3192290.1890230.01556393.534054-40.2454333.5502745164.125350
blue-collar39.555760264.5423602.558461985.1603630.1225420.24899593.656656-41.3758163.7719965175.615150
entrepreneur41.723214263.2678572.535714981.2671700.1387360.15872393.605372-41.2836543.7911205176.313530
housemaid45.500000250.4547172.639623960.5792450.1377360.43339693.676576-39.4952834.0096455179.529623
management42.362859257.0581402.476060962.6470590.185021-0.01268893.522755-40.4894663.6113165166.650513
retired62.027326273.7122092.476744897.9360470.327326-0.69831493.430786-38.5730812.7700665122.262151
self-employed39.949331264.1421532.660802976.6213930.1435610.09415993.559982-40.4881073.6893765170.674384
services37.926430258.3980852.587805979.9740490.1549510.17535993.634659-41.2900483.6991875171.600126
student25.894857283.6834292.104000840.2171430.524571-1.40800093.331613-40.1875431.8842245085.939086
technician38.507638250.2322412.577339964.4081270.1537890.27456693.561471-39.9275693.8204015175.648391
unemployed39.733728249.4516772.564103935.3165680.199211-0.11173693.563781-40.0075943.4665835157.156509
unknown45.563636239.6757582.648485938.7272730.1545450.35787993.718942-38.7978793.9490335172.931818
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" + ], + "text/plain": [ + " age duration campaign pdays previous \\\n", + "job \n", + "admin. 38.187296 254.312128 2.623489 954.319229 0.189023 \n", + "blue-collar 39.555760 264.542360 2.558461 985.160363 0.122542 \n", + "entrepreneur 41.723214 263.267857 2.535714 981.267170 0.138736 \n", + "housemaid 45.500000 250.454717 2.639623 960.579245 0.137736 \n", + "management 42.362859 257.058140 2.476060 962.647059 0.185021 \n", + "retired 62.027326 273.712209 2.476744 897.936047 0.327326 \n", + "self-employed 39.949331 264.142153 2.660802 976.621393 0.143561 \n", + "services 37.926430 258.398085 2.587805 979.974049 0.154951 \n", + "student 25.894857 283.683429 2.104000 840.217143 0.524571 \n", + "technician 38.507638 250.232241 2.577339 964.408127 0.153789 \n", + "unemployed 39.733728 249.451677 2.564103 935.316568 0.199211 \n", + "unknown 45.563636 239.675758 2.648485 938.727273 0.154545 \n", + "\n", + " emp.var.rate cons.price.idx cons.conf.idx euribor3m \\\n", + "job \n", + "admin. 0.015563 93.534054 -40.245433 3.550274 \n", + "blue-collar 0.248995 93.656656 -41.375816 3.771996 \n", + "entrepreneur 0.158723 93.605372 -41.283654 3.791120 \n", + "housemaid 0.433396 93.676576 -39.495283 4.009645 \n", + "management -0.012688 93.522755 -40.489466 3.611316 \n", + "retired -0.698314 93.430786 -38.573081 2.770066 \n", + "self-employed 0.094159 93.559982 -40.488107 3.689376 \n", + "services 0.175359 93.634659 -41.290048 3.699187 \n", + "student -1.408000 93.331613 -40.187543 1.884224 \n", + "technician 0.274566 93.561471 -39.927569 3.820401 \n", + "unemployed -0.111736 93.563781 -40.007594 3.466583 \n", + "unknown 0.357879 93.718942 -38.797879 3.949033 \n", + "\n", + " nr.employed \n", + "job \n", + "admin. 5164.125350 \n", + "blue-collar 5175.615150 \n", + "entrepreneur 5176.313530 \n", + "housemaid 5179.529623 \n", + "management 5166.650513 \n", + "retired 5122.262151 \n", + "self-employed 5170.674384 \n", + "services 5171.600126 \n", + "student 5085.939086 \n", + "technician 5175.648391 \n", + "unemployed 5157.156509 \n", + "unknown 5172.931818 " + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 92 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "b_hhnAuk9mxn", + "colab_type": "code", + "outputId": "e158bd30-4aca-4fd6-ac77-c23b9760ac68", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 206 + } + }, + "source": [ + "data.groupby('marital').mean()" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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agedurationcampaignpdayspreviousemp.var.ratecons.price.idxcons.conf.idxeuribor3mnr.employed
marital
divorced44.899393253.7903302.61340968.6398530.1686900.16398593.606563-40.7070693.7156035170.878643
married42.307165257.4386232.57281967.2476730.1556080.18362593.597367-40.2706593.7458325171.848772
single33.158714261.5243782.53380949.9095780.211359-0.16798993.517300-40.9186983.3174475155.199265
unknown40.275000312.7250003.18750937.1000000.275000-0.22125093.471250-40.8200003.3130375157.393750
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" + ], + "text/plain": [ + " age duration campaign pdays previous emp.var.rate \\\n", + "marital \n", + "divorced 44.899393 253.790330 2.61340 968.639853 0.168690 0.163985 \n", + "married 42.307165 257.438623 2.57281 967.247673 0.155608 0.183625 \n", + "single 33.158714 261.524378 2.53380 949.909578 0.211359 -0.167989 \n", + "unknown 40.275000 312.725000 3.18750 937.100000 0.275000 -0.221250 \n", + "\n", + " cons.price.idx cons.conf.idx euribor3m nr.employed \n", + "marital \n", + "divorced 93.606563 -40.707069 3.715603 5170.878643 \n", + "married 93.597367 -40.270659 3.745832 5171.848772 \n", + "single 93.517300 -40.918698 3.317447 5155.199265 \n", + "unknown 93.471250 -40.820000 3.313037 5157.393750 " + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 93 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "gFUzJiVL-G_o", + "colab_type": "code", + "outputId": "d6bfc261-f113-4182-9449-d493473c1a98", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 289 + } + }, + "source": [ + "data.groupby('education').mean()" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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agedurationcampaignpdayspreviousemp.var.ratecons.price.idxcons.conf.idxeuribor3mnr.employed
education
Basic42.163910263.0438742.559498974.8779670.1410530.19132993.639933-40.9275953.7296545172.014113
high.school37.998213260.8868102.568576964.3583820.1859170.03293793.584857-40.9406413.5561575164.994735
illiterate48.500000276.7777782.277778943.8333330.111111-0.13333393.317333-39.9500003.5165565171.777778
professional.course40.080107252.5338552.586115960.7659740.1630750.17301293.569864-40.1241083.7104575170.155979
university.degree38.879191253.2233732.563527951.8076920.192390-0.02809093.493466-39.9758053.5296635163.226298
unknown43.481225262.3905262.596187942.8307340.2264590.05909993.658615-39.8778163.5710985159.549509
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" + ], + "text/plain": [ + " age duration campaign pdays previous \\\n", + "education \n", + "Basic 42.163910 263.043874 2.559498 974.877967 0.141053 \n", + "high.school 37.998213 260.886810 2.568576 964.358382 0.185917 \n", + "illiterate 48.500000 276.777778 2.277778 943.833333 0.111111 \n", + "professional.course 40.080107 252.533855 2.586115 960.765974 0.163075 \n", + "university.degree 38.879191 253.223373 2.563527 951.807692 0.192390 \n", + "unknown 43.481225 262.390526 2.596187 942.830734 0.226459 \n", + "\n", + " emp.var.rate cons.price.idx cons.conf.idx euribor3m \\\n", + "education \n", + "Basic 0.191329 93.639933 -40.927595 3.729654 \n", + "high.school 0.032937 93.584857 -40.940641 3.556157 \n", + "illiterate -0.133333 93.317333 -39.950000 3.516556 \n", + "professional.course 0.173012 93.569864 -40.124108 3.710457 \n", + "university.degree -0.028090 93.493466 -39.975805 3.529663 \n", + "unknown 0.059099 93.658615 -39.877816 3.571098 \n", + "\n", + " nr.employed \n", + "education \n", + "Basic 5172.014113 \n", + "high.school 5164.994735 \n", + "illiterate 5171.777778 \n", + "professional.course 5170.155979 \n", + "university.degree 5163.226298 \n", + "unknown 5159.549509 " + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 94 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "nSbf32JfkI85", + "colab_type": "code", + "outputId": "f1ff7ea3-fb44-43af-e5ed-7a4f6dc9d760", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 573 + } + }, + "source": [ + "%matplotlib inline\n", + "pd.crosstab(data.job,data.y).plot(kind='bar')\n", + "plt.title('Purchase Frequency for Job Title')\n", + "plt.xlabel('Job')\n", + "plt.ylabel('Frequency of Purchase')\n", + "plt.savefig('purchase_fre_job')" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "error", + "ename": "TypeError", + "evalue": "ignored", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcrosstab\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjob\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkind\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'bar'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtitle\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Purchase Frequency for Job Title'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mxlabel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Job'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 5\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mylabel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Frequency of Purchase'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 6\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msavefig\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'purchase_fre_job'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mTypeError\u001b[0m: 'str' object is not callable" + ] + }, + { + "output_type": "display_data", + "data": { + "image/png": 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5 rows × 62 columns

\n", + "
" + ], + "text/plain": [ + " age duration campaign pdays previous emp.var.rate cons.price.idx \\\n", + "0 56 261 1 999 0 1.1 93.994 \n", + "1 57 149 1 999 0 1.1 93.994 \n", + "2 37 226 1 999 0 1.1 93.994 \n", + "3 40 151 1 999 0 1.1 93.994 \n", + "4 56 307 1 999 0 1.1 93.994 \n", + "\n", + " cons.conf.idx euribor3m nr.employed ... month_oct \\\n", + "0 -36.4 4.857 5191.0 ... 0 \n", + "1 -36.4 4.857 5191.0 ... 0 \n", + "2 -36.4 4.857 5191.0 ... 0 \n", + "3 -36.4 4.857 5191.0 ... 0 \n", + "4 -36.4 4.857 5191.0 ... 0 \n", + "\n", + " month_sep day_of_week_fri day_of_week_mon day_of_week_thu \\\n", + "0 0 0 1 0 \n", + "1 0 0 1 0 \n", + "2 0 0 1 0 \n", + "3 0 0 1 0 \n", + "4 0 0 1 0 \n", + "\n", + " day_of_week_tue day_of_week_wed poutcome_failure poutcome_nonexistent \\\n", + "0 0 0 0 1 \n", + "1 0 0 0 1 \n", + "2 0 0 0 1 \n", + "3 0 0 0 1 \n", + "4 0 0 0 1 \n", + "\n", + " poutcome_success \n", + "0 0 \n", + "1 0 \n", + "2 0 \n", + "3 0 \n", + "4 0 \n", + "\n", + "[5 rows x 62 columns]" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 96 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "j8kc-MRFyr-Q", + "colab_type": "code", + "outputId": "478a006e-30a8-4333-87a8-3f0a3f56a6bc", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 55 + } + }, + "source": [ + "print(list(data.columns))" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "stream", + "text": [ + "['age', 'duration', 'campaign', 'pdays', 'previous', 'emp.var.rate', 'cons.price.idx', 'cons.conf.idx', 'euribor3m', 'nr.employed', 'y', 'job_admin.', 'job_blue-collar', 'job_entrepreneur', 'job_housemaid', 'job_management', 'job_retired', 'job_self-employed', 'job_services', 'job_student', 'job_technician', 'job_unemployed', 'job_unknown', 'marital_divorced', 'marital_married', 'marital_single', 'marital_unknown', 'education_Basic', 'education_high.school', 'education_illiterate', 'education_professional.course', 'education_university.degree', 'education_unknown', 'default_no', 'default_unknown', 'default_yes', 'housing_no', 'housing_unknown', 'housing_yes', 'loan_no', 'loan_unknown', 'loan_yes', 'contact_cellular', 'contact_telephone', 'month_apr', 'month_aug', 'month_dec', 'month_jul', 'month_jun', 'month_mar', 'month_may', 'month_nov', 'month_oct', 'month_sep', 'day_of_week_fri', 'day_of_week_mon', 'day_of_week_thu', 'day_of_week_tue', 'day_of_week_wed', 'poutcome_failure', 'poutcome_nonexistent', 'poutcome_success']\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "xCb9vEiyyv0d", + "colab_type": "code", + "outputId": "542588ea-c4c4-4f0c-a2b6-9bfd6d30bd67", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 344 + } + }, + "source": [ + "data.columns.values" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "array(['age', 'duration', 'campaign', 'pdays', 'previous', 'emp.var.rate',\n", + " 'cons.price.idx', 'cons.conf.idx', 'euribor3m', 'nr.employed', 'y',\n", + " 'job_admin.', 'job_blue-collar', 'job_entrepreneur',\n", + " 'job_housemaid', 'job_management', 'job_retired',\n", + " 'job_self-employed', 'job_services', 'job_student',\n", + " 'job_technician', 'job_unemployed', 'job_unknown',\n", + " 'marital_divorced', 'marital_married', 'marital_single',\n", + " 'marital_unknown', 'education_Basic', 'education_high.school',\n", + " 'education_illiterate', 'education_professional.course',\n", + " 'education_university.degree', 'education_unknown', 'default_no',\n", + " 'default_unknown', 'default_yes', 'housing_no', 'housing_unknown',\n", + " 'housing_yes', 'loan_no', 'loan_unknown', 'loan_yes',\n", + " 'contact_cellular', 'contact_telephone', 'month_apr', 'month_aug',\n", + " 'month_dec', 'month_jul', 'month_jun', 'month_mar', 'month_may',\n", + " 'month_nov', 'month_oct', 'month_sep', 'day_of_week_fri',\n", + " 'day_of_week_mon', 'day_of_week_thu', 'day_of_week_tue',\n", + " 'day_of_week_wed', 'poutcome_failure', 'poutcome_nonexistent',\n", + " 'poutcome_success'], dtype=object)" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 98 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "2rCmZD2Yznci", + "colab_type": "code", + "outputId": "ac6ba83b-7c60-490c-fc0e-d4fedf7e6ca0", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 341 + } + }, + "source": [ + "y_count = data.y.value_counts()\n", + "print('Class 0:', y_count[0])\n", + "print('Class 1:', y_count[1])\n", + "print('Proportion:', round(y_count[0] / y_count[1], 2), ': 1')\n", + "\n", + "y_count.plot(kind='bar', title='Count (y)');" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Class 0: 36548\n", + "Class 1: 4640\n", + "Proportion: 7.88 : 1\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "-NU-ibu51gUs", + "colab_type": "code", + "colab": {} + }, + "source": [ + "# Class count\n", + "count_class_0, count_class_1 = data.y.value_counts()\n", + "\n", + "# Divide by class\n", + "data_class_0 = data[data['y'] == 'no']\n", + "data_class_1 = data[data['y'] == 'yes']" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "tHiZUhiA2OVs", + "colab_type": "code", + "outputId": "c2510647-382b-4465-be12-a14d70ff0813", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 359 + } + }, + "source": [ + "data_class_1_over = data_class_1.sample(count_class_0,replace=True)\n", + "data = pd.concat([data_class_0, data_class_1_over], axis=0)\n", + "\n", + "print('Random over-sampling:')\n", + "print(data.y.value_counts())\n", + "\n", + "data.y.value_counts().plot(kind='bar', title='Count (y)');" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Random over-sampling:\n", + "no 36548\n", + "yes 36548\n", + "Name: y, dtype: int64\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "LmxgIuvynsXS", + "colab_type": "code", + "outputId": "764fd06e-28c7-4629-b4e4-bc69ae5ad795", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 341 + } + }, + "source": [ + "y_count = data.y.value_counts()\n", + "print('Class 0:', y_count[0])\n", + "print('Class 1:', y_count[1])\n", + "print('Proportion:', round(y_count[0] / y_count[1], 2), ': 1')\n", + "\n", + "y_count.plot(kind='bar', title='Count (y)');" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Class 0: 36548\n", + "Class 1: 36548\n", + "Proportion: 1.0 : 1\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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3401511-101.193.994-36.44.8575191.0...0001000010
4563071-101.193.994-36.44.8575191.0...0001000010
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5 rows × 62 columns

\n", + "
" + ], + "text/plain": [ + " age duration campaign pdays previous emp.var.rate cons.price.idx \\\n", + "0 56 261 1 -1 0 1.1 93.994 \n", + "1 57 149 1 -1 0 1.1 93.994 \n", + "2 37 226 1 -1 0 1.1 93.994 \n", + "3 40 151 1 -1 0 1.1 93.994 \n", + "4 56 307 1 -1 0 1.1 93.994 \n", + "\n", + " cons.conf.idx euribor3m nr.employed ... month_oct \\\n", + "0 -36.4 4.857 5191.0 ... 0 \n", + "1 -36.4 4.857 5191.0 ... 0 \n", + "2 -36.4 4.857 5191.0 ... 0 \n", + "3 -36.4 4.857 5191.0 ... 0 \n", + "4 -36.4 4.857 5191.0 ... 0 \n", + "\n", + " month_sep day_of_week_fri day_of_week_mon day_of_week_thu \\\n", + "0 0 0 1 0 \n", + "1 0 0 1 0 \n", + "2 0 0 1 0 \n", + "3 0 0 1 0 \n", + "4 0 0 1 0 \n", + "\n", + " day_of_week_tue day_of_week_wed poutcome_failure poutcome_nonexistent \\\n", + "0 0 0 0 1 \n", + "1 0 0 0 1 \n", + "2 0 0 0 1 \n", + "3 0 0 0 1 \n", + "4 0 0 0 1 \n", + "\n", + " poutcome_success \n", + "0 0 \n", + "1 0 \n", + "2 0 \n", + "3 0 \n", + "4 0 \n", + "\n", + "[5 rows x 62 columns]" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 106 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "DzKmFG8r1Buc", + "colab_type": "code", + "outputId": "6e7a13b1-f279-43cf-e603-6a50ae136a25", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 35 + } + }, + "source": [ + "data['y'].unique()" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "array(['no', 'yes'], dtype=object)" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 107 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "KUJborikMnlq", + "colab_type": "code", + "outputId": "8678898b-52ce-412e-96df-cba178edf61a", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 55 + } + }, + "source": [ + "print(list(data.columns))" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "stream", + "text": [ + "['age', 'duration', 'campaign', 'pdays', 'previous', 'emp.var.rate', 'cons.price.idx', 'cons.conf.idx', 'euribor3m', 'nr.employed', 'y', 'job_admin.', 'job_blue-collar', 'job_entrepreneur', 'job_housemaid', 'job_management', 'job_retired', 'job_self-employed', 'job_services', 'job_student', 'job_technician', 'job_unemployed', 'job_unknown', 'marital_divorced', 'marital_married', 'marital_single', 'marital_unknown', 'education_Basic', 'education_high.school', 'education_illiterate', 'education_professional.course', 'education_university.degree', 'education_unknown', 'default_no', 'default_unknown', 'default_yes', 'housing_no', 'housing_unknown', 'housing_yes', 'loan_no', 'loan_unknown', 'loan_yes', 'contact_cellular', 'contact_telephone', 'month_apr', 'month_aug', 'month_dec', 'month_jul', 'month_jun', 'month_mar', 'month_may', 'month_nov', 'month_oct', 'month_sep', 'day_of_week_fri', 'day_of_week_mon', 'day_of_week_thu', 'day_of_week_tue', 'day_of_week_wed', 'poutcome_failure', 'poutcome_nonexistent', 'poutcome_success']\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "Q0CyKVu29wdd", + "colab_type": "code", + "colab": {} + }, + "source": [ + "from mlxtend.preprocessing import minmax_scaling\n", + "from sklearn.preprocessing import minmax_scale\n", + "from sklearn.preprocessing import normalize" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "PuRG24t4FkzM", + "colab_type": "code", + "outputId": "45e398b5-af7c-458a-9ccc-b7ae45cea4d4", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 255 + } + }, + "source": [ + "data.head()" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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agedurationcampaignpdayspreviousemp.var.ratecons.price.idxcons.conf.idxeuribor3mnr.employed...month_octmonth_sepday_of_week_friday_of_week_monday_of_week_thuday_of_week_tueday_of_week_wedpoutcome_failurepoutcome_nonexistentpoutcome_success
0562611-101.193.994-36.44.8575191.0...0001000010
1571491-101.193.994-36.44.8575191.0...0001000010
2372261-101.193.994-36.44.8575191.0...0001000010
3401511-101.193.994-36.44.8575191.0...0001000010
4563071-101.193.994-36.44.8575191.0...0001000010
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5 rows × 62 columns

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" + ], + "text/plain": [ + " age duration campaign pdays previous emp.var.rate cons.price.idx \\\n", + "0 56 261 1 -1 0 1.1 93.994 \n", + "1 57 149 1 -1 0 1.1 93.994 \n", + "2 37 226 1 -1 0 1.1 93.994 \n", + "3 40 151 1 -1 0 1.1 93.994 \n", + "4 56 307 1 -1 0 1.1 93.994 \n", + "\n", + " cons.conf.idx euribor3m nr.employed ... month_oct \\\n", + "0 -36.4 4.857 5191.0 ... 0 \n", + "1 -36.4 4.857 5191.0 ... 0 \n", + "2 -36.4 4.857 5191.0 ... 0 \n", + "3 -36.4 4.857 5191.0 ... 0 \n", + "4 -36.4 4.857 5191.0 ... 0 \n", + "\n", + " month_sep day_of_week_fri day_of_week_mon day_of_week_thu \\\n", + "0 0 0 1 0 \n", + "1 0 0 1 0 \n", + "2 0 0 1 0 \n", + "3 0 0 1 0 \n", + "4 0 0 1 0 \n", + "\n", + " day_of_week_tue day_of_week_wed poutcome_failure poutcome_nonexistent \\\n", + "0 0 0 0 1 \n", + "1 0 0 0 1 \n", + "2 0 0 0 1 \n", + "3 0 0 0 1 \n", + "4 0 0 0 1 \n", + "\n", + " poutcome_success \n", + "0 0 \n", + "1 0 \n", + "2 0 \n", + "3 0 \n", + "4 0 \n", + "\n", + "[5 rows x 62 columns]" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 110 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "AeGflexhnvMO", + "colab_type": "code", + "colab": {} + }, + "source": [ + "col_to_use=['age', 'duration', 'campaign', 'pdays', 'previous', 'emp.var.rate', 'cons.price.idx', 'cons.conf.idx', 'euribor3m', 'nr.employed', 'job_admin.', 'job_blue-collar', 'job_entrepreneur', 'job_housemaid', 'job_management', 'job_retired', 'job_self-employed', 'job_services', 'job_student', 'job_technician', 'job_unemployed', 'job_unknown', 'marital_divorced', 'marital_married', 'marital_single', 'marital_unknown', 'education_Basic', 'education_high.school', 'education_illiterate', 'education_professional.course', 'education_university.degree', 'education_unknown', 'default_no', 'default_unknown', 'default_yes', 'housing_no', 'housing_unknown', 'housing_yes', 'loan_no', 'loan_unknown', 'loan_yes', 'contact_cellular', 'contact_telephone', 'month_apr', 'month_aug', 'month_dec', 'month_jul', 'month_jun', 'month_mar', 'month_may', 'month_nov', 'month_oct', 'day_of_week_fri', 'day_of_week_mon', 'day_of_week_thu', 'day_of_week_tue', 'day_of_week_wed', 'poutcome_failure', 'poutcome_nonexistent', 'poutcome_success']\n", + "X=data[col_to_use]\n", + "y=data.y\n" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "rmkWOmEEzs4l", + "colab_type": "code", + "outputId": "966a80d1-e9ff-40d0-c819-6b7341170e51", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 255 + } + }, + "source": [ + "from sklearn.linear_model import LogisticRegression\n", + "from sklearn.model_selection import cross_val_score\n", + "lm = LogisticRegression()\n", + "scores=(cross_val_score(lm,X,y,cv=5))\n", + "print(scores)\n", + "print(\"Accuracy: %0.2f (+/- %0.2f)\" % (scores.mean(),scores.std()*2)) " + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "stream", + "text": [ + "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n", + " FutureWarning)\n", + "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n", + " FutureWarning)\n", + "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n", + " FutureWarning)\n", + "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n", + " FutureWarning)\n", + "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n", + " FutureWarning)\n" + ], + "name": "stderr" + }, + { + "output_type": "stream", + "text": [ + "[0.88276334 0.89384405 0.89829001 0.73867834 0.64427418]\n", + "Accuracy: 0.81 (+/- 0.21)\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "a6ZpJSthHlcA", + "colab_type": "code", + "outputId": "35f11fb7-a012-410e-cf7b-01cf3301aced", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 92 + } + }, + "source": [ + "from sklearn.metrics import accuracy_score\n", + "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=0)\n", + "lm.fit(X_train,y_train)\n", + "pred=lm.predict(X_test)\n", + "print(accuracy_score(pred,y_test))" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "stream", + "text": [ + "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n", + " FutureWarning)\n" + ], + "name": "stderr" + }, + { + "output_type": "stream", + "text": [ + "0.868874885996186\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "5Ggp_LS8JNmg", + "colab_type": "code", + "outputId": "b1f9db89-960d-412c-bdf7-cfeb49f500a4", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 146 + } + }, + "source": [ + "from sklearn.preprocessing import StandardScaler\n", + "sc=StandardScaler()\n", + "X_train_std=sc.fit_transform(X_train)\n", + "X_test_std=sc.transform(X_test)" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "stream", + "text": [ + "/usr/local/lib/python3.6/dist-packages/sklearn/preprocessing/data.py:645: DataConversionWarning: Data with input dtype uint8, int64, float64 were all converted to float64 by StandardScaler.\n", + " return self.partial_fit(X, y)\n", + "/usr/local/lib/python3.6/dist-packages/sklearn/base.py:464: DataConversionWarning: Data with input dtype uint8, int64, float64 were all converted to float64 by StandardScaler.\n", + " return self.fit(X, **fit_params).transform(X)\n", + "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:4: DataConversionWarning: Data with input dtype uint8, int64, float64 were all converted to float64 by StandardScaler.\n", + " after removing the cwd from sys.path.\n" + ], + "name": "stderr" + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "DcPBZ5cXKDNA", + "colab_type": "code", + "outputId": "ac0b74ed-3478-4304-e527-9860a7f3774d", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 92 + } + }, + "source": [ + "lm.fit(X_train_std,y_train)\n", + "pred=lm.predict(X_test_std)\n", + "print(accuracy_score(pred,y_test))" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "stream", + "text": [ + "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n", + " FutureWarning)\n" + ], + "name": "stderr" + }, + { + "output_type": "stream", + "text": [ + "0.8699941961694718\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "1SF1QFHTLLpm", + "colab_type": "code", + "outputId": "a2f1f30c-a3ef-4e7f-dc62-99411cc61eee", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 253 + } + }, + "source": [ + "X_train_std" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "array([[-1.53951579, -0.64831503, -0.14603055, ..., 2.77633517,\n", + " -1.89560599, -0.33862485],\n", + " [ 1.56584223, -0.01778706, -0.14603055, ..., 2.77633517,\n", + " -1.89560599, -0.33862485],\n", + " [-0.02880108, -0.75569752, -0.14603055, ..., -0.36018706,\n", + " -1.89560599, 2.95312055],\n", + " ...,\n", + " [ 0.22298471, 2.18217736, -0.56873284, ..., 2.77633517,\n", + " -1.89560599, -0.33862485],\n", + " [-0.95201562, -0.70338298, -0.14603055, ..., 2.77633517,\n", + " -1.89560599, -0.33862485],\n", + " [ 0.97834206, -0.32341415, 0.27667174, ..., -0.36018706,\n", + " 0.52753579, -0.33862485]])" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 116 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "D9ajm5uQLw-A", + "colab_type": "code", + "outputId": "db3083e1-408f-40a3-8b67-5a2ac9ed9b05", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 255 + } + }, + "source": [ + "data.head()" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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agedurationcampaignpdayspreviousemp.var.ratecons.price.idxcons.conf.idxeuribor3mnr.employed...month_octmonth_sepday_of_week_friday_of_week_monday_of_week_thuday_of_week_tueday_of_week_wedpoutcome_failurepoutcome_nonexistentpoutcome_success
0562611-101.193.994-36.44.8575191.0...0001000010
1571491-101.193.994-36.44.8575191.0...0001000010
2372261-101.193.994-36.44.8575191.0...0001000010
3401511-101.193.994-36.44.8575191.0...0001000010
4563071-101.193.994-36.44.8575191.0...0001000010
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5 rows × 62 columns

\n", + "
" + ], + "text/plain": [ + " age duration campaign pdays previous emp.var.rate cons.price.idx \\\n", + "0 56 261 1 -1 0 1.1 93.994 \n", + "1 57 149 1 -1 0 1.1 93.994 \n", + "2 37 226 1 -1 0 1.1 93.994 \n", + "3 40 151 1 -1 0 1.1 93.994 \n", + "4 56 307 1 -1 0 1.1 93.994 \n", + "\n", + " cons.conf.idx euribor3m nr.employed ... month_oct \\\n", + "0 -36.4 4.857 5191.0 ... 0 \n", + "1 -36.4 4.857 5191.0 ... 0 \n", + "2 -36.4 4.857 5191.0 ... 0 \n", + "3 -36.4 4.857 5191.0 ... 0 \n", + "4 -36.4 4.857 5191.0 ... 0 \n", + "\n", + " month_sep day_of_week_fri day_of_week_mon day_of_week_thu \\\n", + "0 0 0 1 0 \n", + "1 0 0 1 0 \n", + "2 0 0 1 0 \n", + "3 0 0 1 0 \n", + "4 0 0 1 0 \n", + "\n", + " day_of_week_tue day_of_week_wed poutcome_failure poutcome_nonexistent \\\n", + "0 0 0 0 1 \n", + "1 0 0 0 1 \n", + "2 0 0 0 1 \n", + "3 0 0 0 1 \n", + "4 0 0 0 1 \n", + "\n", + " poutcome_success \n", + "0 0 \n", + "1 0 \n", + "2 0 \n", + "3 0 \n", + "4 0 \n", + "\n", + "[5 rows x 62 columns]" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 117 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "u5U2cDxO-1V7", + "colab_type": "code", + "outputId": "1b5d7ca5-f621-437b-e1a8-7c5cd1e7f67f", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 55 + } + }, + "source": [ + "\"\"\"data['age'] = minmax_scale(data['age'])\n", + "data['duration']= minmax_scale(data['duration'])\n", + "data['campaign']= minmax_scale(data['campaign'])\n", + "data['pdays']= minmax_scale(data['pdays'])\n", + "data['previous']= minmax_scale(data['previous'])\n", + "data['emp.var.rate']= minmax_scale(data['emp.var.rate'])\n", + "data['cons.price.idx']= minmax_scale(data['cons.price.idx'])\n", + "data['cons.conf.idx']= minmax_scale(data['cons.conf.idx'])\n", + "data['euribor3m']= minmax_scale(data['euribor3m'])\n", + "data['nr.employed']= minmax_scale(data['nr.employed'])\"\"\"" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "\"data['age'] = minmax_scale(data['age'])\\ndata['duration']= minmax_scale(data['duration'])\\ndata['campaign']= minmax_scale(data['campaign'])\\ndata['pdays']= minmax_scale(data['pdays'])\\ndata['previous']= minmax_scale(data['previous'])\\ndata['emp.var.rate']= minmax_scale(data['emp.var.rate'])\\ndata['cons.price.idx']= minmax_scale(data['cons.price.idx'])\\ndata['cons.conf.idx']= minmax_scale(data['cons.conf.idx'])\\ndata['euribor3m']= minmax_scale(data['euribor3m'])\\ndata['nr.employed']= minmax_scale(data['nr.employed'])\"" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 118 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "xrw2nMjoMJ16", + "colab_type": "code", + "outputId": "36b59832-e0c9-4115-e71f-b9c7a6acb20d", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 73 + } + }, + "source": [ + "from sklearn.preprocessing import MinMaxScaler\n", + "sc=MinMaxScaler()\n", + "X_train_mm=sc.fit_transform(X_train)\n", + "X_test_mm=sc.transform(X_test)" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "stream", + "text": [ + "/usr/local/lib/python3.6/dist-packages/sklearn/preprocessing/data.py:334: DataConversionWarning: Data with input dtype uint8, int64, float64 were all converted to float64 by MinMaxScaler.\n", + " return self.partial_fit(X, y)\n" + ], + "name": "stderr" + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "bhc5NQBMMTuj", + "colab_type": "code", + "outputId": "4579b5b4-2d05-40cb-df1e-dd0ff24d4403", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 92 + } + }, + "source": [ + "lm.fit(X_train_mm,y_train)\n", + "pred=lm.predict(X_test_mm)\n", + "print(accuracy_score(pred,y_test))" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "stream", + "text": [ + "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n", + " FutureWarning)\n" + ], + "name": "stderr" + }, + { + "output_type": "stream", + "text": [ + "0.8671751927700854\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "cLOvFbqrMzKn", + "colab_type": "code", + "outputId": "2de7a529-6466-4286-c6e9-e4fdd0484975", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 73 + } + }, + "source": [ + "my_pipe=make_pipeline(MinMaxScaler(),StandardScaler())\n", + "X_train_ms=my_pipe.fit_transform(X_train)\n", + "X_test_ms=my_pipe.transform(X_test)" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "stream", + "text": [ + "/usr/local/lib/python3.6/dist-packages/sklearn/preprocessing/data.py:334: DataConversionWarning: Data with input dtype uint8, int64, float64 were all converted to float64 by MinMaxScaler.\n", + " return self.partial_fit(X, y)\n" + ], + "name": "stderr" + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "isxrt4FRN6S0", + "colab_type": "code", + "outputId": "dd5582dd-ef39-4d12-f3ed-d7c03433a013", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 92 + } + }, + "source": [ + "lm.fit(X_train_ms,y_train)\n", + "pred=lm.predict(X_test_ms)\n", + "print(accuracy_score(pred,y_test))" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "stream", + "text": [ + "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n", + " FutureWarning)\n" + ], + "name": "stderr" + }, + { + "output_type": "stream", + "text": [ + "0.8699941961694718\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "YQ-b0P2JOfLv", + "colab_type": "code", + "outputId": "7b3bee05-657c-4523-b167-d547138af3a1", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 201 + } + }, + "source": [ + "my_pipe=make_pipeline(StandardScaler(),MinMaxScaler())\n", + "\n", + "X_train_sm=my_pipe.fit_transform(X_train)\n", + "\n", + "X_test_sm=my_pipe.transform(X_test)\n", + "lm.fit(X_train_sm,y_train)\n", + "pred=lm.predict(X_test_sm)\n", + "print(accuracy_score(pred,y_test))" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "stream", + "text": [ + "/usr/local/lib/python3.6/dist-packages/sklearn/preprocessing/data.py:645: DataConversionWarning: Data with input dtype uint8, int64, float64 were all converted to float64 by StandardScaler.\n", + " return self.partial_fit(X, y)\n", + "/usr/local/lib/python3.6/dist-packages/sklearn/base.py:464: DataConversionWarning: Data with input dtype uint8, int64, float64 were all converted to float64 by StandardScaler.\n", + " return self.fit(X, **fit_params).transform(X)\n", + "/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py:451: DataConversionWarning: Data with input dtype uint8, int64, float64 were all converted to float64 by StandardScaler.\n", + " Xt = transform.transform(Xt)\n", + "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n", + " FutureWarning)\n" + ], + "name": "stderr" + }, + { + "output_type": "stream", + "text": [ + "0.8671751927700854\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "xmcrRc8g6P2K", + "colab_type": "code", + "colab": {} + }, + "source": [ + "data['y']=np.where(data['y'] =='yes', 1, data['y'])\n", + "data['y']=np.where(data['y'] =='no', 0, data['y'])" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "IbpVvArK9GD4", + "colab_type": "code", + "colab": {} + }, + "source": [ + "y=data.y" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "6xsnADhj5Lrp", + "colab_type": "code", + "outputId": "d648dfd6-7de7-40b7-aa96-1550d6ef273d", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 800 + } + }, + "source": [ + "from sklearn.ensemble import RandomForestRegressor\n", + "from sklearn.model_selection import cross_val_score\n", + "#rf = RandomForestRegressor()\n", + "my_pipe_rf=make_pipeline(StandardScaler(),MinMaxScaler(), RandomForestRegressor())\n", + "\n", + "scores=(cross_val_score(my_pipe_rf,X,y,cv=5))\n", + "print(scores)\n", + "print(\"Accuracy: %0.2f (+/- %0.2f)\" % (scores.mean(),scores.std()*2)) \n" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "stream", + "text": [ + "/usr/local/lib/python3.6/dist-packages/sklearn/preprocessing/data.py:645: DataConversionWarning: Data with input dtype uint8, int64, float64 were all converted to float64 by StandardScaler.\n", + " return self.partial_fit(X, y)\n", + "/usr/local/lib/python3.6/dist-packages/sklearn/base.py:467: DataConversionWarning: Data with input dtype uint8, int64, float64 were all converted to float64 by StandardScaler.\n", + " return self.fit(X, y, **fit_params).transform(X)\n", + "/usr/local/lib/python3.6/dist-packages/sklearn/ensemble/forest.py:246: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n", + " \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n", + "/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py:511: DataConversionWarning: Data with input dtype uint8, int64, float64 were all converted to float64 by StandardScaler.\n", + " Xt = transform.transform(Xt)\n", + "/usr/local/lib/python3.6/dist-packages/sklearn/preprocessing/data.py:645: DataConversionWarning: Data with input dtype uint8, int64, float64 were all converted to float64 by StandardScaler.\n", + " return self.partial_fit(X, y)\n", + "/usr/local/lib/python3.6/dist-packages/sklearn/base.py:467: DataConversionWarning: Data with input dtype uint8, int64, float64 were all converted to float64 by StandardScaler.\n", + " return self.fit(X, y, **fit_params).transform(X)\n", + "/usr/local/lib/python3.6/dist-packages/sklearn/ensemble/forest.py:246: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n", + " \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n", + "/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py:511: DataConversionWarning: Data with input dtype uint8, int64, float64 were all converted to float64 by StandardScaler.\n", + " Xt = transform.transform(Xt)\n", + "/usr/local/lib/python3.6/dist-packages/sklearn/preprocessing/data.py:645: DataConversionWarning: Data with input dtype uint8, int64, float64 were all converted to float64 by StandardScaler.\n", + " return self.partial_fit(X, y)\n", + "/usr/local/lib/python3.6/dist-packages/sklearn/base.py:467: DataConversionWarning: Data with input dtype uint8, int64, float64 were all converted to float64 by StandardScaler.\n", + " return self.fit(X, y, **fit_params).transform(X)\n", + "/usr/local/lib/python3.6/dist-packages/sklearn/ensemble/forest.py:246: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n", + " \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n", + "/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py:511: DataConversionWarning: Data with input dtype uint8, int64, float64 were all converted to float64 by StandardScaler.\n", + " Xt = transform.transform(Xt)\n", + "/usr/local/lib/python3.6/dist-packages/sklearn/preprocessing/data.py:645: DataConversionWarning: Data with input dtype uint8, int64, float64 were all converted to float64 by StandardScaler.\n", + " return self.partial_fit(X, y)\n", + "/usr/local/lib/python3.6/dist-packages/sklearn/base.py:467: DataConversionWarning: Data with input dtype uint8, int64, float64 were all converted to float64 by StandardScaler.\n", + " return self.fit(X, y, **fit_params).transform(X)\n", + "/usr/local/lib/python3.6/dist-packages/sklearn/ensemble/forest.py:246: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n", + " \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n", + "/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py:511: DataConversionWarning: Data with input dtype uint8, int64, float64 were all converted to float64 by StandardScaler.\n", + " Xt = transform.transform(Xt)\n", + "/usr/local/lib/python3.6/dist-packages/sklearn/preprocessing/data.py:645: DataConversionWarning: Data with input dtype uint8, int64, float64 were all converted to float64 by StandardScaler.\n", + " return self.partial_fit(X, y)\n", + "/usr/local/lib/python3.6/dist-packages/sklearn/base.py:467: DataConversionWarning: Data with input dtype uint8, int64, float64 were all converted to float64 by StandardScaler.\n", + " return self.fit(X, y, **fit_params).transform(X)\n", + "/usr/local/lib/python3.6/dist-packages/sklearn/ensemble/forest.py:246: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n", + " \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n" + ], + "name": "stderr" + }, + { + "output_type": "stream", + "text": [ + "[ 0. 0. -0.9363185 0. 0. ]\n", + "Accuracy: -0.19 (+/- 0.75)\n" + ], + "name": "stdout" + }, + { + "output_type": "stream", + "text": [ + "/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py:511: DataConversionWarning: Data with input dtype uint8, int64, float64 were all converted to float64 by StandardScaler.\n", + " Xt = transform.transform(Xt)\n" + ], + "name": "stderr" + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "aBLH4yu0QJi2", + "colab_type": "code", + "colab": {} + }, + "source": [ + "#Importing librairies\n", + "\n", + "import pandas as pd \n", + "import numpy as np\n", + "\n", + "# Scikit-learn library: For SVM\n", + "from sklearn import preprocessing\n", + "from sklearn.metrics import confusion_matrix\n", + "from sklearn import svm\n", + "\n", + "import itertools\n", + "\n", + "# Matplotlib library to plot the charts\n", + "import matplotlib.pyplot as plt\n", + "import matplotlib.mlab as mlab\n", + "\n", + "# Library for the statistic data vizualisation\n", + "import seaborn\n", + "\n", + "%matplotlib inline\n" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "04UX0KuZQhav", + "colab_type": "code", + "outputId": "bc6827f2-ebab-4364-dac3-caa2b9c1a964", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 740 + } + }, + "source": [ + "df_corr = data.corr() # Calculation of the correlation coefficients in pairs, with the default method:\n", + " # Pearson, Standard Correlation Coefficient\n", + "\n", + "plt.figure(figsize=(15,10))\n", + "seaborn.heatmap(df_corr, cmap=\"YlGnBu\") # Displaying \n", + "seaborn.set(font_scale=2,style='white')\n", + "\n", + "plt.title('correlation')\n", + "plt.show()\n", + "\n" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "display_data", + "data": { + "image/png": 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wt7vf2D5HTkRERESke6ugHs3XCT2YBRsRUi4CfAh4yd3nApjZ/cDOwGo1NCtm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oVJSkMa52kLRD11Nuw7L24P0eH5qZJ2Z2UDKaH5LmelQTQcqwSUqNWZfmdT88Mpaj+EIsaV9G\n02G/10S1Opwrby+ytaRD8DTXoZoQM7tL5nWTKO0b2VY7bdiS/lTRK8zG1gtCfVQBPD1vvaNF3vcv\np4bapqb9xUrznp+7mtm7JX0Qj151U9iebGaPTE6iL5vZm9UvuARem9i9ZnXTD7dPhtiK1t/N/O+f\n2eaBwIfllvnNeBr+uHS0PRuHUYOkVzO6SKppQ7Jj8v433/fDJfX9jiY6CSy1Q5K0X/d/Y5gozKTp\naj9rhKdKPt+GGpEhmBBl71DUZ1F1AkMfAF7KoF7t67gT5lHgqdat7T4Aj1atA35kZj/OzLGhynir\nMIwnRvJKvg9pXFWK7TTzraEySll0fCu3WXMtLMYq1GxrxlZSkpk2TelEiAEtY8LQDJYKv5J0HqOL\n9Zx3+i/S/ZM7z/8to2mT1+OtBb6FXwAfjvdobIrX29v/Il77OVZRMr1uRHU2RURz3N3M1tcTpEV4\nn7przvjMIm/VsTeDRcmWklZbEoRJdA2n5sSe9QhLuie+IGovyHYlvyBr1D3PkvT6MUYmeC3n2bgw\nS7Nw+ruesTUX4r502JyheRiD9jY34s2nz8+Ma9Qf385gAX4VcIh5imqXdt/Ipsal74LZNpjX4d/P\nvnTB38rbTZzPcITnda2/m6jCDsCbcDXSpm65r3ftkyTdz8zeKOlR+OL3+J6xDbn2F339JzdJ8/mv\nZID/jIHjos1Gklbi+39Aei7XxL7h0WY2lAkg6SGdMe3j2xXfGBHjME/33iN54e+M/1ZHesKl/dgD\neIaG29GswuutugvfXBuSg3v2a5VcNKP5vm/BaP1iQ42TYPvOPHfB+93lapI/ZamGrLXtl7cf23S1\nnzXCUyWfb0ONyBAURtkTD7JOv1+5uFaXYoEhvDfnjzXINPiRvJ56iOSU2Qk3AFYC/yDpXBvtu1xt\nvFUaQxMjeRXfh2mcYfNKW4WCfZvi+NZElmuuhRNRhZptzdgJ7zlUzynp3ua1nB8Bfm5jMtNskB7/\nIOu0SpI0IqCXCDGgZUwYmsFS4auZ57In7RTt6vN0dvlaujV0Pd1tPkaZomQTmToSuHN6ahN8MfXm\nzHbXStoTX+isxKOe41p3HAE8CI8aXYgbEDk+jxtUTa3TLngN4Pr6osZTUivm/AAAIABJREFUnhay\nT8KN0rW4IZKr9fo07nE8KO3f3vSLyrR5AvB/x/z/jmb2ELmq7402vobwDQwuxJfj34MX9YydmA7b\n4pwUNe9rPdLmncBzGqND0gPxY71Dd6CZ9fXBzHGemQ01lE8Rse9nxq5PLWrRt5Aprls2s+dKek1a\nTP0JeLr1p4g3fBqPSD44zWFc+4sD8drZQ/Df0+bkBZ6+iKcJft7MrkiOmr7Ua/AU+ReZ2aXy1N3D\n8RrqHVv7lk1PbaPUNzf9/QHgQnn68tnA+ZLWmdkBnZddgBs33fZDa/HapiFsuA3JOjw63dcP8T1p\n+/9Lrjp9P1w5N0exk8BSi6fWfm/EcI1wmzdK2tbMjpMLJ32is5/T1n7WCE9N/Hxb1IgMQXmUHeBM\nSfs2mS2SXgS8moFSdENNmcP/kwul3F6uYP1U/PrSZee2EZMcMbmWUdMYbzXGUE2k9CxJ72H0evzC\ndF+bYls938rIZ8m+1R7f4uNlrmtQei0coWvkUadmW618q7J6zi9Kei6+htpfrtXQ9OTeGl9vdDlX\n0lvM7OR0bnoN7gjKOZfaYkB/xtdxIQa0TAhDM1hKFKV5ydMfH83gQt1EkHLppX31hTmKFCUTh+PR\nk0/ji4Z98NYZOfbDow7vYKCY1icGdBxe9/Zq3HjdLT2Xa4T832Z2TOvx9yQ9qWe7n8OP0wXp/kX4\nibqbfnKTmX1S0v4tj/UZ5B0BbSYZKi+T9B3rr89aT4qMNhfitbgx2ScS002H3Yv+2t7Vkk5gtK42\nJ7Tx63ZkKy1+V+c2qsn1V9NExMCNgsdR0OqGgrplDadR/wm4Gl8cPV7S43uOQ8N38fq3U4EvWqp5\nzpGOVbPw25/B77M77ihcFbrhaDO7Ps31ABsVp9kHbyPzZbxp+Gl4hkItd2r9vYOZvVzSK4FPmNl7\nlZfYv7OZnSNP1S5Z+D4DeFbLoP26pGPNbMTQM7N/knQmntp7A26UZlWpa5wE8rqnNlviNdc5ngS8\nV9KXcCPuFZno/TS1nzXCUzWfb43IEJRH2cEjnafKFYlfgpc95MSBagSGXoA7767FxeX6anuvUKtN\nEZ6RMnLcpzTeJhpD00RKcUdHr2KxClNsp5lv2n5R5LNm30qP75SR5YnXi9bYiUaeVajZ1oxtUVLP\neTyeUbAdcAzD16u+kqJH4w6u5+HXii8BD+uZww54mUtzbV+HO+OnUYIOFpgwNIOlQk2a13Zmdo85\nzKGmDckfzexKecH7GuDYtEA9sRmgQdH7tXhqYPsi2MdGycBrOCljMDSe9YuTUfzNtM1H4172HFtb\nR0lReXXNFZJ2BdbIxY5+Sr9qHBoI8fyfMfsEE3ordrbZFon5VprLBWb2j92xrcjOv+OL9NdaTzos\nvrgEv6hN4ucpInUWviB6FHBdY6x1Fspj668SVRGxxJl4ym47Fbrvu1NSt9xNo26+K3cZs13S6yWv\nHdsb+GdJ/4n3mh1RKpV0fHr//0hP9TqCOu/RFu/ZF/ho2l5jMK3BIw0fwb8X78TPFbXiFe193VTS\nX+IS+09NC7A7ZV5zEB5lzEUEcgupV+HRuIa98IjpekNTnlrWJ1bS7Y05jZOg/T1bh6eaDaU4arhm\n+Gu4U8zw1gN/ba16d5uu9nNiTek0n6/ViQxBeZQdM7tE0t/gxuylZvaanm3WCAytTdt6C6xXzcwd\nw+2An8lTrjfCjVlrDBob9KItNt4qjaFpIqVjFYutMuV6CuOtNPJZvG8Vx3ea41VyvWioEe2pUbOt\nEdybWM9p3kfzHZKea2bZDAuN9jS9EV9fbYx/3v9Nv5JsO41/FZ5ZcyFhaC4LwtAMlgSVaV6fl/Q0\n4BKGveR9fepKqWlDck3yxF2cFtZXMmi10TBN0fuNcuGbc9K4xzKaDnpM53E7itl3sf03STuZ2fcA\nJD2YfBrx8/DIxyvw1Nk98ZSVEeQ1rG9MDyf1pcz1VuyjLRLzJRsjEiOpbXzeBnhCchb8FDeE2t+P\nI7qvb21nfTpl4hfp1tQMNouLnFjQxPorM/sDcI68b9jdzFNFd8UvmH0L5JvNrPS4TaxbtkEa9Qrg\nAZRFStv78O/ymqIL8Gj4m0nGYIf7mNndS7fbQ3teze9nRet+R9wR1Pc7KuUYvKb7BDP7hbwnay7q\n+Kp0P5JG38NGDDupVjJ6rJsawb/HnQnnMFBv7hq71U4Cy7TWSEZim67I1h9bz3fr3RsmOgUl3T1F\nhT6fm1uH6s9XdSJDTZT9nnhk5H1k+sBqVHF6I7y/3/PJK07XCAyVqmYWqY9WGm/FxtCUkdJSxeKx\nKbbTzDdRFPms2bfS4zvl8Sqq103/qxHtqVGznThWU9Rz9hmZiW5P0/OBd5vZYcnB9xo8TXzkmFnq\n892a2+3wTK9gGRCGZrAk0Gia11b0p3k9FDeE/qP13MSISQH7W0dRcgwvwC+YJ+LG5BZ41GI9Nih6\nf5mNtrXo44W4gfdG3Av+PTpCARWL3TZPB14h6Y/4Yva2eKRwaBFlXpfUqO4OLQAyxtiBFPalxBfO\npWI1NSIxd8WNtTPSdvdI294GX6h2G9730V3YH0nGGLO8InJp/RX4gvMoec3su/BF7yfxtjrA0G/h\nK/JU6POY0O7B6uqW/xmPMLXVldcxxjucnCpPBh6IR9CPp/P9aDELR9D6BU7OYJoVySnS/r4elouM\nZIyQhr62Nx8ALkuG+Ub4934oIm+DlioPNLODWv+6QF6r2R5b7SSQtCMewWsbQv+TVrsiazWEz31/\nchQ6BV+Jp/8fw8B4bBiKAE/5+daIDJEyP5o+sJuQ6QNr9YrTNQJDpaqZv8fPq3czV/XeHTeK++r4\nJhpvNcZQTaS0xUTF4sTYFNva+dZGPqfct7HHd8ptTrxeTGPkUSeiVzK2up5zAt3zym6WSmKSQ/go\nSSeOvizLWkZrpoMlShiawVKhrfTYqHH2eRK3NbM+dbIN4a4qbFOB1ySeiS+wjjezrEcycaCk88Ys\nFtr8Eviomf0dgDI9NxuDr2/xm1n0YmY51cRausZYjRBPsVgNdSIx2wGPaowDeV3Vl8zsyerpW9hD\n9ziehS9gftMZkzPG2vVX/5Dmun/P+2xqXut3BPBeMztBUjdq3o7w5OaZi/AU1y0zXUP6B+Opht8p\nSFGbiyMoHaeXM7roq41o9tX7YmbrJL2pG/2uNULM7LOSvogL1NwMXN5zHgG4jVzh9Tv4Amon+vso\n1jgJPoArXDa1hk/Fo9EjyNNQ/5pBmnbv9yfjFByp/TSzV6f73SXdxlqqvrgDIjeHms+3RmQIPHK4\nsxX0gc1FS9M8utHSGoGhrmrmY8mrZn4Kr0VrHE93xSOfuRp9KDDeaoyhykhp85oXpGjxg/Dv+ki0\nODE2xXaK+VZFPqfZNyYc3ym32a3XzV0vpjHyatRsJ4616eo5x9G9bvylpJMZ1Kq+Cv9NjjgkO2ud\nFfh58iNTzCFYBMLQDBYVSVcyuqhegRs1h5JPjzg1GWDfY0Kkp5I98cXYFmlOa/ATWm6hcz/cw/hM\n4Gh524fP9yx+iusTKei52YoqPtEyAgI5JO2FX8wmLZ7G0b1Q1AjxTBSrac2pKxLzPvPU0xxb4hGe\nS9PjewP3ksukj2uVMYmNzSzXFD7HSvyzf4uk3fAF122B/8yMvY2k5+Dfmx0l3YNOzeiUEZ6auuWa\nhvSNKM/N+GJ97yblK801pzQ6C0dQzsg+GP99ToxiSXoQ/rsZiUSY2T4TXv6lzPa6EYYhetLI/pNW\nerpGa5Qa/hY3zA9Pc70cX9jlqHES/JeZfVPSDWZ2EZ7i+DXcWO3yEDydrqT2cmLtZ4Myqr7pNV1V\nX6j4fKkTGYK6PrCl0dIagaG2amYjCJdTzbyDmX1YLiaFuSLnuPr3icbblMZQaZorkg5mEC3elEy0\nOFGUYls635pI7bT7RqFxXLnN0/DfwtuAC6xV3tF6XbWRZxVqtjVjqav9rKFbq3omPbWqU2QbBEuI\nMDSDxWZ7/MR8KO7pPodBnVJf24i/B7oX3w2t1QI/8b8Fr7dcgRsq2d6YZvYn4HTgdLmK6BtwT3ZO\nWr+mPrGm5+a75D0Ax7VKaXgnHtX4j0kDS7E6IZ4SsRpgyPlA67m1ZpZbxL0K+ISkpibwV7iXWAza\n1EzDpyS9BvegtxdFucjRyXjaz8b4cR5Jh23xUtyj/RIz+4M8dfmNmXHIa2aebS3lUiCrXEpdumrT\nkP761thc+ifA6nQ/0luS/oX6WEeQBmJWWczsR3gadpcrzKzUk/45CtL00nweyOSU0VmkkeVEhpp0\n9UMKt1HsJMANoL2AKyW9Da9b7nMAFLfqyDlCNFr72VCq6gt1n+9EkaEOJ+EGb6O6/XD6+8CWRktr\nBIauo+daouGShJXy9jJNhsZf4ZkVfZTWR0KdMVSU5pp4CvCwgmhxaYpt0XynTFuFun0rPb4123w2\n3s/5acCbJf0XcK6Z5dqDFRt5qlOzLR5LXe3nOLrn1+Ja1XSdXIWXOZyO/9aOM7OIai4DwtAMFhVL\njbElPdLMDm3968S+BUnO4JC0/wymcxC+MFqTttk0G/5cd2AysPYCnoinsX2J/obsNfWJNT03/wv4\n/yT9gOFIaS4acgme9vinnm1Vo7wQzxPMLLdvE8VqWs91xUYejRuOI5jZN+j02ZP0RkvqjhV00yn3\nwxd4ba94X4piSTpsM99L8Pq15vF6YSeN1sC+mgnKpS2K01XN7D65ufVwN/UrZPYxyRHUFbPqjnus\nJdGqDr9NmQPnM7zoyxmlpWl6zaJlUirqnczsyxpWf20zMU3bKlotZb4LDTVOgmfjNZkvI53b6O89\nV9yqQwW1ny02VZmqLxR8vqoTGSIZCc1390r897QON0T6MgeKoqVWIDBUSPt4vBwX2NpR0q/x8/a4\nPsY1xluNMVQayYPCaLGVp9gWzXfKSC3U7Vvp8S3eppn9OjkNr8d7wT4MX0fkDM0aI69GzbZmbE3t\nZ1uJfnXnX90WXjXaBi/B1wH7Av9uZgdLOotIn10WhKEZLBVukPRuhuuUsp7cMQudT23gHK7BG383\nrMGjADleDXwBeGvyVo+jpj5xPwY9N5vGxH3Kt++c8L5tvob3kbyC4cVTTeps1xhr91JchXtpryFD\nulj2XYC6Y//Yeep0ef3GyGIiRUSPBO6cntoEX5iMGJopnfKNDAz+HwNvNrMfZtIpV5pZ7qKbY2I6\nbCHdBXiJcmlDcbqqetpq9HwXHpDu74WnBTY1Zo/EI9kjwk89kefmvQ+wMWJW8nrcPs5NtzZ9BnBp\nJGILMyvpxdl8NrkUrvVz0Ggt0dC4nEEob5HUVSnOGZlFTgJJ3zCzxwMnmdme6ek+x1ZDX0QyR3Ht\nJ4WqvomSz7dYZCjRjsT/EI+GTKIoWqoCgaFC2sJXPwIeX/rCSuOtxsCqiZQWRYsrUmxr51sTqYWK\nfas4vsXblKvI/geuRfDPwNvGZCXVGHnFEcLKscW1nxqjRG+tnqaJGm2Dm83sz5KeDjT187nssWAJ\nEoZmsFTYB/d674ZfLIxhL2KbmoVODdcDl8hFZFbiF8zVkt4Bw551M3ta98Utb3uXmvrEn+MtRkrY\njfxCOxddORQ/vr8at8EaY6wdjUu8L0WIctvdl0HqWHMB+p6ZfTYzth2FAPcq99VbHo5HRT+Nfw/2\nAfrqOT+Z9q2pf3048Fm8Pq3Lv0h6URrbXjj8KDO2OB12At3PcqJyaYuauuW2MuYq3IudNYwtKYzK\ne4o+tFkQyVVzT5m0Qxna/TH7nARvHvP60shqaSTizJJU1CYaaZNb5PTWEsnr+HJcIa9h/JyZ9Yle\nNdsocRL8l6TfAX/RSilc3z6kJ/p5eG675JWFi2s/raPqa2aTfhdjP1+rFBmqiSK36CtV6FIsMDQJ\n1asaN6+rMd5qjMeaSOkFuIH/JPy3/EXcUdydQ2mKbe18ayK1ULFvFce35ni9HXc2Pwl32P2bpO/0\nZHHUCPzURAiLx1pdPWexEr2Z/aek0/DvwEr8eD2EfNbQ91O2hZn3uX05GdGgYGkShmawJDAXeym9\nQNeIXNTwtXRryJ34x9E0de9SXJ+YQ9KHzewlmX9d2/p7bEQRTxU7Z4zntKHYGNNord2W9NfVvoz8\nBWjE0GRUgfg7DPega/NHM7syRYXWAMfKU65zMunX2nCbmdOSMZmjibq162tzEZPadNhirE65tLhu\nOWNUXSLpTDyS3sc2uDHaRLFvS3/q4TjaEajDKXcSQEH/xoZJkYjWwn4FcFhhKuok1kek03u/lOGs\ni13J91j838DjgBekhfU5eATw8szYiU4CM9srzeFdZpbtgZuhHWVsttvXr6+49rNjQG2Cp9Bf2ROZ\nLf58VScyVEtpc/gagaGxjHNQTKDGeCs2hiojpcfjxtPHJsy19njNPG0Vqvet6PjWbNPMTgBOkHRb\n/Hf/StxpPhKhqzTyStRsq8eqrp6zWIk+OS83wx0EzXUhW55iZq+Qq4E3WVWnkdJm1S+yFiwRwtAM\nliM1IhfFTOn9br8+Z2RCXX1ijsN73q84ooj/1k1ez9n2DnfrOWuMsfb7Ny1p+o5BTSuUpjfpg/GL\n9oUMGsl3uUbe4/FiScfjdVhDRkJj0OM1aB/CIxbrcIP/ytxGm/ROSavM7KYxc51EXz3aRCRtjUcw\n72xmT5f0TEnn56Lm49JVM9vt1hluyWBR18c7cK/y9fixuyM938sJtBeWNU6C9dHVBuX7Nzb/GxuJ\n2ICF/Tja+/Zp3GlzEB613ZueWjvzBuxflddtPT6Nf5ZcFOtVbcdApZPg9fJejX9pLcXK3PfZRvv8\nfikZcTmKaz+7x1kuvPTcnrHFny91IkNVWHlz+GKBIUkbd518ku5sZr+jVZLQ+s1vZmZ/m1IRs7/5\nRLHxVmMMVUZKfwx80iYrFtcIMs0lbXWKfSs6vjXbTNegB+PXwHPxEpin9My1xsibqGY75diaes4a\nJfqqNlstI5PO7+GV5FsKBUuEMDSD5ciB+MLtZbhh8yEq6lpmgaQ9cC9us0C/CjjEzM7pjrVWU/SC\n7W6D1zH+m6Tn4kI3HyajFpuJKG5Ff0Qxq4bY2tbUxlgh3QvQk/F+cTmOwxdf5zCIBO2OR+267Id7\nRRvjdHPyBn2bdk+67OJI3qbkaHzRcF9JbwW+ZWZf75lzHzURjm4N7MfTHBr13N/gdcgjx71yQdJe\n/DdtfPbMjFuPmR2PNwvfHF98rbFB79KmBUotE50EbVTQv7FFUSRC0icyr70Zd159ZEIEYRw3mdkn\nJe1vA9GSM4CvdgdK2h2v730kvoh7iZl9X65mfQItsatKJ8GxlCtWdvs0bkknGq7paj+HMBfQyS4u\nKz/fTVUuMrShDDWHV4XAUJrXpsAZcgXZJnKzCj+/PdCGSxKKf/OJGmO3xsCqiZSeiP+GL2XY0Oum\nXZem2NbOt1bNtmbfSo9vzTa/ALzGzP67+w+N9vCtMfJq1GxrxhbXc1qdEn2NgvY4xqmFB0uAMDSD\n5chxwMfM7HrgCEkX4fV/eyzgHN4JPMfMLoP1nvrP4t79iag/HfZ44JWSdsHrow7D60+emBnbCGJs\ngqe5XUd/RPE83OAaim60/l9sjGm6eqLDGAjI3AgcPOYCtLWZtetUT5J0ds/YLfGLZSMEsQJPAVq/\nAG4b+hUpU0fii6ImonI07jWtNTSHUJ0g0UZm9lW56AhmdrakN/VsunhBYpPrDHtTfVPkscv6ussC\n2ouC/fAFZ+Mk2IJRJ0GbH9KqNcS/7+8e8z4lkZ5r8fq+09L/n8RAEOwEhn8HNayQtCuwRtKLccO1\nL9X4APzc8dJmoQpgZldI6qYj1jgJahQr28832QndtkzVtZ8a7T+6Ff3ZCTWfb43IUBXKN4dvGww1\nAkNPwsWLdsbVxhvW4oZml5rfPNQZbzXGUE2a61twp+vY+n/KU2yr5luZCgt1+1Z6fGsiy31lIOBO\n1TY1Rl6xmm3NWCrqOeVCeA/BHQOTlOhrFLTHMVW6erBwhKEZLEdua2brhUjM7J8lldYizYpfN0Zm\nmsOlklZXvP7wnuf/bF7s/k7gfWZ2XrrA5jgSl9W/jZkpRd1u7hk7th9XjTE2ZdrhOWa2K6Oqkjk2\nkbSVmf0yzWdrPAKQ43S8rrakX2KNR/8mM1ujlOprZr/pu8BXUiNIdJOkxwIbSbobfmEe8YInalQE\nxzFNVGgFTNUf82QbNFr/TNpGb6N1y/RvbMhEVU/GU+rOx+uidyEfiXiomT2u9fgESV81sydJetK4\n/cnQjkg/D3eCvAL/ne4JvKbndRt3U1clXWBmu3QjxZVOgmLFynFZF41TzKar/Wz3H20M2B/0zKH4\n87V6kaFixp3f5PVgxSUW5kqbp0t6bsoKmETNbx7qjLcaA6smzfVHZvbxgvcvTbGtmm/leR3q9q30\n+FalBY+hG52rMfKK1WxrxlJX+3kG7vCZ2K/b6tpsBcuYMDSD5chVkt7FoNXCY/HU1YXk5/Ji9rPS\nHB4FXNektlmr55oq0mGBjSW9AU8NPkzSTgxqPLscQT7qlms2XhTdqLloy9OH3wZsjS8ArgL+IZc+\njKv3noAbWOtFRqzTmy7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M7BS5yE+zJrhiivIYcCdd29B8M5E6u2wJQzMIpuMmM1vT1F2Z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\nCBaWSJ0NgiAIgtlyMvAHYDfgNGB34PDcQEm/ZSAUtAq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" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "aEcHx9CORXTp", + "colab_type": "code", + "outputId": "4a776000-d54d-4e34-b140-d2ae5a75ae33", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 481 + } + }, + "source": [ + "classifier = svm.SVC(kernel='linear') # We set a SVM classifier, the default SVM Classifier (Kernel = Radial Basis Function)\n", + "#my_pipe_svm=make_pipeline(StandardScaler(),MinMaxScaler())\n", + "\n", + "classifier.fit(X_train, y_train) # Then we train our model, with our balanced data train." + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "error", + "ename": "KeyboardInterrupt", + "evalue": "ignored", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0;31m#my_pipe_svm=make_pipeline(StandardScaler(),MinMaxScaler())\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0mclassifier\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# Then we train our model, with our balanced data train.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/sklearn/svm/base.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y, sample_weight)\u001b[0m\n\u001b[1;32m 210\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 211\u001b[0m \u001b[0mseed\u001b[0m \u001b[0;34m=\u001b[0m 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max_iter=self.max_iter, random_seed=random_seed)\n\u001b[0m\u001b[1;32m 272\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 273\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_warn_from_fit_status\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mKeyboardInterrupt\u001b[0m: " + ] + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "8rC3fNyAUcRJ", + "colab_type": "code", + "colab": {} + }, + "source": [ + "class_names=np.array(['1','0']) # Binary label, Class = 1 (fraud) and Class = 0 (no fraud)\n", + "\n", + "# Function to plot the confusion Matrix\n", + "def plot_confusion_matrix(cm, classes,\n", + " title='Confusion matrix',\n", + " cmap=plt.cm.Blues):\n", + " \n", + " plt.imshow(cm, interpolation='nearest', cmap=cmap)\n", + " plt.title(title)\n", + " plt.colorbar()\n", + " tick_marks = np.arange(len(classes))\n", + " plt.xticks(tick_marks, classes, rotation=45)\n", + " plt.yticks(tick_marks, classes)\n", + "\n", + " fmt = 'd' \n", + " thresh = cm.max() / 2.\n", + " for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n", + " plt.text(j, i, format(cm[i, j], fmt),\n", + " horizontalalignment=\"center\",\n", + " color=\"white\" if cm[i, j] > thresh else \"black\")\n", + "\n", + " plt.tight_layout()\n", + " plt.ylabel('True label')\n", + " plt.xlabel('Predicted label')\n", + "\n" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "-_TEuAV_UNpq", + "colab_type": "code", + "colab": {} + }, + "source": [ + "prediction_SVM_all = classifier.predict(X_test) #And finally, we predict our data test.\n", + "\n", + "cm = confusion_matrix(y_test, prediction_SVM_all)\n", + "plot_confusion_matrix(cm,class_names)\n", + "\n" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "weElqDFdVhlO", + "colab_type": "code", + "colab": {} + }, + "source": [ + "print(\"the accuracy is : \"+str((cm[0][0]+cm[1][1]) / (sum(cm[0]) + sum(cm[1]))))" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "MTwA0gYCVkPf", + "colab_type": "code", + "colab": {} + }, + "source": [ + "print(\"the sensitivity or recall is : \"+str((cm[0][0]) / (cm[0][1]+cm[0][0])))" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "6X7YRt2cXHSO", + "colab_type": "code", + "colab": {} + }, + "source": [ + "print(\"the specificity is : \"+str((cm[1][1]) / (cm[1][0]+cm[1][1])))" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "8XUZGfuoXVIS", + "colab_type": "code", + "colab": {} + }, + "source": [ + "print(\"the precision is : \"+str((cm[0][0]) / (cm[0][0]+cm[1][0])))" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "xOEQ-u56YjD2", + "colab_type": "code", + "colab": {} + }, + "source": [ + "#PCA\n", + "\n", + "from sklearn.preprocessing import StandardScaler\n", + "\n", + "scaler = StandardScaler()\n", + "\n", + "scaler.fit(data)\n", + "\n", + "scaled_data = scaler.transform(data)\n", + "\n", + "\n" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "uiJ1lWoeZ5YX", + "colab_type": "code", + "colab": {} + }, + "source": [ + "\n", + "from sklearn.decomposition import PCA\n", + "\n", + "pca = PCA(n_components=2)\n", + "\n", + "pca.fit(scaled_data)\n", + "\n", + "\n" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "5MDQzuOOZxR4", + "colab_type": "code", + "colab": {} + }, + "source": [ + "x_pca = pca.transform(scaled_data)\n", + "\n", + "scaled_data.shape\n", + "\n" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "YJ3bWHQDZ2uV", + "colab_type": "code", + "colab": {} + }, + "source": [ + "x_pca.shape" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "aEhTdJ9IZ9EC", + "colab_type": "code", + "colab": {} + }, + "source": [ + "plt.figure(figsize=(10,6))\n", + "plt.scatter(x_pca[:,0], x_pca[:,1], c=data['y'], cmap='plasma')\n", + "plt.xlabel = \"First Principal Component\"\n", + "plt.ylabel = \"Second Principal component\"\n", + "plt.show()\n", + "\n" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "VV_R5U4OZ-er", + "colab_type": "code", + "colab": {} + }, + "source": [ + "pca.components_" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "QBjV6I7baBlA", + "colab_type": "code", + "colab": {} + }, + "source": [ + "correlation = data.corr()\n", + "correlation" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "tP_YAhC4bhhZ", + "colab_type": "code", + "colab": {} + }, + "source": [ + "eig_vals, eig_vecs = np.linalg.eig(correlation)\n", + "\n", + "print('Eigenvectors \\n%s' %eig_vecs)\n", + "print('\\nEigenvalues \\n%s' %eig_vals)" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "pqT50sqkf6sc", + "colab_type": "code", + "colab": {} + }, + "source": [ + "# Make a list of (eigenvalue, eigenvector) tuples\n", + "eig_pairs = [(np.abs(eig_vals[i]), eig_vecs[:,i]) for i in range(len(eig_vals))]\n", + "\n", + "# Sort the (eigenvalue, eigenvector) tuples from high to low\n", + "eig_pairs.sort(key=lambda x: x[0], reverse=True)\n", + "\n" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "-Dm-aBmxgAih", + "colab_type": "code", + "colab": {} + }, + "source": [ + "tot = sum(eig_vals)\n", + "var_exp = [(i / tot)*100 for i in sorted(eig_vals, reverse=True)]\n", + "# Visually confirm that the list is correctly sorted by decreasing eigenvalues\n", + "print('Eigenvalues in descending order:')\n", + "for i in eig_pairs:\n", + " print(i[0])\n", + " \n" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "an2SELkDNpnz", + "colab_type": "code", + "colab": {} + }, + "source": [ + "var_exp" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "R6K7wreWilXA", + "colab_type": "code", + "colab": {} + }, + "source": [ + "count=0\n", + "sum=0\n", + "for i in var_exp:\n", + " if(sum<=80):\n", + " count=count+1\n", + " sum=sum+i\n", + "print (\"number of dimensions required to capture 80% of variance is \"+str(count)) " + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "xMIwxO69gXwU", + "colab_type": "code", + "colab": {} + }, + "source": [ + "with plt.style.context('dark_background'):\n", + " plt.figure(figsize=(20, 10))\n", + "\n", + " plt.bar(range(61), var_exp, alpha=0.5, align='center',\n", + " label='individual explained variance')\n", + " # plt.ylabel('Explained variance ratio')\n", + " # plt.xlabel('Principal components')\n", + " plt.legend(loc='best')\n", + " plt.tight_layout()\n" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "xEB8gcQ2UiTY", + "colab_type": "code", + "colab": {} + }, + "source": [ + "from sklearn.preprocessing import StandardScaler\n", + "X_std = StandardScaler().fit_transform(X)\n" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "5bAsO_OyUQjn", + "colab_type": "code", + "colab": {} + }, + "source": [ + "from sklearn.decomposition import PCA\n", + "pca = PCA().fit(X_std)\n", + "plt.plot(np.cumsum(pca.explained_variance_ratio_))\n", + "plt.xlim(0,61,1)\n" + ], + "execution_count": 0, + "outputs": [] + } + ] +} \ No newline at end of file From dc3acbea70470e587af3c5215a7db6722e166521 Mon Sep 17 00:00:00 2001 From: NILEENA Date: Fri, 25 Oct 2019 21:01:27 +0530 Subject: [PATCH 3/4] commit2 --- pr.ipynb | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/pr.ipynb b/pr.ipynb index bcdd62e..423ccdf 100644 --- a/pr.ipynb +++ b/pr.ipynb @@ -32,7 +32,7 @@ "colab": {} }, "source": [ - "#just a file to try all codes!!!!!!!!!!!!!!" + "#just a file to try all codes i.e try machine learning models and analyse data!!!!!!!!!!!!!!" ], "execution_count": 0, "outputs": [] @@ -3697,4 +3697,4 @@ "outputs": [] } ] -} \ No newline at end of file +} From 9d8cb4d3c8fd15ea2d7a48dbd9d256cb00b2d1ed Mon Sep 17 00:00:00 2001 From: NILEENA Date: Fri, 25 Oct 2019 21:26:32 +0530 Subject: [PATCH 4/4] commit3 --- ABSTRACT_DBMS.docx | Bin 13632 -> 10014 bytes 1 file changed, 0 insertions(+), 0 deletions(-) diff --git a/ABSTRACT_DBMS.docx b/ABSTRACT_DBMS.docx index 1900ec1045576b47339866d8eef6f1b14dd3e35a..2631eb5ad782ba592d0e12c946ced33ba0471e66 100644 GIT binary patch literal 10014 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