diff --git a/intern-basics/Job Recommendation System/Data.md b/intern-basics/Job Recommendation System/Data.md new file mode 100644 index 0000000..73b612c --- /dev/null +++ b/intern-basics/Job Recommendation System/Data.md @@ -0,0 +1,33 @@ +# Data + +For the Job Recommendation System model we consider the data from the [Kaggle](https://www.kaggle.com/c/job-recommendation). + +The dataset consist of many files in different formats, but we only consider the following files +* apps.tsv +* jobs.tsv +* test_users.tsv +* user_history.tsv +* users.tsv + +In order to understand the content of the data files, you need to understand the structure of this contest. + +In outline, we give you data on users, job postings, and job applications that users have made to job postings. In total, the applications span 13 weeks. +We have split the applications into 7 groups, each group representing a 13-day window. Each 13-day window is split into two parts: +The first 9 days are the training period, and the last 4 days are the test period. All the other details on window split per user you can find in the +[Kaggle](https://www.kaggle.com/c/job-recommendation/data) site. + +## File Formats +Each of the files is in .tsv (tab-seperated value) format. This means that each line in a .tsv file consists of several fields, which are separated by tabs. +To accommodate this file format, fields composed of text have been changed in the following ways to escape tabs, newlines, and carriage returns. + +1. Tabs have been replaced by '\t' +2. Newlines have been replaced by '\n' +3. Carriage returns have been replaced by '\r' +4. Backslashes have been replaced by '\\\' + +## Files + +**users.tsv** contains information about the users. Each row of this file describes a user. The *UserID* column contains a user's unique id number, +the *WindowID* column contains which of the 7 windows the user is assigned to, and the *Split* column tells whether the user is in the Train group or +the Test group. The remaining columns contain *demographic and professional information* about the users. + diff --git a/intern-basics/Job Recommendation System/Job_recommendation_system.ipynb b/intern-basics/Job Recommendation System/Job_recommendation_system.ipynb new file mode 100644 index 0000000..fbe88a8 --- /dev/null +++ b/intern-basics/Job Recommendation System/Job_recommendation_system.ipynb @@ -0,0 +1,4506 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Job recommendation engine" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "* Recommend similar jobs based on the jobs title, description\n", + "* Recommend jobs based on similar user profiles" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Import dependencies" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "import pandas as pd\n", + "import numpy as np\n", + "import ast \n", + "from scipy import stats\n", + "from ast import literal_eval\n", + "from sklearn.feature_extraction.text import TfidfVectorizer, CountVectorizer\n", + "from sklearn.metrics.pairwise import linear_kernel, cosine_similarity\n", + "\n", + "import warnings; warnings.simplefilter('ignore')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Load dataset" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Invalid switch - \"input_data\".\n" + ] + } + ], + "source": [ + "!dir ./input_data/*.tsv" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "b'Skipping line 122433: expected 11 fields, saw 12\\n'\n", + "b'Skipping line 602576: expected 11 fields, saw 12\\n'\n", + "b'Skipping line 990950: expected 11 fields, saw 12\\n'\n" + ] + } + ], + "source": [ + "apps = pd.read_csv('./input_data/apps.tsv', delimiter='\\t',encoding='utf-8')\n", + "user_history = pd.read_csv('./input_data/user_history.tsv', delimiter='\\t',encoding='utf-8')\n", + "jobs = pd.read_csv('./input_data/jobs.tsv', delimiter='\\t',encoding='utf-8', error_bad_lines=False)\n", + "users = pd.read_csv('./input_data/users.tsv' ,delimiter='\\t',encoding='utf-8')\n", + "test_users = pd.read_csv('./input_data/test_users.tsv', delimiter='\\t',encoding='utf-8')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Filtering Window 1 data for all datasets" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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UserIDWindowIDSplitApplicationDateJobID
0471Train2012-04-04 15:56:23.537169528
1471Train2012-04-06 01:03:00.003284009
2471Train2012-04-05 02:40:27.7532121
3471Train2012-04-05 02:37:02.673848187
4471Train2012-04-05 22:44:06.653733748
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" + ], + "text/plain": [ + " UserID WindowID Split ApplicationDate JobID\n", + "0 47 1 Train 2012-04-04 15:56:23.537 169528\n", + "1 47 1 Train 2012-04-06 01:03:00.003 284009\n", + "2 47 1 Train 2012-04-05 02:40:27.753 2121\n", + "3 47 1 Train 2012-04-05 02:37:02.673 848187\n", + "4 47 1 Train 2012-04-05 22:44:06.653 733748" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "apps = apps[apps['WindowID']==1]\n", + "apps.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Index(['UserID', 'WindowID', 'Split', 'ApplicationDate', 'JobID'], dtype='object')" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "apps.columns" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(353582, 5)" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "apps.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Int64Index: 353582 entries, 0 to 353581\n", + "Data columns (total 5 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 UserID 353582 non-null int64 \n", + " 1 WindowID 353582 non-null int64 \n", + " 2 Split 353582 non-null object\n", + " 3 ApplicationDate 353582 non-null object\n", + " 4 JobID 353582 non-null int64 \n", + "dtypes: int64(3), object(2)\n", + "memory usage: 16.2+ MB\n" + ] + } + ], + "source": [ + "# Information on apps file\n", + "apps.info()" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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UserIDWindowIDSplitSequenceJobTitle
0471Train1National Space Communication Programs-Special ...
1471Train2Detention Officer
2471Train3Passenger Screener, TSA
3721Train1Lecturer, Department of Anthropology
4721Train2Student Assistant
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" + ], + "text/plain": [ + " UserID WindowID Split Sequence \\\n", + "0 47 1 Train 1 \n", + "1 47 1 Train 2 \n", + "2 47 1 Train 3 \n", + "3 72 1 Train 1 \n", + "4 72 1 Train 2 \n", + "\n", + " JobTitle \n", + "0 National Space Communication Programs-Special ... \n", + "1 Detention Officer \n", + "2 Passenger Screener, TSA \n", + "3 Lecturer, Department of Anthropology \n", + "4 Student Assistant " + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "user_history = user_history[user_history['WindowID']==1]\n", + "user_history.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "# replace all the NaN with space\n", + "user_history['JobTitle'] = user_history['JobTitle'].fillna('')" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "# users[users['UserID']==1472089]" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Index(['UserID', 'WindowID', 'Split', 'Sequence', 'JobTitle'], dtype='object')" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "user_history.columns" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(348269, 5)" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "user_history.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Int64Index: 348269 entries, 0 to 348268\n", + "Data columns (total 5 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 UserID 348269 non-null int64 \n", + " 1 WindowID 348269 non-null int64 \n", + " 2 Split 348269 non-null object\n", + " 3 Sequence 348269 non-null int64 \n", + " 4 JobTitle 348269 non-null object\n", + "dtypes: int64(3), object(2)\n", + "memory usage: 15.9+ MB\n" + ] + } + ], + "source": [ + "# user_history information\n", + "user_history.info()" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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JobIDWindowIDTitleDescriptionRequirementsCityStateCountryZip5StartDateEndDate
011Security Engineer/Technical Lead<p>Security Clearance Required:&nbsp; Top Secr...<p>SKILL SET</p>\\r<p>&nbsp;</p>\\r<p>Network Se...WashingtonDCUS205312012-03-07 13:17:01.6432012-04-06 23:59:59
141SAP Business Analyst / WM<strong>NO Corp. to Corp resumes&nbsp;are bein...<p><b>WHAT YOU NEED: </b></p>\\r<p>Four year co...CharlotteNCUS282172012-03-21 02:03:44.1372012-04-20 23:59:59
271P/T HUMAN RESOURCES ASSISTANT<b> <b> P/T HUMAN RESOURCES ASSISTANT</b> <...Please refer to the Job Description to view th...Winter ParkFLUS327922012-03-02 16:36:55.4472012-04-01 23:59:59
381Route Delivery DriversCITY BEVERAGES Come to work for the best in th...Please refer to the Job Description to view th...OrlandoFLUSNaN2012-03-03 09:01:10.0772012-04-02 23:59:59
491HousekeepingI make sure every part of their day is magica...Please refer to the Job Description to view th...OrlandoFLUSNaN2012-03-03 09:01:11.882012-04-02 23:59:59
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" + ], + "text/plain": [ + " JobID WindowID Title \\\n", + "0 1 1 Security Engineer/Technical Lead \n", + "1 4 1 SAP Business Analyst / WM \n", + "2 7 1 P/T HUMAN RESOURCES ASSISTANT \n", + "3 8 1 Route Delivery Drivers \n", + "4 9 1 Housekeeping \n", + "\n", + " Description \\\n", + "0

Security Clearance Required:  Top Secr... \n", + "1 NO Corp. to Corp resumes are bein... \n", + "2 P/T HUMAN RESOURCES ASSISTANT <... \n", + "3 CITY BEVERAGES Come to work for the best in th... \n", + "4 I make sure every part of their day is magica... \n", + "\n", + " Requirements City State \\\n", + "0

SKILL SET

\\r

 

\\r

Network Se... Washington DC \n", + "1

WHAT YOU NEED:

\\r

Four year co... Charlotte NC \n", + "2 Please refer to the Job Description to view th... Winter Park FL \n", + "3 Please refer to the Job Description to view th... Orlando FL \n", + "4 Please refer to the Job Description to view th... Orlando FL \n", + "\n", + " Country Zip5 StartDate EndDate \n", + "0 US 20531 2012-03-07 13:17:01.643 2012-04-06 23:59:59 \n", + "1 US 28217 2012-03-21 02:03:44.137 2012-04-20 23:59:59 \n", + "2 US 32792 2012-03-02 16:36:55.447 2012-04-01 23:59:59 \n", + "3 US NaN 2012-03-03 09:01:10.077 2012-04-02 23:59:59 \n", + "4 US NaN 2012-03-03 09:01:11.88 2012-04-02 23:59:59 " + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "jobs = jobs[jobs['WindowID']==1]\n", + "jobs.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Index(['JobID', 'WindowID', 'Title', 'Description', 'Requirements', 'City',\n", + " 'State', 'Country', 'Zip5', 'StartDate', 'EndDate'],\n", + " dtype='object')" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "jobs.columns" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(285091, 11)" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "jobs.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Int64Index: 285091 entries, 0 to 285090\n", + "Data columns (total 11 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 JobID 285091 non-null int64 \n", + " 1 WindowID 285091 non-null int64 \n", + " 2 Title 285091 non-null object\n", + " 3 Description 285090 non-null object\n", + " 4 Requirements 261659 non-null object\n", + " 5 City 285091 non-null object\n", + " 6 State 285091 non-null object\n", + " 7 Country 285088 non-null object\n", + " 8 Zip5 182469 non-null object\n", + " 9 StartDate 285091 non-null object\n", + " 10 EndDate 285087 non-null object\n", + "dtypes: int64(2), object(9)\n", + "memory usage: 26.1+ MB\n" + ] + } + ], + "source": [ + "# jobs information\n", + "jobs.info()" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "

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UserIDWindowIDSplitCityStateCountryZipCodeDegreeTypeMajorGraduationDateWorkHistoryCountTotalYearsExperienceCurrentlyEmployedManagedOthersManagedHowMany
0471TrainParamountCAUS90723High SchoolNaN1999-06-01 00:00:00310.0YesNo0
1721TrainLa MesaCAUS91941Master'sAnthropology2011-01-01 00:00:00108.0YesNo0
2801TrainWilliamstownNJUS08094High SchoolNot Applicable1985-06-01 00:00:00511.0YesYes5
3981TrainAstoriaNYUS11105Master'sJournalism2007-05-01 00:00:0033.0YesNo0
41231TrainBaton RougeLAUS70808Bachelor'sAgricultural Business2011-05-01 00:00:0019.0YesNo0
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" + ], + "text/plain": [ + " UserID WindowID Split City State Country ZipCode DegreeType \\\n", + "0 47 1 Train Paramount CA US 90723 High School \n", + "1 72 1 Train La Mesa CA US 91941 Master's \n", + "2 80 1 Train Williamstown NJ US 08094 High School \n", + "3 98 1 Train Astoria NY US 11105 Master's \n", + "4 123 1 Train Baton Rouge LA US 70808 Bachelor's \n", + "\n", + " Major GraduationDate WorkHistoryCount \\\n", + "0 NaN 1999-06-01 00:00:00 3 \n", + "1 Anthropology 2011-01-01 00:00:00 10 \n", + "2 Not Applicable 1985-06-01 00:00:00 5 \n", + "3 Journalism 2007-05-01 00:00:00 3 \n", + "4 Agricultural Business 2011-05-01 00:00:00 1 \n", + "\n", + " TotalYearsExperience CurrentlyEmployed ManagedOthers ManagedHowMany \n", + "0 10.0 Yes No 0 \n", + "1 8.0 Yes No 0 \n", + "2 11.0 Yes Yes 5 \n", + "3 3.0 Yes No 0 \n", + "4 9.0 Yes No 0 " + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "users = users[users['WindowID']==1]\n", + "users.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Index(['UserID', 'WindowID', 'Split', 'City', 'State', 'Country', 'ZipCode',\n", + " 'DegreeType', 'Major', 'GraduationDate', 'WorkHistoryCount',\n", + " 'TotalYearsExperience', 'CurrentlyEmployed', 'ManagedOthers',\n", + " 'ManagedHowMany'],\n", + " dtype='object')" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "users.columns" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(77060, 15)" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "users.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Int64Index: 77060 entries, 0 to 77059\n", + "Data columns (total 15 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 UserID 77060 non-null int64 \n", + " 1 WindowID 77060 non-null int64 \n", + " 2 Split 77060 non-null object \n", + " 3 City 77060 non-null object \n", + " 4 State 76952 non-null object \n", + " 5 Country 77060 non-null object \n", + " 6 ZipCode 76704 non-null object \n", + " 7 DegreeType 77060 non-null object \n", + " 8 Major 58219 non-null object \n", + " 9 GraduationDate 53852 non-null object \n", + " 10 WorkHistoryCount 77060 non-null int64 \n", + " 11 TotalYearsExperience 74212 non-null float64\n", + " 12 CurrentlyEmployed 67033 non-null object \n", + " 13 ManagedOthers 77060 non-null object \n", + " 14 ManagedHowMany 77060 non-null int64 \n", + "dtypes: float64(1), int64(4), object(10)\n", + "memory usage: 9.4+ MB\n" + ] + } + ], + "source": [ + "# users information\n", + "users.info()" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " UserID WindowID\n", + "0 767 1\n", + "1 769 1\n", + "2 861 1\n", + "3 1006 1\n", + "4 1192 1" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "test_users = test_users[test_users['WindowID']==1]\n", + "test_users.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Index(['UserID', 'WindowID'], dtype='object')" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "test_users.columns" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(5419, 2)" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "test_users.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Int64Index: 5419 entries, 0 to 5418\n", + "Data columns (total 2 columns):\n", + " # Column Non-Null Count Dtype\n", + "--- ------ -------------- -----\n", + " 0 UserID 5419 non-null int64\n", + " 1 WindowID 5419 non-null int64\n", + "dtypes: int64(2)\n", + "memory usage: 127.0 KB\n" + ] + } + ], + "source": [ + "# test_users information\n", + "test_users.info()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Exploratory Data Analysis (EDA) and Pre-processing" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Split training and testing data based on column `split`" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "* Here, there are three datafiles/dataframes are having attribute split. \n", + " * apps\n", + " * user_history\n", + " * users\n", + "* This data attribute indicates that whether the data record can be used for training or testing so we need to filter out based on that. \n", + "* We are generating training and testing dataframes \n" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [], + "source": [ + "# spliting apps data\n", + "apps_training = apps.loc[apps['Split'] == 'Train']" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(303833, 5)" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "apps_training.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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UserIDWindowIDSplitApplicationDateJobID
35357714719761Train2012-04-02 13:53:18.88702563
35357814719761Train2012-04-02 13:52:16.3271020868
35357914719761Train2012-04-02 13:00:52.527891097
35358014719831Train2012-04-09 21:41:05.663553373
35358114719831Train2012-04-09 21:56:46.787553371
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" + ], + "text/plain": [ + " UserID WindowID Split ApplicationDate JobID\n", + "353577 1471976 1 Train 2012-04-02 13:53:18.88 702563\n", + "353578 1471976 1 Train 2012-04-02 13:52:16.327 1020868\n", + "353579 1471976 1 Train 2012-04-02 13:00:52.527 891097\n", + "353580 1471983 1 Train 2012-04-09 21:41:05.663 553373\n", + "353581 1471983 1 Train 2012-04-09 21:56:46.787 553371" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "apps_training.tail()" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [], + "source": [ + "apps_testing = apps.loc[apps['Split'] == 'Test']" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(49749, 5)" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "apps_testing.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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UserIDWindowIDSplitApplicationDateJobID
1267671Test2012-04-01 14:37:20.02385377
1277691Test2012-04-16 22:36:52.48853328
1287691Test2012-04-09 18:59:28.19386106
1297691Test2012-04-09 18:59:31.127327571
1307691Test2012-04-08 21:29:11.993119161
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" + ], + "text/plain": [ + " UserID WindowID Split ApplicationDate JobID\n", + "126 767 1 Test 2012-04-01 14:37:20.023 85377\n", + "127 769 1 Test 2012-04-16 22:36:52.48 853328\n", + "128 769 1 Test 2012-04-09 18:59:28.193 86106\n", + "129 769 1 Test 2012-04-09 18:59:31.127 327571\n", + "130 769 1 Test 2012-04-08 21:29:11.993 119161" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "apps_testing.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [], + "source": [ + "# spliting user_history data\n", + "user_history_training = user_history.loc[user_history['Split'] =='Train']" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [], + "source": [ + "user_history_training = user_history.loc[user_history['Split'] =='Train']\n", + "user_history_testing = user_history.loc[user_history['Split'] =='Test']\n", + "apps_training = apps.loc[apps['Split'] == 'Train']\n", + "apps_testing = apps.loc[apps['Split'] == 'Test']\n", + "users_training = users.loc[users['Split']=='Train']\n", + "users_testing = users.loc[users['Split']=='Test']" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(323851, 5)" + ] + }, + "execution_count": 34, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "user_history_training.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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UserIDWindowIDSplitSequenceJobTitle
0471Train1National Space Communication Programs-Special ...
1471Train2Detention Officer
2471Train3Passenger Screener, TSA
3721Train1Lecturer, Department of Anthropology
4721Train2Student Assistant
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" + ], + "text/plain": [ + " UserID WindowID Split Sequence \\\n", + "0 47 1 Train 1 \n", + "1 47 1 Train 2 \n", + "2 47 1 Train 3 \n", + "3 72 1 Train 1 \n", + "4 72 1 Train 2 \n", + "\n", + " JobTitle \n", + "0 National Space Communication Programs-Special ... \n", + "1 Detention Officer \n", + "2 Passenger Screener, TSA \n", + "3 Lecturer, Department of Anthropology \n", + "4 Student Assistant " + ] + }, + "execution_count": 35, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "user_history_training.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [], + "source": [ + "user_history_testing = user_history.loc[user_history['Split'] =='Test']" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(24418, 5)" + ] + }, + "execution_count": 37, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "user_history_testing.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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UserIDWindowIDSplitSequenceJobTitle
1447671Test1Claims Adjuster
1457671Test2Professional Baseball Player
1467671Test3Professional Baseball Player
1477671Test4Professional Baseball Player
1487671Test5Professional Baseball Player
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" + ], + "text/plain": [ + " UserID WindowID Split Sequence JobTitle\n", + "144 767 1 Test 1 Claims Adjuster\n", + "145 767 1 Test 2 Professional Baseball Player\n", + "146 767 1 Test 3 Professional Baseball Player\n", + "147 767 1 Test 4 Professional Baseball Player\n", + "148 767 1 Test 5 Professional Baseball Player" + ] + }, + "execution_count": 38, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "user_history_testing.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [], + "source": [ + "# spliting user data\n", + "users_training = users.loc[users['Split']=='Train']" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(71641, 15)" + ] + }, + "execution_count": 40, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "users_training.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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UserIDWindowIDSplitCityStateCountryZipCodeDegreeTypeMajorGraduationDateWorkHistoryCountTotalYearsExperienceCurrentlyEmployedManagedOthersManagedHowMany
0471TrainParamountCAUS90723High SchoolNaN1999-06-01 00:00:00310.0YesNo0
1721TrainLa MesaCAUS91941Master'sAnthropology2011-01-01 00:00:00108.0YesNo0
2801TrainWilliamstownNJUS08094High SchoolNot Applicable1985-06-01 00:00:00511.0YesYes5
3981TrainAstoriaNYUS11105Master'sJournalism2007-05-01 00:00:0033.0YesNo0
41231TrainBaton RougeLAUS70808Bachelor'sAgricultural Business2011-05-01 00:00:0019.0YesNo0
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" + ], + "text/plain": [ + " UserID WindowID Split City State Country ZipCode DegreeType \\\n", + "0 47 1 Train Paramount CA US 90723 High School \n", + "1 72 1 Train La Mesa CA US 91941 Master's \n", + "2 80 1 Train Williamstown NJ US 08094 High School \n", + "3 98 1 Train Astoria NY US 11105 Master's \n", + "4 123 1 Train Baton Rouge LA US 70808 Bachelor's \n", + "\n", + " Major GraduationDate WorkHistoryCount \\\n", + "0 NaN 1999-06-01 00:00:00 3 \n", + "1 Anthropology 2011-01-01 00:00:00 10 \n", + "2 Not Applicable 1985-06-01 00:00:00 5 \n", + "3 Journalism 2007-05-01 00:00:00 3 \n", + "4 Agricultural Business 2011-05-01 00:00:00 1 \n", + "\n", + " TotalYearsExperience CurrentlyEmployed ManagedOthers ManagedHowMany \n", + "0 10.0 Yes No 0 \n", + "1 8.0 Yes No 0 \n", + "2 11.0 Yes Yes 5 \n", + "3 3.0 Yes No 0 \n", + "4 9.0 Yes No 0 " + ] + }, + "execution_count": 41, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "users_training.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": {}, + "outputs": [], + "source": [ + "users_testing = users.loc[users['Split']=='Test']" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(5419, 15)" + ] + }, + "execution_count": 43, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "users_testing.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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UserIDWindowIDSplitCityStateCountryZipCodeDegreeTypeMajorGraduationDateWorkHistoryCountTotalYearsExperienceCurrentlyEmployedManagedOthersManagedHowMany
317671TestMurrietaCAUS92562Bachelor'sUniversity Studies/Business2008-05-01 00:00:00516.0NoNo0
327691TestRoselleILUS60172Bachelor'sRadio-Television2011-05-01 00:00:0055.0YesNo0
338611TestMorrisILUS60450High SchoolGeneral Studies1989-05-01 00:00:00721.0NaNNo0
3810061TestWest ChesterPAUS19382High SchoolNot Applicable2008-06-01 00:00:0036.0YesNo0
4411921TestCincinnatiOHUS45255Bachelor'sMarketingNaN56.0YesNo0
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" + ], + "text/plain": [ + " UserID WindowID Split City State Country ZipCode DegreeType \\\n", + "31 767 1 Test Murrieta CA US 92562 Bachelor's \n", + "32 769 1 Test Roselle IL US 60172 Bachelor's \n", + "33 861 1 Test Morris IL US 60450 High School \n", + "38 1006 1 Test West Chester PA US 19382 High School \n", + "44 1192 1 Test Cincinnati OH US 45255 Bachelor's \n", + "\n", + " Major GraduationDate WorkHistoryCount \\\n", + "31 University Studies/Business 2008-05-01 00:00:00 5 \n", + "32 Radio-Television 2011-05-01 00:00:00 5 \n", + "33 General Studies 1989-05-01 00:00:00 7 \n", + "38 Not Applicable 2008-06-01 00:00:00 3 \n", + "44 Marketing NaN 5 \n", + "\n", + " TotalYearsExperience CurrentlyEmployed ManagedOthers ManagedHowMany \n", + "31 16.0 No No 0 \n", + "32 5.0 Yes No 0 \n", + "33 21.0 NaN No 0 \n", + "38 6.0 Yes No 0 \n", + "44 6.0 Yes No 0 " + ] + }, + "execution_count": 44, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "users_testing.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### List down all training data records " + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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UserIDWindowIDSplitApplicationDateJobID
0471Train2012-04-04 15:56:23.537169528
1471Train2012-04-06 01:03:00.003284009
2471Train2012-04-05 02:40:27.7532121
3471Train2012-04-05 02:37:02.673848187
4471Train2012-04-05 22:44:06.653733748
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" + ], + "text/plain": [ + " UserID WindowID Split ApplicationDate JobID\n", + "0 47 1 Train 2012-04-04 15:56:23.537 169528\n", + "1 47 1 Train 2012-04-06 01:03:00.003 284009\n", + "2 47 1 Train 2012-04-05 02:40:27.753 2121\n", + "3 47 1 Train 2012-04-05 02:37:02.673 848187\n", + "4 47 1 Train 2012-04-05 22:44:06.653 733748" + ] + }, + "execution_count": 45, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "apps_training.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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UserIDWindowIDSplitSequenceJobTitle
0471Train1National Space Communication Programs-Special ...
1471Train2Detention Officer
2471Train3Passenger Screener, TSA
3721Train1Lecturer, Department of Anthropology
4721Train2Student Assistant
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" + ], + "text/plain": [ + " UserID WindowID Split Sequence \\\n", + "0 47 1 Train 1 \n", + "1 47 1 Train 2 \n", + "2 47 1 Train 3 \n", + "3 72 1 Train 1 \n", + "4 72 1 Train 2 \n", + "\n", + " JobTitle \n", + "0 National Space Communication Programs-Special ... \n", + "1 Detention Officer \n", + "2 Passenger Screener, TSA \n", + "3 Lecturer, Department of Anthropology \n", + "4 Student Assistant " + ] + }, + "execution_count": 46, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "user_history_training.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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01234
UserID47728098123
WindowID11111
SplitTrainTrainTrainTrainTrain
CityParamountLa MesaWilliamstownAstoriaBaton Rouge
StateCACANJNYLA
CountryUSUSUSUSUS
ZipCode9072391941080941110570808
DegreeTypeHigh SchoolMaster'sHigh SchoolMaster'sBachelor's
MajorNaNAnthropologyNot ApplicableJournalismAgricultural Business
GraduationDate1999-06-01 00:00:002011-01-01 00:00:001985-06-01 00:00:002007-05-01 00:00:002011-05-01 00:00:00
WorkHistoryCount310531
TotalYearsExperience1081139
CurrentlyEmployedYesYesYesYesYes
ManagedOthersNoNoYesNoNo
ManagedHowMany00500
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JobIDWindowIDTitleDescriptionRequirementsCityStateCountryZip5StartDateEndDate
011Security Engineer/Technical Lead<p>Security Clearance Required:&nbsp; Top Secr...<p>SKILL SET</p>\\r<p>&nbsp;</p>\\r<p>Network Se...WashingtonDCUS205312012-03-07 13:17:01.6432012-04-06 23:59:59
141SAP Business Analyst / WM<strong>NO Corp. to Corp resumes&nbsp;are bein...<p><b>WHAT YOU NEED: </b></p>\\r<p>Four year co...CharlotteNCUS282172012-03-21 02:03:44.1372012-04-20 23:59:59
271P/T HUMAN RESOURCES ASSISTANT<b> <b> P/T HUMAN RESOURCES ASSISTANT</b> <...Please refer to the Job Description to view th...Winter ParkFLUS327922012-03-02 16:36:55.4472012-04-01 23:59:59
381Route Delivery DriversCITY BEVERAGES Come to work for the best in th...Please refer to the Job Description to view th...OrlandoFLUSNaN2012-03-03 09:01:10.0772012-04-02 23:59:59
491HousekeepingI make sure every part of their day is magica...Please refer to the Job Description to view th...OrlandoFLUSNaN2012-03-03 09:01:11.882012-04-02 23:59:59
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" + ], + "text/plain": [ + " JobID WindowID Title \\\n", + "0 1 1 Security Engineer/Technical Lead \n", + "1 4 1 SAP Business Analyst / WM \n", + "2 7 1 P/T HUMAN RESOURCES ASSISTANT \n", + "3 8 1 Route Delivery Drivers \n", + "4 9 1 Housekeeping \n", + "\n", + " Description \\\n", + "0

Security Clearance Required:  Top Secr... \n", + "1 NO Corp. to Corp resumes are bein... \n", + "2 P/T HUMAN RESOURCES ASSISTANT <... \n", + "3 CITY BEVERAGES Come to work for the best in th... \n", + "4 I make sure every part of their day is magica... \n", + "\n", + " Requirements City State \\\n", + "0

SKILL SET

\\r

 

\\r

Network Se... Washington DC \n", + "1

WHAT YOU NEED:

\\r

Four year co... Charlotte NC \n", + "2 Please refer to the Job Description to view th... Winter Park FL \n", + "3 Please refer to the Job Description to view th... Orlando FL \n", + "4 Please refer to the Job Description to view th... Orlando FL \n", + "\n", + " Country Zip5 StartDate EndDate \n", + "0 US 20531 2012-03-07 13:17:01.643 2012-04-06 23:59:59 \n", + "1 US 28217 2012-03-21 02:03:44.137 2012-04-20 23:59:59 \n", + "2 US 32792 2012-03-02 16:36:55.447 2012-04-01 23:59:59 \n", + "3 US NaN 2012-03-03 09:01:10.077 2012-04-02 23:59:59 \n", + "4 US NaN 2012-03-03 09:01:11.88 2012-04-02 23:59:59 " + ] + }, + "execution_count": 48, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "jobs.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(77060, 15)" + ] + }, + "execution_count": 49, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "users.shape" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## revised approach\n", + "\n", + "\n", + "### Let's find out Similar jobs" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Index(['JobID', 'WindowID', 'Title', 'Description', 'Requirements', 'City',\n", + " 'State', 'Country', 'Zip5', 'StartDate', 'EndDate'],\n", + " dtype='object')" + ] + }, + "execution_count": 50, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "jobs_base_line = jobs\n", + "jobs_base_line.columns" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": {}, + "outputs": [], + "source": [ + "# jobs_US.head().transpose()" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": {}, + "outputs": [], + "source": [ + "jobs_base_line = jobs_base_line.iloc[0:10000,0:8]" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": {}, + "outputs": [], + "source": [ + "# jobs_base_line.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "metadata": {}, + "outputs": [], + "source": [ + "jobs_base_line['Title'] = jobs_base_line['Title'].fillna('')\n", + "jobs_base_line['Description'] = jobs_base_line['Description'].fillna('')\n", + "#jobs_base_line['Requirements'] = jobs_base_line['Requirements'].fillna('')\n", + "\n", + "jobs_base_line['Description'] = jobs_base_line['Title'] + jobs_base_line['Description']" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": {}, + "outputs": [], + "source": [ + "tf = TfidfVectorizer(analyzer='word',ngram_range=(1, 2),min_df=0, stop_words='english')\n", + "tfidf_matrix = tf.fit_transform(jobs_base_line['Description'])" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(10000, 515585)" + ] + }, + "execution_count": 56, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "tfidf_matrix.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "metadata": {}, + "outputs": [], + "source": [ + "# http://scikit-learn.org/stable/modules/metrics.html#linear-kernel\n", + "# cosine_sim = linear_kernel(tfidf_matrix, tfidf_matrix)\n", + "cosine_sim = cosine_similarity(tfidf_matrix)" + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([1. , 0.04301522, 0.00643905, ..., 0.03802139, 0.03802139,\n", + " 0.03802139])" + ] + }, + "execution_count": 58, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cosine_sim[0]" + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "metadata": {}, + "outputs": [], + "source": [ + "jobs_base_line = jobs_base_line.reset_index()\n", + "titles = jobs_base_line['Title']\n", + "indices = pd.Series(jobs_base_line.index, index=jobs_base_line['Title'])\n", + "#indices.head(2)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "metadata": {}, + "outputs": [], + "source": [ + "def get_recommendations(title):\n", + " idx = indices[title]\n", + " #print (idx)\n", + " sim_scores = list(enumerate(cosine_sim[idx]))\n", + " #print (sim_scores)\n", + " sim_scores = sorted(sim_scores, key=lambda x: x[1], reverse=True)\n", + " job_indices = [i[0] for i in sim_scores]\n", + " return titles.iloc[job_indices]" + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "1 SAP Business Analyst / WM\n", + "6054 SAP FI/CO Business Consultant\n", + "5871 SAP FI/CO Business Analyst\n", + "5162 SAP Basis Administrator\n", + "5354 SAP Sales and Distribution Solution Architect\n", + "4799 Senior Specialist - SAP Configuration - SD\n", + "5120 SAP Integration Specialist\n", + "5412 Senior Business Systems Analyst - SAP\n", + "5247 Business Analyst\n", + "4731 SAP ABAP Developer with PRA experience\n", + "Name: Title, dtype: object" + ] + }, + "execution_count": 61, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "get_recommendations('SAP Business Analyst / WM').head(10)" + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0 Security Engineer/Technical Lead\n", + "5909 Senior Security Engineer\n", + "3774 Director of Admissions\n", + "6296 3 Network Architects needed - immediate\n", + "3560 Assistant Manager\n", + "401 National Sales & Marketing Manager\n", + "2608 Inventory Analyst/ Scheduler\n", + "3760 CLINICAL PHARMACIST\n", + "3481 Customer Service Representatives\n", + "3561 Store Manager\n", + "Name: Title, dtype: object" + ] + }, + "execution_count": 62, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "get_recommendations('Security Engineer/Technical Lead').head(10)" + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "13 Immediate Opening\n", + "216 Accounting/Bookkeeper\n", + "2874 Cable TV/Internet/Telephone Installers\n", + "8426 no job\n", + "4031 Electricians\n", + "4032 Electricians\n", + "4033 Electricians\n", + "620 DENTAL\n", + "93 A/C HEATING REFRIG MECHANIC\n", + "125 Optician\n", + "Name: Title, dtype: object" + ] + }, + "execution_count": 63, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "get_recommendations('Immediate Opening').head(10)" + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "26 EXPERIENCED ROOFERS\n", + "7952 Commercial Roofers EXPERIENCED in Hot Asphal...\n", + "51 Driver\n", + "8015 OFFICE MANAGER\n", + "53 DRIVERS\n", + "33 CNA OPENINGS AT TUSKAWILLA SNF\n", + "44 SALES REPRESENTATIVE\n", + "30 Automotive Retail Dealer\n", + "60 Associate Attorney\n", + "59 SECURITY GUARDS\n", + "Name: Title, dtype: object" + ] + }, + "execution_count": 64, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "get_recommendations('EXPERIENCED ROOFERS').head(10)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Best approach\n", + "\n", + "#### Find out similar users -- Find out for which jobs they have applied -- suggest those job to the other users who shared similar user profile.\n", + "\n", + "We are finding put similar user profile based on their degree type, majors and total years of experience. \n", + "* We will get to 10 similar users.\n", + "* We will find our which are the jobs for which these users have applied\n", + "* We take an union of these jobs and recommend the jobs all these user base" + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "

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UserIDWindowIDSplitCityStateCountryZipCodeDegreeTypeMajorGraduationDateWorkHistoryCountTotalYearsExperienceCurrentlyEmployedManagedOthersManagedHowMany
0471TrainParamountCAUS90723High SchoolNaN1999-06-01 00:00:00310.0YesNo0
1721TrainLa MesaCAUS91941Master'sAnthropology2011-01-01 00:00:00108.0YesNo0
2801TrainWilliamstownNJUS08094High SchoolNot Applicable1985-06-01 00:00:00511.0YesYes5
3981TrainAstoriaNYUS11105Master'sJournalism2007-05-01 00:00:0033.0YesNo0
41231TrainBaton RougeLAUS70808Bachelor'sAgricultural Business2011-05-01 00:00:0019.0YesNo0
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" + ], + "text/plain": [ + " UserID WindowID Split City State Country ZipCode DegreeType \\\n", + "0 47 1 Train Paramount CA US 90723 High School \n", + "1 72 1 Train La Mesa CA US 91941 Master's \n", + "2 80 1 Train Williamstown NJ US 08094 High School \n", + "3 98 1 Train Astoria NY US 11105 Master's \n", + "4 123 1 Train Baton Rouge LA US 70808 Bachelor's \n", + "\n", + " Major GraduationDate WorkHistoryCount \\\n", + "0 NaN 1999-06-01 00:00:00 3 \n", + "1 Anthropology 2011-01-01 00:00:00 10 \n", + "2 Not Applicable 1985-06-01 00:00:00 5 \n", + "3 Journalism 2007-05-01 00:00:00 3 \n", + "4 Agricultural Business 2011-05-01 00:00:00 1 \n", + "\n", + " TotalYearsExperience CurrentlyEmployed ManagedOthers ManagedHowMany \n", + "0 10.0 Yes No 0 \n", + "1 8.0 Yes No 0 \n", + "2 11.0 Yes Yes 5 \n", + "3 3.0 Yes No 0 \n", + "4 9.0 Yes No 0 " + ] + }, + "execution_count": 65, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "users_training.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "metadata": { + "scrolled": false + }, + "outputs": [], + "source": [ + "user_based_approach = users_training" + ] + }, + { + "cell_type": "code", + "execution_count": 67, + "metadata": {}, + "outputs": [], + "source": [ + "# for each in user_based_approach.index:\n", + "# userid = user_based_approach.iloc[each].UserID\n", + "# all_work = ''.join(list(user_history[user_history['UserID'] == userid]['JobTitle']))\n", + "# user_based_approach.iloc[each]['WorkHistory'].replace('',all_work)\n", + "# print(all_work)\n", + "# break" + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "metadata": {}, + "outputs": [], + "source": [ + "# function for adding user_history data to the user data\n", + "def create_work_history(userid):\n", + " return ''.join(list(user_history_training[user_history_training['UserID'] == userid]['JobTitle']))" + ] + }, + { + "cell_type": "code", + "execution_count": 69, + "metadata": {}, + "outputs": [], + "source": [ + "# add user work history data\n", + "user_based_approach['WorkHistory'] = user_based_approach['UserID'].apply(lambda x: create_work_history(x))" + ] + }, + { + "cell_type": "code", + "execution_count": 70, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(71641, 16)" + ] + }, + "execution_count": 70, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "user_based_approach.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 71, + "metadata": {}, + "outputs": [], + "source": [ + "user_based_approach = user_based_approach.iloc[0:20000,:]" + ] + }, + { + "cell_type": "code", + "execution_count": 72, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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UserIDWindowIDSplitCityStateCountryZipCodeDegreeTypeMajorGraduationDateWorkHistoryCountTotalYearsExperienceCurrentlyEmployedManagedOthersManagedHowManyWorkHistory
0471TrainParamountCAUS90723High SchoolNaN1999-06-01 00:00:00310.0YesNo0National Space Communication Programs-Special ...
1721TrainLa MesaCAUS91941Master'sAnthropology2011-01-01 00:00:00108.0YesNo0Lecturer, Department of AnthropologyStudent As...
2801TrainWilliamstownNJUS08094High SchoolNot Applicable1985-06-01 00:00:00511.0YesYes5Auto Publishing/Electro Mechanical Technician,...
3981TrainAstoriaNYUS11105Master'sJournalism2007-05-01 00:00:0033.0YesNo0Editor-in-ChiefDeputy Sports & Website EditorA...
41231TrainBaton RougeLAUS70808Bachelor'sAgricultural Business2011-05-01 00:00:0019.0YesNo0Lead Hostess and Takeout Server
\n", + "
" + ], + "text/plain": [ + " UserID WindowID Split City State Country ZipCode DegreeType \\\n", + "0 47 1 Train Paramount CA US 90723 High School \n", + "1 72 1 Train La Mesa CA US 91941 Master's \n", + "2 80 1 Train Williamstown NJ US 08094 High School \n", + "3 98 1 Train Astoria NY US 11105 Master's \n", + "4 123 1 Train Baton Rouge LA US 70808 Bachelor's \n", + "\n", + " Major GraduationDate WorkHistoryCount \\\n", + "0 NaN 1999-06-01 00:00:00 3 \n", + "1 Anthropology 2011-01-01 00:00:00 10 \n", + "2 Not Applicable 1985-06-01 00:00:00 5 \n", + "3 Journalism 2007-05-01 00:00:00 3 \n", + "4 Agricultural Business 2011-05-01 00:00:00 1 \n", + "\n", + " TotalYearsExperience CurrentlyEmployed ManagedOthers ManagedHowMany \\\n", + "0 10.0 Yes No 0 \n", + "1 8.0 Yes No 0 \n", + "2 11.0 Yes Yes 5 \n", + "3 3.0 Yes No 0 \n", + "4 9.0 Yes No 0 \n", + "\n", + " WorkHistory \n", + "0 National Space Communication Programs-Special ... \n", + "1 Lecturer, Department of AnthropologyStudent As... \n", + "2 Auto Publishing/Electro Mechanical Technician,... \n", + "3 Editor-in-ChiefDeputy Sports & Website EditorA... \n", + "4 Lead Hostess and Takeout Server " + ] + }, + "execution_count": 72, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "user_based_approach.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 73, + "metadata": {}, + "outputs": [], + "source": [ + "user_based_approach = user_based_approach.reset_index()\n", + "userid = user_based_approach['UserID']\n", + "indices = pd.Series(user_based_approach.index, index=user_based_approach['UserID'])\n", + "#indices.head(2)" + ] + }, + { + "cell_type": "code", + "execution_count": 74, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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indexUserIDWindowIDSplitCityStateCountryZipCodeDegreeTypeMajorGraduationDateWorkHistoryCountTotalYearsExperienceCurrentlyEmployedManagedOthersManagedHowManyWorkHistory
00471TrainParamountCAUS90723High SchoolNaN1999-06-01 00:00:00310.0YesNo0National Space Communication Programs-Special ...
11721TrainLa MesaCAUS91941Master'sAnthropology2011-01-01 00:00:00108.0YesNo0Lecturer, Department of AnthropologyStudent As...
22801TrainWilliamstownNJUS08094High SchoolNot Applicable1985-06-01 00:00:00511.0YesYes5Auto Publishing/Electro Mechanical Technician,...
33981TrainAstoriaNYUS11105Master'sJournalism2007-05-01 00:00:0033.0YesNo0Editor-in-ChiefDeputy Sports & Website EditorA...
441231TrainBaton RougeLAUS70808Bachelor'sAgricultural Business2011-05-01 00:00:0019.0YesNo0Lead Hostess and Takeout Server
\n", + "
" + ], + "text/plain": [ + " index UserID WindowID Split City State Country ZipCode \\\n", + "0 0 47 1 Train Paramount CA US 90723 \n", + "1 1 72 1 Train La Mesa CA US 91941 \n", + "2 2 80 1 Train Williamstown NJ US 08094 \n", + "3 3 98 1 Train Astoria NY US 11105 \n", + "4 4 123 1 Train Baton Rouge LA US 70808 \n", + "\n", + " DegreeType Major GraduationDate WorkHistoryCount \\\n", + "0 High School NaN 1999-06-01 00:00:00 3 \n", + "1 Master's Anthropology 2011-01-01 00:00:00 10 \n", + "2 High School Not Applicable 1985-06-01 00:00:00 5 \n", + "3 Master's Journalism 2007-05-01 00:00:00 3 \n", + "4 Bachelor's Agricultural Business 2011-05-01 00:00:00 1 \n", + "\n", + " TotalYearsExperience CurrentlyEmployed ManagedOthers ManagedHowMany \\\n", + "0 10.0 Yes No 0 \n", + "1 8.0 Yes No 0 \n", + "2 11.0 Yes Yes 5 \n", + "3 3.0 Yes No 0 \n", + "4 9.0 Yes No 0 \n", + "\n", + " WorkHistory \n", + "0 National Space Communication Programs-Special ... \n", + "1 Lecturer, Department of AnthropologyStudent As... \n", + "2 Auto Publishing/Electro Mechanical Technician,... \n", + "3 Editor-in-ChiefDeputy Sports & Website EditorA... \n", + "4 Lead Hostess and Takeout Server " + ] + }, + "execution_count": 74, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "user_based_approach.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 75, + "metadata": {}, + "outputs": [], + "source": [ + "user_based_approach['DegreeType'] = user_based_approach['DegreeType'].fillna('')\n", + "user_based_approach['Major'] = user_based_approach['Major'].fillna('')\n", + "user_based_approach['TotalYearsExperience'] = str(user_based_approach['TotalYearsExperience'].fillna(''))\n", + "\n", + "user_based_approach['DegreeType'] = user_based_approach['DegreeType'] + user_based_approach['Major'] + \\\n", + " user_based_approach['TotalYearsExperience'] + user_based_approach['WorkHistory']\n" + ] + }, + { + "cell_type": "code", + "execution_count": 76, + "metadata": {}, + "outputs": [], + "source": [ + "tf = TfidfVectorizer(analyzer='word',ngram_range=(1, 2),min_df=0, stop_words='english')\n", + "tfidf_matrix = tf.fit_transform(user_based_approach['DegreeType'])" + ] + }, + { + "cell_type": "code", + "execution_count": 77, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(20000, 173049)" + ] + }, + "execution_count": 77, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "tfidf_matrix.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 78, + "metadata": {}, + "outputs": [], + "source": [ + "# http://scikit-learn.org/stable/modules/metrics.html#linear-kernel\n", + "cosine_sim1 = linear_kernel(tfidf_matrix, tfidf_matrix)\n", + "# cosine_sim = cosine_similarity(tfidf_matrix)" + ] + }, + { + "cell_type": "code", + "execution_count": 79, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(10000, 10000)" + ] + }, + "execution_count": 79, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cosine_sim.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 80, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([1. , 0.04301522, 0.00643905, ..., 0.03802139, 0.03802139,\n", + " 0.03802139])" + ] + }, + "execution_count": 80, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cosine_sim[0]" + ] + }, + { + "cell_type": "code", + "execution_count": 81, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "47\n" + ] + } + ], + "source": [ + "# a = [i for i in test_users.UserID if i in indices]\n", + "# print(a)\n", + "for i in indices.index:\n", + " print(i)\n", + " break" + ] + }, + { + "cell_type": "code", + "execution_count": 82, + "metadata": {}, + "outputs": [], + "source": [ + "# Function for finding the index of similar user\n", + "def get_recommendations_userwise(userid,length=11):\n", + " idx = indices[userid]\n", + "# print (idx)\n", + " sim_scores = list(enumerate(cosine_sim[idx]))\n", + "# print (sim_scores[:10])\n", + " sim_scores = sorted(sim_scores, key=lambda x: x[1], reverse=True)\n", + "# print(sim_scores[:10])\n", + " user_indices = [i[0] for i in sim_scores]\n", + "# print (user_indices[:10])\n", + " return user_indices[0:length]" + ] + }, + { + "cell_type": "code", + "execution_count": 83, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-----Top 10 Similar users with userId: 72------\n" + ] + }, + { + "data": { + "text/plain": [ + "[1, 6054, 5871, 5162, 5354, 4799, 5120, 5412, 5247, 4731]" + ] + }, + "execution_count": 83, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "print (\"-----Top 10 Similar users with userId: 72------\")\n", + "get_recommendations_userwise(72,10)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 84, + "metadata": {}, + "outputs": [], + "source": [ + "# Convertion of user indices to the userId\n", + "def convert_to_userID(user_index):\n", + " user_idx = user_based_approach['index'].isin(user_index)\n", + " df1 = pd.DataFrame(data = user_based_approach[user_idx], columns=['UserID'])\n", + " return df1['UserID'].tolist()" + ] + }, + { + "cell_type": "code", + "execution_count": 85, + "metadata": {}, + "outputs": [], + "source": [ + "# get the job list from the similar users\n", + "def get_job_id(usrid_list):\n", + " jobs_userwise = apps_training['UserID'].isin(usrid_list) #\n", + " df1 = pd.DataFrame(data = apps_training[jobs_userwise], columns=['JobID'])\n", + " joblist = df1['JobID'].tolist()\n", + " Job_list = jobs['JobID'].isin(joblist) #[1083186, 516837, 507614, 754917, 686406, 1058896, 335132])\n", + " df_temp = pd.DataFrame(data = jobs[Job_list], columns=['JobID','Title','Description','City','State'])\n", + " return df_temp[:11]" + ] + }, + { + "cell_type": "code", + "execution_count": 86, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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indexUserIDWindowIDSplitCityStateCountryZipCodeDegreeTypeMajorGraduationDateWorkHistoryCountTotalYearsExperienceCurrentlyEmployedManagedOthersManagedHowManyWorkHistory
441231TrainBaton RougeLAUS70808Bachelor'sAgricultural Business0 10\\n1 ...Agricultural Business2011-05-01 00:00:0010 10\\n1 8\\n2 11\\n3 ...YesNo0Lead Hostess and Takeout Server
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" + ], + "text/plain": [ + " index UserID WindowID Split City State Country ZipCode \\\n", + "4 4 123 1 Train Baton Rouge LA US 70808 \n", + "\n", + " DegreeType Major \\\n", + "4 Bachelor'sAgricultural Business0 10\\n1 ... Agricultural Business \n", + "\n", + " GraduationDate WorkHistoryCount \\\n", + "4 2011-05-01 00:00:00 1 \n", + "\n", + " TotalYearsExperience CurrentlyEmployed \\\n", + "4 0 10\\n1 8\\n2 11\\n3 ... Yes \n", + "\n", + " ManagedOthers ManagedHowMany WorkHistory \n", + "4 No 0 Lead Hostess and Takeout Server " + ] + }, + "execution_count": 86, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "user_based_approach[user_based_approach['UserID']==123]" + ] + }, + { + "cell_type": "code", + "execution_count": 87, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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JobIDTitleDescriptionCityState
16536867Mail Sorters - Part Time Evenings<div>\\r<div>\\r<div>\\r<p><strong>Mail Sorters -...SecaucusNJ
250410312Receptionist<span>To assist all departments in the dealers...ArlingtonTX
285411623Receptionist/HR Assistant<p><span>Operates system switchboard, </span><...MansfieldTX
395515796Maintenance Opportunities<p align=\"center\"><strong>Maintenance Opportun...Green BayWI
2021279199Community Support CoordinatorCommunity Options, Inc. is a national nonprofi...MoorestownNJ
2123384219Human Resources Business Process Analyst<b>Job ID:</b> 80006\\r\\n\\r\\n<b>Position Descri...RoanokeVA
50179196495Set Up Technician<SPAN></SPAN>WPI, a manufacturer of plastic in...Green BayWI
62581246306Front desk assistantF/t Personable, energetic, multitasker needed ...BedfordTX
63838250360Financial Representative<p><b><span style=\"text-decoration: underline\"...Delray BeachFL
76486300459FC BkkpFC Bkkp - P/T, Exp. w/Accrual, Multiple Books...Fort LauderdaleFL
79862314080Clinical Telephone Operator<P><SPAN>Primary responsibilities include oper...SartellMN
\n", + "
" + ], + "text/plain": [ + " JobID Title \\\n", + "1653 6867 Mail Sorters - Part Time Evenings \n", + "2504 10312 Receptionist \n", + "2854 11623 Receptionist/HR Assistant \n", + "3955 15796 Maintenance Opportunities \n", + "20212 79199 Community Support Coordinator \n", + "21233 84219 Human Resources Business Process Analyst \n", + "50179 196495 Set Up Technician \n", + "62581 246306 Front desk assistant \n", + "63838 250360 Financial Representative \n", + "76486 300459 FC Bkkp \n", + "79862 314080 Clinical Telephone Operator \n", + "\n", + " Description City \\\n", + "1653
\\r
\\r
\\r

Mail Sorters -... Secaucus \n", + "2504 To assist all departments in the dealers... Arlington \n", + "2854

Operates system switchboard, <... Mansfield \n", + "3955

Maintenance Opportun... Green Bay \n", + "20212 Community Options, Inc. is a national nonprofi... Moorestown \n", + "21233 Job ID: 80006\\r\\n\\r\\nPosition Descri... Roanoke \n", + "50179 WPI, a manufacturer of plastic in... Green Bay \n", + "62581 F/t Personable, energetic, multitasker needed ... Bedford \n", + "63838

Primary responsibilities include oper... Sartell \n", + "\n", + " State \n", + "1653 NJ \n", + "2504 TX \n", + "2854 TX \n", + "3955 WI \n", + "20212 NJ \n", + "21233 VA \n", + "50179 WI \n", + "62581 TX \n", + "63838 FL \n", + "76486 FL \n", + "79862 MN " + ] + }, + "execution_count": 87, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "get_job_id(convert_to_userID(get_recommendations_userwise(123)))" + ] + }, + { + "cell_type": "code", + "execution_count": 88, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[4, 23, 3378, 6008, 6311, 41, 56, 9815, 7700, 2170]" + ] + }, + "execution_count": 88, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "get_recommendations_userwise(123,10)" + ] + }, + { + "cell_type": "code", + "execution_count": 89, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-----Top 10 Similar users with userId: 47------\n" + ] + }, + { + "data": { + "text/plain": [ + "[0, 5909, 3774, 6296, 3560, 401, 2608, 3760, 3481, 3561, 2603]" + ] + }, + "execution_count": 89, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "print (\"-----Top 10 Similar users with userId: 47------\")\n", + "get_recommendations_userwise(47)" + ] + }, + { + "cell_type": "code", + "execution_count": 90, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "

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JobIDTitleDescriptionCityState
6092121MEDICAL- FRONT OFFICEMedical - Front Office\\r\\nIndustrial Clinic, ...Los AngelesCA
438817358Data Entry - Customer Service Representative ...<div style=\"text-align: center\"><strong>\\r<p s...GreenvilleSC
782028992Customer Service Rep Experienced CSRs needed ...Customer Service Rep\\r\\n Experienced CSRs...NashvilleTN
1059839361Customer Service Representative<br>\\r<p style=\"text-align: center\" align=\"cen...CharlotteNC
2387392858Compliance Specialist II — Advertising ReviewOur unique culture of independence gives Raymo...Saint PetersburgFL
34021132647Purchasing Assistant<div><p>&nbsp;</p>\\r<p><b><span>ESSENTIAL DUTI...CharlotteNC
42812169528Resort Host/Marketing Coordinator - Anaheim, CA<P STYLE=\"MARGIN-TOP: 0px; MARGIN-BOTTOM: 0px\"...AnaheimCA
47531186401Tax AssociateTax Associate\\t\\t\\t20-32583576\\n\\nA large insu...Hoffman EstatesIL
48693190688Medical Assistant/Per DiemKelly Healthcare Resources<BR> <BR><BR>Kelly H...BellevilleIL
55344217664Office AdministratorFORT MILL MANUFACTURE SEEKING IMMEDIATE HIRE!\\...Fort MillSC
55357217697Executive Administrative Assistant FTHOLY ANGEL THE HARDEST JOB YOU'LL EVER LOVE! E...BelmontNC
\n", + "
" + ], + "text/plain": [ + " JobID Title \\\n", + "609 2121 MEDICAL- FRONT OFFICE \n", + "4388 17358 Data Entry - Customer Service Representative ... \n", + "7820 28992 Customer Service Rep Experienced CSRs needed ... \n", + "10598 39361 Customer Service Representative \n", + "23873 92858 Compliance Specialist II — Advertising Review \n", + "34021 132647 Purchasing Assistant \n", + "42812 169528 Resort Host/Marketing Coordinator - Anaheim, CA \n", + "47531 186401 Tax Associate \n", + "48693 190688 Medical Assistant/Per Diem \n", + "55344 217664 Office Administrator \n", + "55357 217697 Executive Administrative Assistant FT \n", + "\n", + " Description City \\\n", + "609 Medical - Front Office\\r\\nIndustrial Clinic, ... Los Angeles \n", + "4388
\\r

\\r

 

\\r

ESSENTIAL DUTI... Charlotte \n", + "42812



Kelly H... Belleville \n", + "55344 FORT MILL MANUFACTURE SEEKING IMMEDIATE HIRE!\\... Fort Mill \n", + "55357 HOLY ANGEL THE HARDEST JOB YOU'LL EVER LOVE! E... Belmont \n", + "\n", + " State \n", + "609 CA \n", + "4388 SC \n", + "7820 TN \n", + "10598 NC \n", + "23873 FL \n", + "34021 NC \n", + "42812 CA \n", + "47531 IL \n", + "48693 IL \n", + "55344 SC \n", + "55357 NC " + ] + }, + "execution_count": 90, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "get_job_id(convert_to_userID(get_recommendations_userwise(47,10)))" + ] + }, + { + "cell_type": "code", + "execution_count": 91, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "

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UserIDWindowIDSplitCityStateCountryZipCodeDegreeTypeMajorGraduationDateWorkHistoryCountTotalYearsExperienceCurrentlyEmployedManagedOthersManagedHowMany
0471TrainParamountCAUS90723High SchoolNaN1999-06-01 00:00:00310.0YesNo0
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" + ], + "text/plain": [ + " UserID WindowID Split City State Country ZipCode DegreeType \\\n", + "0 47 1 Train Paramount CA US 90723 High School \n", + "\n", + " Major GraduationDate WorkHistoryCount TotalYearsExperience \\\n", + "0 NaN 1999-06-01 00:00:00 3 10.0 \n", + "\n", + " CurrentlyEmployed ManagedOthers ManagedHowMany \n", + "0 Yes No 0 " + ] + }, + "execution_count": 91, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "users.loc[users.UserID == 47]" + ] + }, + { + "cell_type": "code", + "execution_count": 92, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[169528, 284009, 2121, 848187, 733748, 576958, 262470, 602298]" + ] + }, + "execution_count": 92, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "b = list(apps.loc[apps.UserID == 47]['JobID'])\n", + "b" + ] + }, + { + "cell_type": "code", + "execution_count": 93, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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JobIDWindowIDTitleDescriptionRequirementsCityStateCountryZip5StartDateEndDate
428121695281Resort Host/Marketing Coordinator - Anaheim, CA<P STYLE=\"MARGIN-TOP: 0px; MARGIN-BOTTOM: 0px\"...• Bachelor's Degree preferred;<BR> \\r\\n• Posse...AnaheimCAUS928012012-04-03 22:31:26.412012-05-02 23:59:59
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" + ], + "text/plain": [ + " JobID WindowID Title \\\n", + "42812 169528 1 Resort Host/Marketing Coordinator - Anaheim, CA \n", + "\n", + " Description \\\n", + "42812

\\r\\n• Posse... Anaheim CA \n", + "\n", + " Country Zip5 StartDate EndDate \n", + "42812 US 92801 2012-04-03 22:31:26.41 2012-05-02 23:59:59 " + ] + }, + "execution_count": 93, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "jobs.loc[jobs.JobID == 169528]" + ] + }, + { + "cell_type": "code", + "execution_count": 94, + "metadata": {}, + "outputs": [], + "source": [ + "# jobs.at[853328,'Requirements']" + ] + }, + { + "cell_type": "code", + "execution_count": 95, + "metadata": {}, + "outputs": [], + "source": [ + "# def find_accuracy(indices):\n", + "# total = 0\n", + "# for uid in indices.index:\n", + "# app_job_ids=set(list(apps.loc[apps.UserID == uid]['JobID']))\n", + "# len_app_job = len(app_job_ids)\n", + "# if len_app_job != 0:\n", + "# rec_job_ids=get_job_id(get_recommendations_userwise(uid, len_app_job))\n", + "# total += len(app_job_ids.intersection(rec_job_ids))/len_app_job\n", + "# return total/len(indices.index)" + ] + } + ], + "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": "3.6.8" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/intern-basics/Job Recommendation System/Learn.md b/intern-basics/Job Recommendation System/Learn.md new file mode 100644 index 0000000..f393e4e --- /dev/null +++ b/intern-basics/Job Recommendation System/Learn.md @@ -0,0 +1,30 @@ +# TfidfTransformer + +*(sklearn.feature_extraction.text.TfidfVectorizer)* + +It Converts a collection of raw documents to a matrix of TF-IDF features. +It is Equivalent to *CountVectorizer* followed by *TfidfTransformer*. +* CountVectorizer implements both tokenization and occurrence counting in a single class. +* TfidfTransformer helps in removing the stop words (e.g. “the”, “a”, “is” in English) which carry very little information about the actual contents of the document. + +Tf means **term-frequency** while tf–idf means term-frequency times **inverse document-frequency**: tf-idf(t,d)=tf(t,d)\*idf(t). + +Using the `TfidfTransformer`’s default settings, `TfidfTransformer(norm='l2', use_idf=True, smooth_idf=True, sublinear_tf=False)` the term frequency, the number of times a term occurs in a given document, is multiplied with idf component, which is computed as + +### idf(t) = log((1+n)/1+df(t)) +1 + +where n is the total number of documents in the document set, and df(t) is the number of documents in the document set that contain the term t. The resulting tf-idf vectors are then normalized by the Euclidean norm. + +# cosine_similarity + +*sklearn.metrics.pairwise.cosine_similarity(X, Y=None, dense_output=True)* + +Compute cosine similarity between samples in X and Y. + +Cosine similarity, or the cosine kernel, computes similarity as the normalized dot product of X and Y: + +![cosine similarity](https://wikimedia.org/api/rest_v1/media/math/render/svg/1d94e5903f7936d3c131e040ef2c51b473dd071d) + +On L2-normalized data, this function is equivalent to linear_kernel. + +![cosine image](https://www.oreilly.com/library/view/statistics-for-machine/9781788295758/assets/2b4a7a82-ad4c-4b2a-b808-e423a334de6f.png) diff --git a/intern-basics/MPG_Prediction_Model/MPG_Prediction.ipynb b/intern-basics/MPG_Prediction_Model/MPG_Prediction.ipynb new file mode 100644 index 0000000..f2be7b6 --- /dev/null +++ b/intern-basics/MPG_Prediction_Model/MPG_Prediction.ipynb @@ -0,0 +1,1579 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Predicting Fuel Efficiency of Vehicles\n", + "## Selecting and Training Models\n", + "\n", + "1. Select and Train a few Algorithms(Linear Regression, Decision Tree, RandomForest)\n", + "2. Evaluation using Mean Squared Error\n", + "3. Model Evaluation using Cross Validation\n", + "4. Hyperparameter Tuning using GridSearchCV\n", + "5. Check Feature Importance\n", + "6. Evaluate the Final System on test data\n", + "7. Saving the Model" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "cols = ['MPG', 'Cylinders', 'Displacement', 'Horsepower', 'Weight', 'Acceleration', 'Model Year', 'Origin']\n", + "# Reading the .data file using pandas\n", + "df = pd.read_csv('auto-mpg.data', names=cols, na_values = '?',\n", + " comment = '\\t',\n", + " sep = ' ',\n", + " skipinitialspace = True)\n", + "# Make a copy of the dataframe\n", + "data = df.copy()" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "

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MPGCylindersDisplacementHorsepowerWeightAccelerationModel YearOrigin
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" + ], + "text/plain": [ + " MPG Cylinders Displacement Horsepower Weight Acceleration \\\n", + "309 41.5 4 98.0 76.0 2144.0 14.7 \n", + "391 36.0 4 135.0 84.0 2370.0 13.0 \n", + "16 18.0 6 199.0 97.0 2774.0 15.5 \n", + "377 31.0 4 91.0 68.0 1970.0 17.6 \n", + "220 33.5 4 85.0 70.0 1945.0 16.8 \n", + "\n", + " Model Year Origin \n", + "309 80 2 \n", + "391 82 1 \n", + "16 70 1 \n", + "377 82 3 \n", + "220 77 3 " + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.sample(5)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Problem Statement** — The data contains the MPG(Mile Per Gallon) variable which is continuous data and tells us about the efficiency of fuel consumption of a vehicle in the 70s and 80s." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "RangeIndex: 398 entries, 0 to 397\n", + "Data columns (total 8 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 MPG 398 non-null float64\n", + " 1 Cylinders 398 non-null int64 \n", + " 2 Displacement 398 non-null float64\n", + " 3 Horsepower 392 non-null float64\n", + " 4 Weight 398 non-null float64\n", + " 5 Acceleration 398 non-null float64\n", + " 6 Model Year 398 non-null int64 \n", + " 7 Origin 398 non-null int64 \n", + "dtypes: float64(5), int64(3)\n", + "memory usage: 25.0 KB\n" + ] + } + ], + "source": [ + "# Data Information\n", + "data.info()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "MPG 0\n", + "Cylinders 0\n", + "Displacement 0\n", + "Horsepower 6\n", + "Weight 0\n", + "Acceleration 0\n", + "Model Year 0\n", + "Origin 0\n", + "dtype: int64" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Check for all null values\n", + "data.isnull().sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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MPGCylindersDisplacementHorsepowerWeightAccelerationModel YearOrigin
count398.000000398.000000398.000000392.000000398.000000398.000000398.000000398.000000
mean23.5145735.454774193.425879104.4693882970.42462315.56809076.0100501.572864
std7.8159841.701004104.26983838.491160846.8417742.7576893.6976270.802055
min9.0000003.00000068.00000046.0000001613.0000008.00000070.0000001.000000
25%17.5000004.000000104.25000075.0000002223.75000013.82500073.0000001.000000
50%23.0000004.000000148.50000093.5000002803.50000015.50000076.0000001.000000
75%29.0000008.000000262.000000126.0000003608.00000017.17500079.0000002.000000
max46.6000008.000000455.000000230.0000005140.00000024.80000082.0000003.000000
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" + ], + "text/plain": [ + " MPG Cylinders Displacement Horsepower Weight \\\n", + "count 398.000000 398.000000 398.000000 392.000000 398.000000 \n", + "mean 23.514573 5.454774 193.425879 104.469388 2970.424623 \n", + "std 7.815984 1.701004 104.269838 38.491160 846.841774 \n", + "min 9.000000 3.000000 68.000000 46.000000 1613.000000 \n", + "25% 17.500000 4.000000 104.250000 75.000000 2223.750000 \n", + "50% 23.000000 4.000000 148.500000 93.500000 2803.500000 \n", + "75% 29.000000 8.000000 262.000000 126.000000 3608.000000 \n", + "max 46.600000 8.000000 455.000000 230.000000 5140.000000 \n", + "\n", + " Acceleration Model Year Origin \n", + "count 398.000000 398.000000 398.000000 \n", + "mean 15.568090 76.010050 1.572864 \n", + "std 2.757689 3.697627 0.802055 \n", + "min 8.000000 70.000000 1.000000 \n", + "25% 13.825000 73.000000 1.000000 \n", + "50% 15.500000 76.000000 1.000000 \n", + "75% 17.175000 79.000000 2.000000 \n", + "max 24.800000 82.000000 3.000000 " + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data.describe()" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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/vwcYKj++OtV1wEciYvzKfesX4M8uTeDV0LTk1acgHiyl/Fz2LNKJ8AhYkpJ4BCxJSTwClqQkBliSkhhgSUpigCUpiQGWpCT/B5Mr9KJgBckYAAAAAElFTkSuQmCC\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Check for outliers in horsepower column\n", + "sns.boxplot(x=data['Horsepower'])" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "RangeIndex: 398 entries, 0 to 397\n", + "Data columns (total 8 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 MPG 398 non-null float64\n", + " 1 Cylinders 398 non-null int64 \n", + " 2 Displacement 398 non-null float64\n", + " 3 Horsepower 398 non-null float64\n", + " 4 Weight 398 non-null float64\n", + " 5 Acceleration 398 non-null float64\n", + " 6 Model Year 398 non-null int64 \n", + " 7 Origin 398 non-null int64 \n", + "dtypes: float64(5), int64(3)\n", + "memory usage: 25.0 KB\n" + ] + } + ], + "source": [ + "# Since there are a few outliers, we can use the median of the column\n", + "# to impute the missing values using the pandas median() method.\n", + "median = data['Horsepower'].median()\n", + "data['Horsepower'] = data['Horsepower'].fillna(median)\n", + "data.info()" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "4 0.512563\n", + "8 0.258794\n", + "6 0.211055\n", + "3 0.010050\n", + "5 0.007538\n", + "Name: Cylinders, dtype: float64" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Category Distribution\n", + "data['Cylinders'].value_counts()/len(data)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "1 249\n", + "3 79\n", + "2 70\n", + "Name: Origin, dtype: int64" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data['Origin'].value_counts()" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# pairplots to get an intuition of potential correlations\n", + "sns.pairplot(data[[\"MPG\", \"Cylinders\", \"Displacement\", \"Weight\", \"Horsepower\"]], diag_kind=\"kde\")" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "# Split the data into train and test using stratified sampling\n", + "from sklearn.model_selection import StratifiedShuffleSplit\n", + "split = StratifiedShuffleSplit(n_splits=1, test_size=0.2, random_state = 42)\n", + "for train_index, test_index in split.split(data, data['Cylinders']):\n", + " strat_train_set = data.loc[train_index]\n", + " strat_test_set = data.loc[test_index]" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "4 0.512579\n", + "8 0.257862\n", + "6 0.210692\n", + "5 0.009434\n", + "3 0.009434\n", + "Name: Cylinders, dtype: float64" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Checking for the cylinder distribution in training set\n", + "strat_train_set['Cylinders'].value_counts() / len(strat_train_set)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "4 0.5125\n", + "8 0.2625\n", + "6 0.2125\n", + "3 0.0125\n", + "Name: Cylinders, dtype: float64" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# for test dataset\n", + "strat_test_set['Cylinders'].value_counts() / len(strat_test_set)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " Cylinders Displacement Horsepower Weight Acceleration Model Year \\\n", + "145 4 83.0 61.0 2003.0 19.0 74 \n", + "151 4 79.0 67.0 2000.0 16.0 74 \n", + "388 4 156.0 92.0 2585.0 14.5 82 \n", + "48 6 250.0 88.0 3139.0 14.5 71 \n", + "114 4 98.0 90.0 2265.0 15.5 73 \n", + ".. ... ... ... ... ... ... \n", + "147 4 90.0 75.0 2108.0 15.5 74 \n", + "156 8 400.0 170.0 4668.0 11.5 75 \n", + "395 4 135.0 84.0 2295.0 11.6 82 \n", + "14 4 113.0 95.0 2372.0 15.0 70 \n", + "362 6 146.0 120.0 2930.0 13.8 81 \n", + "\n", + " Origin \n", + "145 3 \n", + "151 2 \n", + "388 1 \n", + "48 1 \n", + "114 2 \n", + ".. ... \n", + "147 2 \n", + "156 1 \n", + "395 1 \n", + "14 3 \n", + "362 3 \n", + "\n", + "[318 rows x 7 columns]" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data = strat_train_set.drop(\"MPG\", axis=1)\n", + "data_labels = strat_train_set[\"MPG\"].copy()\n", + "data" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.impute import SimpleImputer\n", + "from sklearn.preprocessing import OneHotEncoder\n", + "from sklearn.pipeline import Pipeline\n", + "from sklearn.preprocessing import StandardScaler\n", + "from sklearn.compose import ColumnTransformer" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "# preprocess the Origin column in data\n", + "def preprocess_origin_cols(df):\n", + " df[\"Origin\"] = df[\"Origin\"].map({1: \"India\", 2: \"USA\", 3: \"Germany\"})\n", + " return df" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.base import BaseEstimator, TransformerMixin\n", + "\n", + "acc_ix, hpower_ix, cyl_ix = 4, 2, 0\n", + "\n", + "##custom class inheriting the BaseEstimator and TransformerMixin\n", + "class CustomAttrAdder(BaseEstimator, TransformerMixin):\n", + " def __init__(self, acc_on_power=True):\n", + " self.acc_on_power = acc_on_power # new optional variable\n", + " def fit(self, X, y=None):\n", + " return self # nothing else to do\n", + " def transform(self, X):\n", + " acc_on_cyl = X[:, acc_ix] / X[:, cyl_ix] # required new variable\n", + " if self.acc_on_power:\n", + " acc_on_power = X[:, acc_ix] / X[:, hpower_ix]\n", + " return np.c_[X, acc_on_power, acc_on_cyl] # returns a 2D array\n", + " \n", + " return np.c_[X, acc_on_cyl]" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [], + "source": [ + "def num_pipeline_transformer(data):\n", + " '''\n", + " Function to process numerical transformations\n", + " Argument:\n", + " data: original dataframe \n", + " Returns:\n", + " num_attrs: numerical dataframe\n", + " num_pipeline: numerical pipeline object\n", + " \n", + " '''\n", + " numerics = ['float64', 'int64']\n", + "\n", + " num_attrs = data.select_dtypes(include=numerics)\n", + "\n", + " num_pipeline = Pipeline([\n", + " ('imputer', SimpleImputer(strategy=\"median\")),\n", + " ('attrs_adder', CustomAttrAdder()),\n", + " ('std_scaler', StandardScaler()),\n", + " ])\n", + " return num_attrs, num_pipeline" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [], + "source": [ + "def pipeline_transformer(data):\n", + " '''\n", + " Complete transformation pipeline for both\n", + " nuerical and categorical data.\n", + " \n", + " Argument:\n", + " data: original dataframe \n", + " Returns:\n", + " prepared_data: transformed data, ready to use\n", + " '''\n", + " cat_attrs = [\"Origin\"]\n", + " num_attrs, num_pipeline = num_pipeline_transformer(data)\n", + " full_pipeline = ColumnTransformer([\n", + " (\"num\", num_pipeline, list(num_attrs)),\n", + " (\"cat\", OneHotEncoder(), cat_attrs),\n", + " ])\n", + " prepared_data = full_pipeline.fit_transform(data)\n", + " return prepared_data" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[-0.85657842, -1.07804475, -1.15261228, ..., 1. ,\n", + " 0. , 0. ],\n", + " [-0.85657842, -1.1174582 , -0.99069325, ..., 0. ,\n", + " 0. , 1. ],\n", + " [-0.85657842, -0.3587492 , -0.31603064, ..., 0. ,\n", + " 1. , 0. ],\n", + " ...,\n", + " [-0.85657842, -0.56566984, -0.53192268, ..., 0. ,\n", + " 1. , 0. ],\n", + " [-0.85657842, -0.78244384, -0.23507113, ..., 1. ,\n", + " 0. , 0. ],\n", + " [ 0.32260746, -0.45728283, 0.43959149, ..., 1. ,\n", + " 0. , 0. ]])" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# from raw data to processed data in 2 steps\n", + "\n", + "preprocessed_df = preprocess_origin_cols(data)\n", + "prepared_data = pipeline_transformer(preprocessed_df)\n", + "prepared_data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Linear Regression" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Prediction of samples: [29.07706113 27.78221362 26.08507595 12.68921922 22.24811759]\n", + "Actual Labels of samples: [32.0, 31.0, 26.0, 18.0, 26.0]\n" + ] + } + ], + "source": [ + "from sklearn.linear_model import LinearRegression\n", + "lin_reg = LinearRegression()\n", + "\n", + "lin_reg.fit(prepared_data, data_labels)\n", + "\n", + "# testing the predictions with the \n", + "sample_data = data.iloc[:5]\n", + "sample_labels = data_labels.iloc[:5]\n", + "\n", + "sample_data_prepared = pipeline_transformer(sample_data)\n", + "\n", + "print(\"Prediction of samples: \", lin_reg.predict(sample_data_prepared))\n", + "print(\"Actual Labels of samples: \", list(sample_labels))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Mean Squared Error" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "2.9599557382767734" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from sklearn.metrics import mean_squared_error\n", + "mpg_predictions = lin_reg.predict(prepared_data)\n", + "lin_mse = mean_squared_error(data_labels, mpg_predictions)\n", + "lin_rmse = np.sqrt(lin_mse)\n", + "lin_rmse" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Decision Tree" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "DecisionTreeRegressor()" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from sklearn.tree import DecisionTreeRegressor\n", + "\n", + "tree_reg = DecisionTreeRegressor()\n", + "tree_reg.fit(prepared_data, data_labels)" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.0" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "mpg_predictions = tree_reg.predict(prepared_data)\n", + "tree_mse = mean_squared_error(data_labels, mpg_predictions)\n", + "tree_rmse = np.sqrt(tree_mse)\n", + "tree_rmse" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Random Forest Model" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.9911761325749128" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from sklearn.ensemble import RandomForestRegressor\n", + "\n", + "random_tree_reg = RandomForestRegressor(random_state=24)\n", + "random_tree_reg.fit(prepared_data, data_labels)\n", + "\n", + "mpg_predictions = random_tree_reg.predict(prepared_data)\n", + "random_tree_mse = mean_squared_error(data_labels, mpg_predictions)\n", + "random_tree_rmse = np.sqrt(random_tree_mse)\n", + "random_tree_rmse" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Model Evaluation using Cross Validation" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.model_selection import cross_val_score\n", + "\n", + "scores = cross_val_score(tree_reg, \n", + " prepared_data, \n", + " data_labels, \n", + " scoring=\"neg_mean_squared_error\", \n", + " cv = 10)\n", + "tree_reg_rmse_scores = np.sqrt(-scores)" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "3.2848589809442137" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "tree_reg_rmse_scores.mean()" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "3.0767797889608994" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Linear Regression using cross validation set\n", + "scores = cross_val_score(lin_reg, prepared_data, data_labels, scoring=\"neg_mean_squared_error\", cv = 10)\n", + "lin_reg_rmse_scores = np.sqrt(-scores)\n", + "lin_reg_rmse_scores.mean()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Random Forest model ( Cross Val)" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "2.5747012406922205" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "forest_reg = RandomForestRegressor()\n", + "forest_reg.fit(prepared_data, data_labels)\n", + "forest_reg_cv_scores = cross_val_score(forest_reg,\n", + " prepared_data,\n", + " data_labels,\n", + " scoring='neg_mean_squared_error',\n", + " cv = 10)\n", + "\n", + "forest_reg_rmse_scores = np.sqrt(-forest_reg_cv_scores)\n", + "forest_reg_rmse_scores.mean()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Support Vector Machine Regressor" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "3.088411910825019" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from sklearn.svm import SVR\n", + "\n", + "svm_reg = SVR(kernel='linear')\n", + "svm_reg.fit(prepared_data, data_labels)\n", + "svm_cv_scores = cross_val_score(svm_reg, prepared_data, data_labels,\n", + " scoring='neg_mean_squared_error',\n", + " cv = 10)\n", + "svm_rmse_scores = np.sqrt(-svm_cv_scores)\n", + "svm_rmse_scores.mean()\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Hyperparameter Tuning using GridSearchCV" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "GridSearchCV(cv=10, estimator=RandomForestRegressor(),\n", + " param_grid=[{'max_features': [2, 4, 6, 8],\n", + " 'n_estimators': [3, 10, 30]},\n", + " {'bootstrap': [False], 'max_features': [2, 3, 4],\n", + " 'n_estimators': [3, 10]}],\n", + " return_train_score=True, scoring='neg_mean_squared_error')" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from sklearn.model_selection import GridSearchCV\n", + "\n", + "param_grid = [\n", + " {'n_estimators': [3, 10, 30], 'max_features': [2, 4, 6, 8]},\n", + " {'bootstrap': [False], 'n_estimators': [3, 10], 'max_features': [2, 3, 4]},\n", + " ]\n", + "\n", + "forest_reg = RandomForestRegressor()\n", + "\n", + "grid_search = GridSearchCV(forest_reg, param_grid,\n", + " scoring='neg_mean_squared_error',\n", + " return_train_score=True,\n", + " cv=10,\n", + " )\n", + "\n", + "grid_search.fit(prepared_data, data_labels)" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'max_features': 8, 'n_estimators': 30}" + ] + }, + "execution_count": 33, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "grid_search.best_params_" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "3.5787410401977535 {'max_features': 2, 'n_estimators': 3}\n", + "2.999602498867088 {'max_features': 2, 'n_estimators': 10}\n", + "2.9019422761340956 {'max_features': 2, 'n_estimators': 30}\n", + "3.222425898889705 {'max_features': 4, 'n_estimators': 3}\n", + "2.8973866892453493 {'max_features': 4, 'n_estimators': 10}\n", + "2.7825074985326963 {'max_features': 4, 'n_estimators': 30}\n", + "3.193349050542729 {'max_features': 6, 'n_estimators': 3}\n", + "2.8482872754035546 {'max_features': 6, 'n_estimators': 10}\n", + "2.700634845673968 {'max_features': 6, 'n_estimators': 30}\n", + "2.9971142221133755 {'max_features': 8, 'n_estimators': 3}\n", + "2.87655221071472 {'max_features': 8, 'n_estimators': 10}\n", + "2.699935644269864 {'max_features': 8, 'n_estimators': 30}\n", + "3.2761467526353067 {'bootstrap': False, 'max_features': 2, 'n_estimators': 3}\n", + "2.9582469709490162 {'bootstrap': False, 'max_features': 2, 'n_estimators': 10}\n", + "3.2594155209991964 {'bootstrap': False, 'max_features': 3, 'n_estimators': 3}\n", + "2.8225467027337037 {'bootstrap': False, 'max_features': 3, 'n_estimators': 10}\n", + "3.158636146007859 {'bootstrap': False, 'max_features': 4, 'n_estimators': 3}\n", + "2.9577900748728143 {'bootstrap': False, 'max_features': 4, 'n_estimators': 10}\n" + ] + } + ], + "source": [ + "cv_scores = grid_search.cv_results_\n", + "# Printing all the parameters along with their scores\n", + "for mean_score, params in zip(cv_scores['mean_test_score'], cv_scores['params']):\n", + " print(np.sqrt(-mean_score), params)" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([0.21807596, 0.28901719, 0.12451093, 0.18856819, 0.01533777,\n", + " 0.11232161, 0.02920664, 0.0181604 , 0.00210384, 0.00108823,\n", + " 0.00160924])" + ] + }, + "execution_count": 35, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Feature importance\n", + "feature_importances = grid_search.best_estimator_.feature_importances_\n", + "feature_importances" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[('acc_on_power', 0.029206638628792476),\n", + " ('acc_on_cyl', 0.018160399137911568),\n", + " ('Weight', 0.18856818756556581),\n", + " ('Model Year', 0.11232160665841945),\n", + " ('Horsepower', 0.12451093139694776),\n", + " ('Displacement', 0.2890171891922707),\n", + " ('Cylinders', 0.21807596492306472),\n", + " ('Acceleration', 0.015337766998897846)]" + ] + }, + "execution_count": 36, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "extra_attrs = [\"acc_on_power\", \"acc_on_cyl\"]\n", + "numerics = ['float64', 'int64']\n", + "num_attrs = list(data.select_dtypes(include=numerics))\n", + "\n", + "attrs = num_attrs + extra_attrs\n", + "sorted(zip(attrs, feature_importances), reverse=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Evaluating the model on Test Data" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [], + "source": [ + "# capturing the best configuration\n", + "final_model = grid_search.best_estimator_\n", + "\n", + "# segregating the target variable from test set\n", + "X_test = strat_test_set.drop(\"MPG\", axis=1)\n", + "y_test = strat_test_set[\"MPG\"].copy()\n", + "# preprocessing the test data origin column\n", + "X_test_preprocessed = preprocess_origin_cols(X_test)\n", + "\n", + "# preparing the data with final transformation\n", + "X_test_prepared = pipeline_transformer(X_test_preprocessed)\n", + "\n", + "##making final predictions\n", + "final_predictions = final_model.predict(X_test_prepared)\n", + "final_mse = mean_squared_error(y_test, final_predictions)\n", + "final_rmse = np.sqrt(final_mse)" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "2.8958576977392307" + ] + }, + "execution_count": 38, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "final_rmse" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Creating a function to cover this entire flow" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [], + "source": [ + "def predict_mpg(config, model):\n", + " \n", + " if type(config) == dict:\n", + " df = pd.DataFrame(config)\n", + " else:\n", + " df = config\n", + " \n", + " preproc_df = preprocess_origin_cols(df)\n", + " prepared_df = pipeline_transformer(preproc_df)\n", + " y_pred = model.predict(prepared_df)\n", + " return y_pred\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([33.34666667, 18.28 , 21.61333333])" + ] + }, + "execution_count": 40, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "##checking it on a random sample\n", + "vehicle_config = {\n", + " 'Cylinders': [4, 6, 8],\n", + " 'Displacement': [155.0, 160.0, 165.5],\n", + " 'Horsepower': [93.0, 130.0, 98.0],\n", + " 'Weight': [2500.0, 3150.0, 2600.0],\n", + " 'Acceleration': [15.0, 14.0, 16.0],\n", + " 'Model Year': [81, 80, 78],\n", + " 'Origin': [3, 2, 1]\n", + "}\n", + "\n", + "predict_mpg(vehicle_config, final_model)" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [], + "source": [ + "import pickle" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": {}, + "outputs": [], + "source": [ + "##saving the model\n", + "with open(\"model.bin\", 'wb') as f_out:\n", + " pickle.dump(final_model, f_out)\n", + " f_out.close()" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([33.34666667, 18.28 , 21.61333333])" + ] + }, + "execution_count": 43, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "##loading the model from the saved file\n", + "with open('model.bin', 'rb') as f_in:\n", + " model = pickle.load(f_in)\n", + "\n", + "predict_mpg(vehicle_config, model)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "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": "3.6.8" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/intern-basics/MPG_Prediction_Model/Readme.md b/intern-basics/MPG_Prediction_Model/Readme.md new file mode 100644 index 0000000..6422e34 --- /dev/null +++ b/intern-basics/MPG_Prediction_Model/Readme.md @@ -0,0 +1,19 @@ +# MPG Prediction Model + +This prediction model shows the different factors that affect vehicle mileage. By considering those factors we can predict the miles per Gallon of a vehicle. + +To make a prediction model first we have to collect the data. This data set is found in the [UCI Machine Learning Repository](http://archive.ics.uci.edu/ml/datasets/Auto+MPG). +The attributes given in the dataset are +1. mpg: continuous +2. cylinders: multi-valued discrete +3. displacement: continuous +4. horsepower: continuous +5. weight: continuous +6. acceleration: continuous +7. model year: multi-valued discrete +8. origin: multi-valued discrete +9. car name: string (unique for each instance) + +By considering all other attributes we have to predict the mpg value. + +In the notebook, you can see that we have compared different models and pick the best model as our prediction model and store the model values in the model.bin folder. diff --git a/intern-basics/MPG_Prediction_Model/auto-mpg.data b/intern-basics/MPG_Prediction_Model/auto-mpg.data new file mode 100644 index 0000000..33404b0 --- /dev/null +++ b/intern-basics/MPG_Prediction_Model/auto-mpg.data @@ -0,0 +1,398 @@ +18.0 8 307.0 130.0 3504. 12.0 70 1 "chevrolet chevelle malibu" +15.0 8 350.0 165.0 3693. 11.5 70 1 "buick skylark 320" +18.0 8 318.0 150.0 3436. 11.0 70 1 "plymouth satellite" +16.0 8 304.0 150.0 3433. 12.0 70 1 "amc rebel sst" +17.0 8 302.0 140.0 3449. 10.5 70 1 "ford torino" +15.0 8 429.0 198.0 4341. 10.0 70 1 "ford galaxie 500" +14.0 8 454.0 220.0 4354. 9.0 70 1 "chevrolet impala" +14.0 8 440.0 215.0 4312. 8.5 70 1 "plymouth fury iii" +14.0 8 455.0 225.0 4425. 10.0 70 1 "pontiac catalina" +15.0 8 390.0 190.0 3850. 8.5 70 1 "amc ambassador dpl" +15.0 8 383.0 170.0 3563. 10.0 70 1 "dodge challenger se" +14.0 8 340.0 160.0 3609. 8.0 70 1 "plymouth 'cuda 340" +15.0 8 400.0 150.0 3761. 9.5 70 1 "chevrolet monte carlo" +14.0 8 455.0 225.0 3086. 10.0 70 1 "buick estate wagon (sw)" +24.0 4 113.0 95.00 2372. 15.0 70 3 "toyota corona mark ii" +22.0 6 198.0 95.00 2833. 15.5 70 1 "plymouth duster" +18.0 6 199.0 97.00 2774. 15.5 70 1 "amc hornet" +21.0 6 200.0 85.00 2587. 16.0 70 1 "ford maverick" +27.0 4 97.00 88.00 2130. 14.5 70 3 "datsun pl510" +26.0 4 97.00 46.00 1835. 20.5 70 2 "volkswagen 1131 deluxe sedan" +25.0 4 110.0 87.00 2672. 17.5 70 2 "peugeot 504" +24.0 4 107.0 90.00 2430. 14.5 70 2 "audi 100 ls" +25.0 4 104.0 95.00 2375. 17.5 70 2 "saab 99e" +26.0 4 121.0 113.0 2234. 12.5 70 2 "bmw 2002" +21.0 6 199.0 90.00 2648. 15.0 70 1 "amc gremlin" +10.0 8 360.0 215.0 4615. 14.0 70 1 "ford f250" +10.0 8 307.0 200.0 4376. 15.0 70 1 "chevy c20" +11.0 8 318.0 210.0 4382. 13.5 70 1 "dodge d200" +9.0 8 304.0 193.0 4732. 18.5 70 1 "hi 1200d" +27.0 4 97.00 88.00 2130. 14.5 71 3 "datsun pl510" +28.0 4 140.0 90.00 2264. 15.5 71 1 "chevrolet vega 2300" +25.0 4 113.0 95.00 2228. 14.0 71 3 "toyota corona" +25.0 4 98.00 ? 2046. 19.0 71 1 "ford pinto" +19.0 6 232.0 100.0 2634. 13.0 71 1 "amc gremlin" +16.0 6 225.0 105.0 3439. 15.5 71 1 "plymouth satellite custom" +17.0 6 250.0 100.0 3329. 15.5 71 1 "chevrolet chevelle malibu" +19.0 6 250.0 88.00 3302. 15.5 71 1 "ford torino 500" +18.0 6 232.0 100.0 3288. 15.5 71 1 "amc matador" +14.0 8 350.0 165.0 4209. 12.0 71 1 "chevrolet impala" +14.0 8 400.0 175.0 4464. 11.5 71 1 "pontiac catalina brougham" +14.0 8 351.0 153.0 4154. 13.5 71 1 "ford galaxie 500" +14.0 8 318.0 150.0 4096. 13.0 71 1 "plymouth fury iii" +12.0 8 383.0 180.0 4955. 11.5 71 1 "dodge monaco (sw)" +13.0 8 400.0 170.0 4746. 12.0 71 1 "ford country squire (sw)" +13.0 8 400.0 175.0 5140. 12.0 71 1 "pontiac safari (sw)" +18.0 6 258.0 110.0 2962. 13.5 71 1 "amc hornet sportabout (sw)" +22.0 4 140.0 72.00 2408. 19.0 71 1 "chevrolet vega (sw)" +19.0 6 250.0 100.0 3282. 15.0 71 1 "pontiac firebird" +18.0 6 250.0 88.00 3139. 14.5 71 1 "ford mustang" +23.0 4 122.0 86.00 2220. 14.0 71 1 "mercury capri 2000" +28.0 4 116.0 90.00 2123. 14.0 71 2 "opel 1900" +30.0 4 79.00 70.00 2074. 19.5 71 2 "peugeot 304" +30.0 4 88.00 76.00 2065. 14.5 71 2 "fiat 124b" +31.0 4 71.00 65.00 1773. 19.0 71 3 "toyota corolla 1200" +35.0 4 72.00 69.00 1613. 18.0 71 3 "datsun 1200" +27.0 4 97.00 60.00 1834. 19.0 71 2 "volkswagen model 111" +26.0 4 91.00 70.00 1955. 20.5 71 1 "plymouth cricket" +24.0 4 113.0 95.00 2278. 15.5 72 3 "toyota corona hardtop" +25.0 4 97.50 80.00 2126. 17.0 72 1 "dodge colt hardtop" +23.0 4 97.00 54.00 2254. 23.5 72 2 "volkswagen type 3" +20.0 4 140.0 90.00 2408. 19.5 72 1 "chevrolet vega" +21.0 4 122.0 86.00 2226. 16.5 72 1 "ford pinto runabout" +13.0 8 350.0 165.0 4274. 12.0 72 1 "chevrolet impala" +14.0 8 400.0 175.0 4385. 12.0 72 1 "pontiac catalina" +15.0 8 318.0 150.0 4135. 13.5 72 1 "plymouth fury iii" +14.0 8 351.0 153.0 4129. 13.0 72 1 "ford galaxie 500" +17.0 8 304.0 150.0 3672. 11.5 72 1 "amc ambassador sst" +11.0 8 429.0 208.0 4633. 11.0 72 1 "mercury marquis" +13.0 8 350.0 155.0 4502. 13.5 72 1 "buick lesabre custom" +12.0 8 350.0 160.0 4456. 13.5 72 1 "oldsmobile delta 88 royale" +13.0 8 400.0 190.0 4422. 12.5 72 1 "chrysler newport royal" +19.0 3 70.00 97.00 2330. 13.5 72 3 "mazda rx2 coupe" +15.0 8 304.0 150.0 3892. 12.5 72 1 "amc matador (sw)" +13.0 8 307.0 130.0 4098. 14.0 72 1 "chevrolet chevelle concours (sw)" +13.0 8 302.0 140.0 4294. 16.0 72 1 "ford gran torino (sw)" +14.0 8 318.0 150.0 4077. 14.0 72 1 "plymouth satellite custom (sw)" +18.0 4 121.0 112.0 2933. 14.5 72 2 "volvo 145e (sw)" +22.0 4 121.0 76.00 2511. 18.0 72 2 "volkswagen 411 (sw)" +21.0 4 120.0 87.00 2979. 19.5 72 2 "peugeot 504 (sw)" +26.0 4 96.00 69.00 2189. 18.0 72 2 "renault 12 (sw)" +22.0 4 122.0 86.00 2395. 16.0 72 1 "ford pinto (sw)" +28.0 4 97.00 92.00 2288. 17.0 72 3 "datsun 510 (sw)" +23.0 4 120.0 97.00 2506. 14.5 72 3 "toyouta corona mark ii (sw)" +28.0 4 98.00 80.00 2164. 15.0 72 1 "dodge colt (sw)" +27.0 4 97.00 88.00 2100. 16.5 72 3 "toyota corolla 1600 (sw)" +13.0 8 350.0 175.0 4100. 13.0 73 1 "buick century 350" +14.0 8 304.0 150.0 3672. 11.5 73 1 "amc matador" +13.0 8 350.0 145.0 3988. 13.0 73 1 "chevrolet malibu" +14.0 8 302.0 137.0 4042. 14.5 73 1 "ford gran torino" +15.0 8 318.0 150.0 3777. 12.5 73 1 "dodge coronet custom" +12.0 8 429.0 198.0 4952. 11.5 73 1 "mercury marquis brougham" +13.0 8 400.0 150.0 4464. 12.0 73 1 "chevrolet caprice classic" +13.0 8 351.0 158.0 4363. 13.0 73 1 "ford ltd" +14.0 8 318.0 150.0 4237. 14.5 73 1 "plymouth fury gran sedan" +13.0 8 440.0 215.0 4735. 11.0 73 1 "chrysler new yorker brougham" +12.0 8 455.0 225.0 4951. 11.0 73 1 "buick electra 225 custom" +13.0 8 360.0 175.0 3821. 11.0 73 1 "amc ambassador brougham" +18.0 6 225.0 105.0 3121. 16.5 73 1 "plymouth valiant" +16.0 6 250.0 100.0 3278. 18.0 73 1 "chevrolet nova custom" +18.0 6 232.0 100.0 2945. 16.0 73 1 "amc hornet" +18.0 6 250.0 88.00 3021. 16.5 73 1 "ford maverick" +23.0 6 198.0 95.00 2904. 16.0 73 1 "plymouth duster" +26.0 4 97.00 46.00 1950. 21.0 73 2 "volkswagen super beetle" +11.0 8 400.0 150.0 4997. 14.0 73 1 "chevrolet impala" +12.0 8 400.0 167.0 4906. 12.5 73 1 "ford country" +13.0 8 360.0 170.0 4654. 13.0 73 1 "plymouth custom suburb" +12.0 8 350.0 180.0 4499. 12.5 73 1 "oldsmobile vista cruiser" +18.0 6 232.0 100.0 2789. 15.0 73 1 "amc gremlin" +20.0 4 97.00 88.00 2279. 19.0 73 3 "toyota carina" +21.0 4 140.0 72.00 2401. 19.5 73 1 "chevrolet vega" +22.0 4 108.0 94.00 2379. 16.5 73 3 "datsun 610" +18.0 3 70.00 90.00 2124. 13.5 73 3 "maxda rx3" +19.0 4 122.0 85.00 2310. 18.5 73 1 "ford pinto" +21.0 6 155.0 107.0 2472. 14.0 73 1 "mercury capri v6" +26.0 4 98.00 90.00 2265. 15.5 73 2 "fiat 124 sport coupe" +15.0 8 350.0 145.0 4082. 13.0 73 1 "chevrolet monte carlo s" +16.0 8 400.0 230.0 4278. 9.50 73 1 "pontiac grand prix" +29.0 4 68.00 49.00 1867. 19.5 73 2 "fiat 128" +24.0 4 116.0 75.00 2158. 15.5 73 2 "opel manta" +20.0 4 114.0 91.00 2582. 14.0 73 2 "audi 100ls" +19.0 4 121.0 112.0 2868. 15.5 73 2 "volvo 144ea" +15.0 8 318.0 150.0 3399. 11.0 73 1 "dodge dart custom" +24.0 4 121.0 110.0 2660. 14.0 73 2 "saab 99le" +20.0 6 156.0 122.0 2807. 13.5 73 3 "toyota mark ii" +11.0 8 350.0 180.0 3664. 11.0 73 1 "oldsmobile omega" +20.0 6 198.0 95.00 3102. 16.5 74 1 "plymouth duster" +21.0 6 200.0 ? 2875. 17.0 74 1 "ford maverick" +19.0 6 232.0 100.0 2901. 16.0 74 1 "amc hornet" +15.0 6 250.0 100.0 3336. 17.0 74 1 "chevrolet nova" +31.0 4 79.00 67.00 1950. 19.0 74 3 "datsun b210" +26.0 4 122.0 80.00 2451. 16.5 74 1 "ford pinto" +32.0 4 71.00 65.00 1836. 21.0 74 3 "toyota corolla 1200" +25.0 4 140.0 75.00 2542. 17.0 74 1 "chevrolet vega" +16.0 6 250.0 100.0 3781. 17.0 74 1 "chevrolet chevelle malibu classic" +16.0 6 258.0 110.0 3632. 18.0 74 1 "amc matador" +18.0 6 225.0 105.0 3613. 16.5 74 1 "plymouth satellite sebring" +16.0 8 302.0 140.0 4141. 14.0 74 1 "ford gran torino" +13.0 8 350.0 150.0 4699. 14.5 74 1 "buick century luxus (sw)" +14.0 8 318.0 150.0 4457. 13.5 74 1 "dodge coronet custom (sw)" +14.0 8 302.0 140.0 4638. 16.0 74 1 "ford gran torino (sw)" +14.0 8 304.0 150.0 4257. 15.5 74 1 "amc matador (sw)" +29.0 4 98.00 83.00 2219. 16.5 74 2 "audi fox" +26.0 4 79.00 67.00 1963. 15.5 74 2 "volkswagen dasher" +26.0 4 97.00 78.00 2300. 14.5 74 2 "opel manta" +31.0 4 76.00 52.00 1649. 16.5 74 3 "toyota corona" +32.0 4 83.00 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3039. 15.0 75 1 "buick skyhawk" +20.0 8 262.0 110.0 3221. 13.5 75 1 "chevrolet monza 2+2" +13.0 8 302.0 129.0 3169. 12.0 75 1 "ford mustang ii" +29.0 4 97.00 75.00 2171. 16.0 75 3 "toyota corolla" +23.0 4 140.0 83.00 2639. 17.0 75 1 "ford pinto" +20.0 6 232.0 100.0 2914. 16.0 75 1 "amc gremlin" +23.0 4 140.0 78.00 2592. 18.5 75 1 "pontiac astro" +24.0 4 134.0 96.00 2702. 13.5 75 3 "toyota corona" +25.0 4 90.00 71.00 2223. 16.5 75 2 "volkswagen dasher" +24.0 4 119.0 97.00 2545. 17.0 75 3 "datsun 710" +18.0 6 171.0 97.00 2984. 14.5 75 1 "ford pinto" +29.0 4 90.00 70.00 1937. 14.0 75 2 "volkswagen rabbit" +19.0 6 232.0 90.00 3211. 17.0 75 1 "amc pacer" +23.0 4 115.0 95.00 2694. 15.0 75 2 "audi 100ls" +23.0 4 120.0 88.00 2957. 17.0 75 2 "peugeot 504" +22.0 4 121.0 98.00 2945. 14.5 75 2 "volvo 244dl" +25.0 4 121.0 115.0 2671. 13.5 75 2 "saab 99le" +33.0 4 91.00 53.00 1795. 17.5 75 3 "honda civic cvcc" +28.0 4 107.0 86.00 2464. 15.5 76 2 "fiat 131" +25.0 4 116.0 81.00 2220. 16.9 76 2 "opel 1900" +25.0 4 140.0 92.00 2572. 14.9 76 1 "capri ii" +26.0 4 98.00 79.00 2255. 17.7 76 1 "dodge colt" +27.0 4 101.0 83.00 2202. 15.3 76 2 "renault 12tl" +17.5 8 305.0 140.0 4215. 13.0 76 1 "chevrolet chevelle malibu classic" +16.0 8 318.0 150.0 4190. 13.0 76 1 "dodge coronet brougham" +15.5 8 304.0 120.0 3962. 13.9 76 1 "amc matador" +14.5 8 351.0 152.0 4215. 12.8 76 1 "ford gran torino" +22.0 6 225.0 100.0 3233. 15.4 76 1 "plymouth valiant" +22.0 6 250.0 105.0 3353. 14.5 76 1 "chevrolet nova" +24.0 6 200.0 81.00 3012. 17.6 76 1 "ford maverick" +22.5 6 232.0 90.00 3085. 17.6 76 1 "amc hornet" +29.0 4 85.00 52.00 2035. 22.2 76 1 "chevrolet chevette" +24.5 4 98.00 60.00 2164. 22.1 76 1 "chevrolet woody" +29.0 4 90.00 70.00 1937. 14.2 76 2 "vw rabbit" +33.0 4 91.00 53.00 1795. 17.4 76 3 "honda civic" +20.0 6 225.0 100.0 3651. 17.7 76 1 "dodge aspen se" +18.0 6 250.0 78.00 3574. 21.0 76 1 "ford granada ghia" +18.5 6 250.0 110.0 3645. 16.2 76 1 "pontiac ventura sj" +17.5 6 258.0 95.00 3193. 17.8 76 1 "amc pacer d/l" +29.5 4 97.00 71.00 1825. 12.2 76 2 "volkswagen rabbit" +32.0 4 85.00 70.00 1990. 17.0 76 3 "datsun b-210" +28.0 4 97.00 75.00 2155. 16.4 76 3 "toyota corolla" +26.5 4 140.0 72.00 2565. 13.6 76 1 "ford pinto" +20.0 4 130.0 102.0 3150. 15.7 76 2 "volvo 245" +13.0 8 318.0 150.0 3940. 13.2 76 1 "plymouth volare premier v8" +19.0 4 120.0 88.00 3270. 21.9 76 2 "peugeot 504" +19.0 6 156.0 108.0 2930. 15.5 76 3 "toyota mark ii" +16.5 6 168.0 120.0 3820. 16.7 76 2 "mercedes-benz 280s" +16.5 8 350.0 180.0 4380. 12.1 76 1 "cadillac seville" +13.0 8 350.0 145.0 4055. 12.0 76 1 "chevy c10" +13.0 8 302.0 130.0 3870. 15.0 76 1 "ford f108" +13.0 8 318.0 150.0 3755. 14.0 76 1 "dodge d100" +31.5 4 98.00 68.00 2045. 18.5 77 3 "honda accord cvcc" +30.0 4 111.0 80.00 2155. 14.8 77 1 "buick opel isuzu deluxe" +36.0 4 79.00 58.00 1825. 18.6 77 2 "renault 5 gtl" +25.5 4 122.0 96.00 2300. 15.5 77 1 "plymouth arrow gs" +33.5 4 85.00 70.00 1945. 16.8 77 3 "datsun f-10 hatchback" +17.5 8 305.0 145.0 3880. 12.5 77 1 "chevrolet caprice classic" +17.0 8 260.0 110.0 4060. 19.0 77 1 "oldsmobile cutlass supreme" +15.5 8 318.0 145.0 4140. 13.7 77 1 "dodge monaco brougham" +15.0 8 302.0 130.0 4295. 14.9 77 1 "mercury cougar brougham" +17.5 6 250.0 110.0 3520. 16.4 77 1 "chevrolet concours" +20.5 6 231.0 105.0 3425. 16.9 77 1 "buick skylark" +19.0 6 225.0 100.0 3630. 17.7 77 1 "plymouth volare custom" +18.5 6 250.0 98.00 3525. 19.0 77 1 "ford granada" +16.0 8 400.0 180.0 4220. 11.1 77 1 "pontiac grand prix lj" +15.5 8 350.0 170.0 4165. 11.4 77 1 "chevrolet monte carlo landau" +15.5 8 400.0 190.0 4325. 12.2 77 1 "chrysler cordoba" +16.0 8 351.0 149.0 4335. 14.5 77 1 "ford thunderbird" +29.0 4 97.00 78.00 1940. 14.5 77 2 "volkswagen rabbit custom" +24.5 4 151.0 88.00 2740. 16.0 77 1 "pontiac sunbird coupe" +26.0 4 97.00 75.00 2265. 18.2 77 3 "toyota corolla liftback" +25.5 4 140.0 89.00 2755. 15.8 77 1 "ford mustang ii 2+2" +30.5 4 98.00 63.00 2051. 17.0 77 1 "chevrolet chevette" +33.5 4 98.00 83.00 2075. 15.9 77 1 "dodge colt m/m" +30.0 4 97.00 67.00 1985. 16.4 77 3 "subaru dl" +30.5 4 97.00 78.00 2190. 14.1 77 2 "volkswagen dasher" +22.0 6 146.0 97.00 2815. 14.5 77 3 "datsun 810" +21.5 4 121.0 110.0 2600. 12.8 77 2 "bmw 320i" +21.5 3 80.00 110.0 2720. 13.5 77 3 "mazda rx-4" +43.1 4 90.00 48.00 1985. 21.5 78 2 "volkswagen rabbit custom diesel" +36.1 4 98.00 66.00 1800. 14.4 78 1 "ford fiesta" +32.8 4 78.00 52.00 1985. 19.4 78 3 "mazda glc deluxe" +39.4 4 85.00 70.00 2070. 18.6 78 3 "datsun b210 gx" +36.1 4 91.00 60.00 1800. 16.4 78 3 "honda civic cvcc" +19.9 8 260.0 110.0 3365. 15.5 78 1 "oldsmobile cutlass salon brougham" +19.4 8 318.0 140.0 3735. 13.2 78 1 "dodge diplomat" +20.2 8 302.0 139.0 3570. 12.8 78 1 "mercury monarch ghia" +19.2 6 231.0 105.0 3535. 19.2 78 1 "pontiac phoenix lj" +20.5 6 200.0 95.00 3155. 18.2 78 1 "chevrolet malibu" +20.2 6 200.0 85.00 2965. 15.8 78 1 "ford fairmont (auto)" +25.1 4 140.0 88.00 2720. 15.4 78 1 "ford fairmont (man)" +20.5 6 225.0 100.0 3430. 17.2 78 1 "plymouth volare" +19.4 6 232.0 90.00 3210. 17.2 78 1 "amc concord" +20.6 6 231.0 105.0 3380. 15.8 78 1 "buick century special" +20.8 6 200.0 85.00 3070. 16.7 78 1 "mercury zephyr" +18.6 6 225.0 110.0 3620. 18.7 78 1 "dodge aspen" +18.1 6 258.0 120.0 3410. 15.1 78 1 "amc concord d/l" +19.2 8 305.0 145.0 3425. 13.2 78 1 "chevrolet monte carlo landau" +17.7 6 231.0 165.0 3445. 13.4 78 1 "buick regal sport coupe (turbo)" +18.1 8 302.0 139.0 3205. 11.2 78 1 "ford futura" +17.5 8 318.0 140.0 4080. 13.7 78 1 "dodge magnum xe" +30.0 4 98.00 68.00 2155. 16.5 78 1 "chevrolet chevette" +27.5 4 134.0 95.00 2560. 14.2 78 3 "toyota corona" +27.2 4 119.0 97.00 2300. 14.7 78 3 "datsun 510" +30.9 4 105.0 75.00 2230. 14.5 78 1 "dodge omni" +21.1 4 134.0 95.00 2515. 14.8 78 3 "toyota celica gt liftback" +23.2 4 156.0 105.0 2745. 16.7 78 1 "plymouth sapporo" +23.8 4 151.0 85.00 2855. 17.6 78 1 "oldsmobile starfire sx" +23.9 4 119.0 97.00 2405. 14.9 78 3 "datsun 200-sx" +20.3 5 131.0 103.0 2830. 15.9 78 2 "audi 5000" +17.0 6 163.0 125.0 3140. 13.6 78 2 "volvo 264gl" +21.6 4 121.0 115.0 2795. 15.7 78 2 "saab 99gle" +16.2 6 163.0 133.0 3410. 15.8 78 2 "peugeot 604sl" +31.5 4 89.00 71.00 1990. 14.9 78 2 "volkswagen scirocco" +29.5 4 98.00 68.00 2135. 16.6 78 3 "honda accord lx" +21.5 6 231.0 115.0 3245. 15.4 79 1 "pontiac lemans v6" +19.8 6 200.0 85.00 2990. 18.2 79 1 "mercury zephyr 6" +22.3 4 140.0 88.00 2890. 17.3 79 1 "ford fairmont 4" +20.2 6 232.0 90.00 3265. 18.2 79 1 "amc concord dl 6" +20.6 6 225.0 110.0 3360. 16.6 79 1 "dodge aspen 6" +17.0 8 305.0 130.0 3840. 15.4 79 1 "chevrolet caprice classic" +17.6 8 302.0 129.0 3725. 13.4 79 1 "ford ltd landau" +16.5 8 351.0 138.0 3955. 13.2 79 1 "mercury grand marquis" +18.2 8 318.0 135.0 3830. 15.2 79 1 "dodge st. regis" +16.9 8 350.0 155.0 4360. 14.9 79 1 "buick estate wagon (sw)" +15.5 8 351.0 142.0 4054. 14.3 79 1 "ford country squire (sw)" +19.2 8 267.0 125.0 3605. 15.0 79 1 "chevrolet malibu classic (sw)" +18.5 8 360.0 150.0 3940. 13.0 79 1 "chrysler lebaron town @ country (sw)" +31.9 4 89.00 71.00 1925. 14.0 79 2 "vw rabbit custom" +34.1 4 86.00 65.00 1975. 15.2 79 3 "maxda glc deluxe" +35.7 4 98.00 80.00 1915. 14.4 79 1 "dodge colt hatchback custom" +27.4 4 121.0 80.00 2670. 15.0 79 1 "amc spirit dl" +25.4 5 183.0 77.00 3530. 20.1 79 2 "mercedes benz 300d" +23.0 8 350.0 125.0 3900. 17.4 79 1 "cadillac eldorado" +27.2 4 141.0 71.00 3190. 24.8 79 2 "peugeot 504" +23.9 8 260.0 90.00 3420. 22.2 79 1 "oldsmobile cutlass salon brougham" +34.2 4 105.0 70.00 2200. 13.2 79 1 "plymouth horizon" +34.5 4 105.0 70.00 2150. 14.9 79 1 "plymouth horizon tc3" +31.8 4 85.00 65.00 2020. 19.2 79 3 "datsun 210" +37.3 4 91.00 69.00 2130. 14.7 79 2 "fiat strada custom" +28.4 4 151.0 90.00 2670. 16.0 79 1 "buick skylark limited" +28.8 6 173.0 115.0 2595. 11.3 79 1 "chevrolet citation" +26.8 6 173.0 115.0 2700. 12.9 79 1 "oldsmobile omega brougham" +33.5 4 151.0 90.00 2556. 13.2 79 1 "pontiac phoenix" +41.5 4 98.00 76.00 2144. 14.7 80 2 "vw rabbit" +38.1 4 89.00 60.00 1968. 18.8 80 3 "toyota corolla tercel" +32.1 4 98.00 70.00 2120. 15.5 80 1 "chevrolet chevette" +37.2 4 86.00 65.00 2019. 16.4 80 3 "datsun 310" +28.0 4 151.0 90.00 2678. 16.5 80 1 "chevrolet citation" +26.4 4 140.0 88.00 2870. 18.1 80 1 "ford fairmont" +24.3 4 151.0 90.00 3003. 20.1 80 1 "amc concord" +19.1 6 225.0 90.00 3381. 18.7 80 1 "dodge aspen" +34.3 4 97.00 78.00 2188. 15.8 80 2 "audi 4000" +29.8 4 134.0 90.00 2711. 15.5 80 3 "toyota corona liftback" +31.3 4 120.0 75.00 2542. 17.5 80 3 "mazda 626" +37.0 4 119.0 92.00 2434. 15.0 80 3 "datsun 510 hatchback" +32.2 4 108.0 75.00 2265. 15.2 80 3 "toyota corolla" +46.6 4 86.00 65.00 2110. 17.9 80 3 "mazda glc" +27.9 4 156.0 105.0 2800. 14.4 80 1 "dodge colt" +40.8 4 85.00 65.00 2110. 19.2 80 3 "datsun 210" +44.3 4 90.00 48.00 2085. 21.7 80 2 "vw rabbit c (diesel)" +43.4 4 90.00 48.00 2335. 23.7 80 2 "vw dasher (diesel)" +36.4 5 121.0 67.00 2950. 19.9 80 2 "audi 5000s (diesel)" +30.0 4 146.0 67.00 3250. 21.8 80 2 "mercedes-benz 240d" +44.6 4 91.00 67.00 1850. 13.8 80 3 "honda civic 1500 gl" +40.9 4 85.00 ? 1835. 17.3 80 2 "renault lecar deluxe" +33.8 4 97.00 67.00 2145. 18.0 80 3 "subaru dl" +29.8 4 89.00 62.00 1845. 15.3 80 2 "vokswagen rabbit" +32.7 6 168.0 132.0 2910. 11.4 80 3 "datsun 280-zx" +23.7 3 70.00 100.0 2420. 12.5 80 3 "mazda rx-7 gs" +35.0 4 122.0 88.00 2500. 15.1 80 2 "triumph tr7 coupe" +23.6 4 140.0 ? 2905. 14.3 80 1 "ford mustang cobra" +32.4 4 107.0 72.00 2290. 17.0 80 3 "honda accord" +27.2 4 135.0 84.00 2490. 15.7 81 1 "plymouth reliant" +26.6 4 151.0 84.00 2635. 16.4 81 1 "buick skylark" +25.8 4 156.0 92.00 2620. 14.4 81 1 "dodge aries wagon (sw)" +23.5 6 173.0 110.0 2725. 12.6 81 1 "chevrolet citation" +30.0 4 135.0 84.00 2385. 12.9 81 1 "plymouth reliant" +39.1 4 79.00 58.00 1755. 16.9 81 3 "toyota starlet" +39.0 4 86.00 64.00 1875. 16.4 81 1 "plymouth champ" +35.1 4 81.00 60.00 1760. 16.1 81 3 "honda civic 1300" +32.3 4 97.00 67.00 2065. 17.8 81 3 "subaru" +37.0 4 85.00 65.00 1975. 19.4 81 3 "datsun 210 mpg" +37.7 4 89.00 62.00 2050. 17.3 81 3 "toyota tercel" +34.1 4 91.00 68.00 1985. 16.0 81 3 "mazda glc 4" +34.7 4 105.0 63.00 2215. 14.9 81 1 "plymouth horizon 4" +34.4 4 98.00 65.00 2045. 16.2 81 1 "ford escort 4w" +29.9 4 98.00 65.00 2380. 20.7 81 1 "ford escort 2h" +33.0 4 105.0 74.00 2190. 14.2 81 2 "volkswagen jetta" +34.5 4 100.0 ? 2320. 15.8 81 2 "renault 18i" +33.7 4 107.0 75.00 2210. 14.4 81 3 "honda prelude" +32.4 4 108.0 75.00 2350. 16.8 81 3 "toyota corolla" +32.9 4 119.0 100.0 2615. 14.8 81 3 "datsun 200sx" +31.6 4 120.0 74.00 2635. 18.3 81 3 "mazda 626" +28.1 4 141.0 80.00 3230. 20.4 81 2 "peugeot 505s turbo diesel" +30.7 6 145.0 76.00 3160. 19.6 81 2 "volvo diesel" +25.4 6 168.0 116.0 2900. 12.6 81 3 "toyota cressida" +24.2 6 146.0 120.0 2930. 13.8 81 3 "datsun 810 maxima" +22.4 6 231.0 110.0 3415. 15.8 81 1 "buick century" +26.6 8 350.0 105.0 3725. 19.0 81 1 "oldsmobile cutlass ls" +20.2 6 200.0 88.00 3060. 17.1 81 1 "ford granada gl" +17.6 6 225.0 85.00 3465. 16.6 81 1 "chrysler lebaron salon" +28.0 4 112.0 88.00 2605. 19.6 82 1 "chevrolet cavalier" +27.0 4 112.0 88.00 2640. 18.6 82 1 "chevrolet cavalier wagon" +34.0 4 112.0 88.00 2395. 18.0 82 1 "chevrolet cavalier 2-door" +31.0 4 112.0 85.00 2575. 16.2 82 1 "pontiac j2000 se hatchback" +29.0 4 135.0 84.00 2525. 16.0 82 1 "dodge aries se" +27.0 4 151.0 90.00 2735. 18.0 82 1 "pontiac phoenix" +24.0 4 140.0 92.00 2865. 16.4 82 1 "ford fairmont futura" +23.0 4 151.0 ? 3035. 20.5 82 1 "amc concord dl" +36.0 4 105.0 74.00 1980. 15.3 82 2 "volkswagen rabbit l" +37.0 4 91.00 68.00 2025. 18.2 82 3 "mazda glc custom l" +31.0 4 91.00 68.00 1970. 17.6 82 3 "mazda glc custom" +38.0 4 105.0 63.00 2125. 14.7 82 1 "plymouth horizon miser" +36.0 4 98.00 70.00 2125. 17.3 82 1 "mercury lynx l" +36.0 4 120.0 88.00 2160. 14.5 82 3 "nissan stanza xe" +36.0 4 107.0 75.00 2205. 14.5 82 3 "honda accord" +34.0 4 108.0 70.00 2245 16.9 82 3 "toyota corolla" +38.0 4 91.00 67.00 1965. 15.0 82 3 "honda civic" +32.0 4 91.00 67.00 1965. 15.7 82 3 "honda civic (auto)" +38.0 4 91.00 67.00 1995. 16.2 82 3 "datsun 310 gx" +25.0 6 181.0 110.0 2945. 16.4 82 1 "buick century limited" +38.0 6 262.0 85.00 3015. 17.0 82 1 "oldsmobile cutlass ciera (diesel)" +26.0 4 156.0 92.00 2585. 14.5 82 1 "chrysler lebaron medallion" +22.0 6 232.0 112.0 2835 14.7 82 1 "ford granada l" +32.0 4 144.0 96.00 2665. 13.9 82 3 "toyota celica gt" +36.0 4 135.0 84.00 2370. 13.0 82 1 "dodge charger 2.2" +27.0 4 151.0 90.00 2950. 17.3 82 1 "chevrolet camaro" +27.0 4 140.0 86.00 2790. 15.6 82 1 "ford mustang gl" +44.0 4 97.00 52.00 2130. 24.6 82 2 "vw pickup" +32.0 4 135.0 84.00 2295. 11.6 82 1 "dodge rampage" +28.0 4 120.0 79.00 2625. 18.6 82 1 "ford ranger" +31.0 4 119.0 82.00 2720. 19.4 82 1 "chevy s-10" diff --git a/intern-basics/MPG_Prediction_Model/model.bin b/intern-basics/MPG_Prediction_Model/model.bin new file mode 100644 index 0000000..b6611bd Binary files /dev/null and b/intern-basics/MPG_Prediction_Model/model.bin differ