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28.session.tar
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28.session.tar
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[texteditor]
font/italic = False
font/family = ['Monospace', 'DejaVu Sans Mono', 'Consolas', 'Bitstream Vera Sans Mono', 'Andale Mono', 'Liberation Mono', 'Courier New', 'Courier', 'monospace', 'Fixed', 'Terminal']
font/bold = False
font/size = 10
[dicteditor]
font/italic = False
font/family = ['Monospace', 'DejaVu Sans Mono', 'Consolas', 'Bitstream Vera Sans Mono', 'Andale Mono', 'Liberation Mono', 'Courier New', 'Courier', 'monospace', 'Fixed', 'Terminal']
font/bold = False
font/size = 9
[explorer]
wrap = True
show_all = True
enable = True
name_filters = ['*.py', '*.pyw', '*.ipy', '*.pyx', '*.pxd', '*.pxi', '*.c', '*.h', '*.cc', '*.cpp', '*.cxx', '*.h', '*.hh', '*.hpp', '*.hxx', '*.cl', '*.f', '*.for', '*.f77', '*.f90', '*.f95', '*.f2k', '*.pro', '*.m', '*.jl', '*.yaml', '*.yml', '*.patch', '*.diff', '*.rej', '*.bat', '*.cmd', '*.txt', '*.txt', '*.rst', '*.po', '*.pot', '*.nsi', '*.nsh', '*.css', '*.htm', '*.html', '*.xml', '*.js', '*.json', '*.ipynb', '*.enaml', '*.properties', '*.session', '*.ini', '*.inf', '*.reg', '*.cfg', '*.desktop', '*.pkl', '*.png', '*.gif', '*.npz', '*.npy', '*.mat', '*.h5', '*.tif', '*.pickle', '*.spydata', '*.csv', '*.json', '*.jpg', '*.txt', '*.png', '*.ico', '*.svg', 'README', 'INSTALL', 'LICENSE', 'CHANGELOG']
show_hidden = True
show_icontext = False
font/family = None
first_time = True
[find_in_files]
exclude = ['\\.pyc$|\\.pyo$|\\.orig$|\\.hg|\\.svn|\\bbuild\\b', '\\.pyc$|\\.pyo$|\\.orig$|\\.hg|\\.svn']
search_text_regexp = True
search_text_samples = ['(^|#)[ ]*(TODO|FIXME|XXX|HINT|TIP|@todo)([^#]*)']
in_python_path = False
enable = True
exclude_regexp = True
more_options = True
include = ['\\.py$|\\.pyw$|\\.ipy$|\\.pyx$|\\.pxd$|\\.pxi$|\\.c$|\\.h$|\\.cc$|\\.cpp$|\\.cxx$|\\.h$|\\.hh$|\\.hpp$|\\.hxx$|\\.cl$|\\.f$|\\.for$|\\.f77$|\\.f90$|\\.f95$|\\.f2k$|\\.pro$|\\.m$|\\.jl$|\\.yaml$|\\.yml$|\\.patch$|\\.diff$|\\.rej$|\\.bat$|\\.cmd$|\\.txt$|\\.txt$|\\.rst$|\\.po$|\\.pot$|\\.nsi$|\\.nsh$|\\.css$|\\.htm$|\\.html$|\\.xml$|\\.js$|\\.json$|\\.ipynb$|\\.enaml$|\\.properties$|\\.session$|\\.ini$|\\.inf$|\\.reg$|\\.cfg$|\\.desktop$|README|INSTALL', '\\.pyw?$|\\.ipy$|\\.txt$|\\.rst$', '.']
supported_encodings = ['utf-8', 'iso-8859-1', 'cp1252']
include_regexp = True
search_text = ['']
search_path = None
include_idx = None
exclude_idx = None
first_time = True
[workingdir]
editor/open/auto_set_to_basedir = False
editor/open/browse_workdir = False
editor/open/browse_scriptdir = True
working_dir_history = 20
editor/new/browse_scriptdir = False
startup/use_last_directory = True
editor/save/auto_set_to_basedir = False
editor/new/browse_workdir = True
working_dir_adjusttocontents = False
startup/use_fixed_directory = False
startup/fixed_directory = H:\TUTORIALS\Data Science & Analytics\Python
[shortcuts]
editor/conditional breakpoint = Shift+F12
editor/find next = F3
editor/code completion = Ctrl+Space
editor/close all = Ctrl+Shift+W
_/debug exit = Ctrl+Shift+F12
editor/move line up = Alt+Up
editor/unblockcomment = Ctrl+5
editor/new file = Ctrl+N
editor/find previous = Shift+F3
profiler/run profiler = F10
_/switch to inspector = Ctrl+Shift+H
editor/file list management = Ctrl+E
editor/duplicate line = Ctrl+Alt+Up
_/close pane = Shift+Ctrl+F4
editor/run selection = F9
_/switch to outline_explorer = Ctrl+Shift+O
editor/go to previous file = Ctrl+Tab
_/switch to/from layout 3 = Shift+Alt+F3
editor/re-run last script = Ctrl+F6
editor/breakpoint = F12
editor/debug with winpdb = F7
_/switch to historylog = Ctrl+Shift+L
editor/go to definition = Ctrl+G
_/maximize pane = Ctrl+Alt+Shift+M
_/switch to breakpoints = Ctrl+Shift+B
editor/blockcomment = Ctrl+4
_/set layout 1 = Ctrl+Shift+Alt+F1
console/inspect current object = Ctrl+I
_/fullscreen mode = F11
editor/inspect current object = Ctrl+I
editor/debug = Ctrl+F5
_/switch to/from layout 1 = Shift+Alt+F1
pylint/run analysis = F8
editor/previous cursor position = Ctrl+Alt+Left
editor/print = Ctrl+P
editor/show/hide project explorer = Ctrl+Alt+P
_/debug continue = Ctrl+F12
editor/copy line = Ctrl+Alt+Down
_/switch to variable_explorer = Ctrl+Shift+V
editor/toggle comment = Ctrl+1
_/switch to explorer = Ctrl+Shift+X
_/quit = Ctrl+Q
_/set layout 2 = Ctrl+Shift+Alt+F2
_/switch to project_explorer = Ctrl+Shift+P
_/set layout 3 = Ctrl+Shift+Alt+F3
editor/go to line = Ctrl+L
editor/move line down = Alt+Down
editor/go to next file = Ctrl+Shift+Tab
editor/configure = F6
editor/run = F5
editor/delete line = Ctrl+D
_/switch to ipython_console = Ctrl+Shift+I
_/switch to find_in_files = Ctrl+Shift+F
_/debug step into = Ctrl+F11
editor/find text = Ctrl+F
_/preferences = Ctrl+Alt+Shift+P
editor/save all = Ctrl+Shift+S
console/clear line = Shift+Escape
console/clear shell = Ctrl+L
_/debug step over = Ctrl+F10
editor/open file = Ctrl+O
_/switch to/from layout 2 = Shift+Alt+F2
editor/show/hide outline = Ctrl+Alt+O
editor/save file = Ctrl+S
editor/replace text = Ctrl+H
_/switch to onlinehelp = Ctrl+Shift+D
_/switch to editor = Ctrl+Shift+E
_/switch to console = Ctrl+Shift+C
editor/next cursor position = Ctrl+Alt+Right
editor/last edit location = Ctrl+Alt+Shift+Left
_/debug step return = Ctrl+Shift+F11
[color_schemes]
zenburn/string = ('#cc9393', False, False)
pydev/background = #ffffff
idle/ctrlclick = #0000ff
spyder/dark/instance = ('#be5f00', False, True)
monokai/currentline = #484848
idle/unmatched_p = #ff9999
zenburn/normal = ('#dcdccc', False, False)
spyder/matched_p = #99ff99
emacs/ctrlclick = #0000ff
spyder/background = #ffffff
monokai/ctrlclick = #0000ff
scintilla/background = #ffffff
idle/comment = ('#dd0000', False, True)
idle/background = #ffffff
idle/keyword = ('#ff7700', True, False)
spyder/dark/currentline = #2b2b43
emacs/matched_p = #009800
spyder/dark/matched_p = #009800
zenburn/unmatched_p = #bd6e76
zenburn/instance = ('#dcdccc', False, True)
idle/definition = ('#0000ff', False, False)
spyder/dark/occurence = #abab67
emacs/background = #000000
monokai/unmatched_p = #bd6e76
spyder/instance = ('#924900', False, True)
emacs/normal = ('#ffffff', False, False)
spyder/dark/number = ('#c80000', False, False)
zenburn/builtin = ('#efef8f', False, False)
spyder/dark/string = ('#11a642', False, True)
zenburn/definition = ('#efef8f', False, False)
monokai/normal = ('#ddddda', False, False)
idle/occurence = #e8f2fe
pydev/comment = ('#c0c0c0', False, False)
monokai/number = ('#ae81ff', False, False)
spyder/dark/background = #131926
emacs/occurence = #abab67
pydev/keyword = ('#0000ff', False, False)
pydev/normal = ('#000000', False, False)
pydev/instance = ('#000000', False, True)
monokai/instance = ('#ddddda', False, True)
scintilla/comment = ('#007f00', False, False)
spyder/dark/normal = ('#ffffff', False, False)
monokai/string = ('#e6db74', False, False)
idle/instance = ('#777777', True, True)
spyder/definition = ('#000000', True, False)
pydev/matched_p = #99ff99
scintilla/ctrlclick = #0000ff
spyder/occurence = #ffff99
pydev/sideareas = #efefef
monokai/matched_p = #688060
spyder/normal = ('#000000', False, False)
pydev/builtin = ('#900090', False, False)
idle/number = ('#924900', False, False)
idle/currentline = #f2e6f3
idle/matched_p = #99ff99
spyder/sideareas = #efefef
monokai/occurence = #666666
spyder/currentcell = #fdfdde
emacs/currentcell = #1c1c2d
spyder/dark/comment = ('#7f7f7f', False, False)
spyder/dark/builtin = ('#aa00aa', False, False)
names = ['Custom', 'Emacs', 'IDLE', 'Monokai', 'Pydev', 'Scintilla', 'Spyder', 'Spyder/Dark', 'Zenburn']
zenburn/sideareas = #3f3f3f
pydev/string = ('#00aa00', False, True)
zenburn/ctrlclick = #0000ff
scintilla/normal = ('#000000', False, False)
pydev/unmatched_p = #ff99992
zenburn/background = #3f3f3f
monokai/sideareas = #2a2b24
idle/string = ('#00aa00', False, False)
spyder/dark/unmatched_p = #c80000
monokai/currentcell = #3d3d3d
monokai/definition = ('#a6e22e', False, False)
spyder/currentline = #f8edf8
emacs/number = ('#800000', False, False)
zenburn/occurence = #7a738f
spyder/unmatched_p = #ff9999
spyder/comment = ('#ff0000', False, True)
emacs/definition = ('#ff8040', True, False)
scintilla/definition = ('#007f7f', True, False)
scintilla/builtin = ('#000000', False, False)
monokai/builtin = ('#ae81ff', False, False)
pydev/definition = ('#000000', True, False)
zenburn/currentcell = #2c2c2c
spyder/keyword = ('#0000ff', True, False)
spyder/string = ('#00aa00', False, False)
pydev/currentcell = #eff8fe
spyder/dark/sideareas = #282828
emacs/string = ('#00aa00', False, True)
idle/currentcell = #feefff
spyder/dark/currentcell = #31314e
zenburn/currentline = #333333
emacs/keyword = ('#3c51e8', False, False)
scintilla/string = ('#7f007f', False, False)
spyder/dark/ctrlclick = #0000ff
emacs/builtin = ('#900090', False, False)
scintilla/currentline = #e1f0d1
emacs/sideareas = #555555
idle/sideareas = #efefef
monokai/comment = ('#75715e', False, True)
scintilla/number = ('#007f7f', False, False)
scintilla/currentcell = #edfcdc
pydev/currentline = #e8f2fe
scintilla/sideareas = #efefef
zenburn/keyword = ('#dfaf8f', True, False)
emacs/instance = ('#ffffff', False, True)
zenburn/comment = ('#7f9f7f', False, True)
scintilla/keyword = ('#00007f', True, False)
spyder/dark/keyword = ('#558eff', False, False)
spyder/number = ('#800000', False, False)
monokai/background = #2a2b24
spyder/builtin = ('#900090', False, False)
emacs/currentline = #2b2b43
pydev/number = ('#800000', False, False)
spyder/ctrlclick = #0000ff
scintilla/matched_p = #99ff99
pydev/ctrlclick = #0000ff
idle/builtin = ('#900090', False, False)
scintilla/instance = ('#000000', False, True)
scintilla/occurence = #ffff99
zenburn/number = ('#8cd0d3', False, False)
monokai/keyword = ('#f92672', False, False)
emacs/unmatched_p = #c80000
scintilla/unmatched_p = #ff9999
spyder/dark/definition = ('#ffffff', True, False)
pydev/occurence = #ffff99
zenburn/matched_p = #688060
emacs/comment = ('#005100', False, False)
idle/normal = ('#000000', False, False)
custom/background = #ffffff
custom/currentline = #f7ecf8
custom/currentcell = #fdfdde
custom/occurence = #ffff99
custom/ctrlclick = #0000ff
custom/sideareas = #efefef
custom/matched_p = #99ff99
custom/unmatched_p = #ff9999
custom/normal = ('#000000', False, False)
custom/keyword = ('#0000ff', False, False)
custom/builtin = ('#900090', False, False)
custom/definition = ('#000000', True, False)
custom/comment = ('#adadad', False, True)
custom/string = ('#00aa00', False, False)
custom/number = ('#800000', False, False)
custom/instance = ('#924900', False, True)
[run]
breakpoints = {'C:\\Users\\vatsal\\.spyder2-py3\\temp.py': [], 'C:\\Users\\vatsal\\OneDrive\\Sentiment_Module_Twitter_1.1\\Improved_Complete.py': [], 'H:\\TUTORIALS\\Data Science & Analytics\\Python\\pyglatin.py': [], 'H:\\TUTORIALS\\Data Science & Analytics\\Python\\Test.py': []}
history = 20
configurations = [('C:\\Users\\vatsal\\OneDrive\\Date_Your_Data\\start.py', {'interact': False, 'workdir': 'C:\\Users\\vatsal\\OneDrive\\Date_Your_Data', 'current': True, 'workdir/enabled': True, 'systerm': False, 'args/enabled': False, 'args': '', 'python_args': '', 'show_kill_warning': True, 'python_args/enabled': False}), ('C:\\Users\\vatsal\\OneDrive\\Sentiment_Module_Twitter_1.1\\final_demo_v1.1.py', {'interact': False, 'workdir': 'C:\\Users\\vatsal\\OneDrive\\Sentiment_Module_Twitter_1.1', 'current': True, 'workdir/enabled': True, 'systerm': False, 'args/enabled': False, 'args': '', 'python_args': '', 'show_kill_warning': True, 'python_args/enabled': False}), ('C:\\Users\\vatsal\\OneDrive\\Sentiment_Module_Twitter_1.1\\evaluate_features.py', {'interact': False, 'workdir': 'C:\\Users\\vatsal\\OneDrive\\Sentiment_Module_Twitter_1.1', 'current': True, 'workdir/enabled': True, 'systerm': False, 'args/enabled': False, 'args': '', 'python_args': '', 'show_kill_warning': True, 'python_args/enabled': False}), ('C:\\Users\\vatsal\\OneDrive\\Sentiment_Module_Twitter_1.1\\Improved_Complete.py', {'interact': False, 'workdir': 'C:\\Users\\vatsal\\OneDrive\\Sentiment_Module_Twitter_1.1', 'current': True, 'workdir/enabled': True, 'systerm': False, 'args/enabled': False, 'args': '', 'python_args': '', 'show_kill_warning': True, 'python_args/enabled': False}), ('C:\\Users\\vatsal\\OneDrive\\leetcode\\Word Count.py', {'interact': False, 'workdir': 'C:\\Users\\vatsal\\OneDrive\\leetcode', 'current': True, 'workdir/enabled': True, 'systerm': False, 'args/enabled': False, 'args': '', 'python_args': '', 'show_kill_warning': True, 'python_args/enabled': False}), ('C:\\Users\\vatsal\\OneDrive\\leetcode\\Coin change.py', {'interact': False, 'workdir': 'C:\\Users\\vatsal\\OneDrive\\leetcode', 'current': True, 'workdir/enabled': True, 'systerm': False, 'args/enabled': False, 'args': '', 'python_args': '', 'show_kill_warning': True, 'python_args/enabled': False}), ('C:\\Users\\vatsal\\OneDrive\\leetcode\\Longest Substring Without Repeating Characters.py', {'interact': False, 'workdir': 'C:\\Users\\vatsal\\OneDrive\\leetcode', 'current': True, 'workdir/enabled': True, 'systerm': False, 'args/enabled': False, 'args': '', 'python_args': '', 'show_kill_warning': True, 'python_args/enabled': False}), ('C:\\Users\\vatsal\\features.py', {'interact': False, 'workdir': 'C:\\Users\\vatsal', 'current': True, 'workdir/enabled': True, 'systerm': False, 'args/enabled': False, 'args': '', 'python_args': '', 'show_kill_warning': True, 'python_args/enabled': False}), ('C:\\Users\\vatsal\\timediff.py', {'interact': False, 'workdir': 'C:\\Users\\vatsal', 'current': True, 'workdir/enabled': True, 'systerm': False, 'args/enabled': False, 'args': '', 'python_args': '', 'show_kill_warning': True, 'python_args/enabled': False}), ('C:\\Users\\vatsal\\rural_water_supply.py', {'interact': False, 'workdir': 'C:\\Users\\vatsal', 'current': True, 'workdir/enabled': True, 'systerm': False, 'args/enabled': False, 'args': '', 'python_args': '', 'show_kill_warning': True, 'python_args/enabled': False}), ('C:\\Users\\vatsal\\chunking.py', {'interact': False, 'workdir': 'C:\\Users\\vatsal', 'current': True, 'workdir/enabled': True, 'systerm': False, 'args/enabled': False, 'args': '', 'python_args': '', 'show_kill_warning': True, 'python_args/enabled': False}), ('H:\\TUTORIALS\\Data Science & Analytics\\Python\\ml1.py', {'interact': False, 'workdir': 'H:\\TUTORIALS\\Data Science & Analytics\\Python', 'current': True, 'workdir/enabled': True, 'systerm': False, 'args/enabled': False, 'args': '', 'python_args': '', 'show_kill_warning': True, 'python_args/enabled': False}), ('H:\\TUTORIALS\\Data Science & Analytics\\Python\\Test.py', {'interact': False, 'workdir': 'H:\\TUTORIALS\\Data Science & Analytics\\Python', 'current': True, 'workdir/enabled': True, 'systerm': False, 'args/enabled': False, 'args': '', 'python_args': '', 'show_kill_warning': True, 'python_args/enabled': False}), ('H:\\TUTORIALS\\Data Science & Analytics\\Python\\pyglatin.py', {'interact': False, 'workdir': 'H:\\TUTORIALS\\Data Science & Analytics\\Python', 'current': True, 'workdir/enabled': True, 'systerm': False, 'args/enabled': False, 'args': '', 'python_args': '', 'show_kill_warning': True, 'python_args/enabled': False}), ('C:\\Users\\vatsal\\.spyder2-py3\\temp.py', {'interact': False, 'workdir': 'C:\\Users\\vatsal\\.spyder2-py3', 'current': True, 'workdir/enabled': True, 'systerm': False, 'args/enabled': False, 'args': '', 'python_args': '', 'show_kill_warning': True, 'python_args/enabled': False})]
defaultconfiguration = {}
default/wdir/use_fixed_directory = False
default/interpreter/current = True
default/interpreter/systerm = False
open_on_firstrun = True
default/wdir/use_script_directory = True
open_at_least_once = False
default/interpreter/dedicated = False
default/wdir/fixed_directory = H:\TUTORIALS\Data Science & Analytics\Python
[dicteditor_header]
font/family = None
[profiler]
max_entries = 50
first_time = False
[pylint]
max_entries = 50
first_time = False
save_before = True
[breakpoints]
first_time = False
temp.py 0000666 0000000 0000000 00000002221 12644374723 010540 0 ustar 0000000 0000000 # -*- coding: utf-8 -*-
"""
Spyder Editor
This is a temporary script file.
CC coordinating conjunction
CD cardinal digit
DT determiner
EX existential there (like: "there is" ... think of it like "there exists")
FW foreign word
IN preposition/subordinating conjunction
JJ adjective 'big'
JJR adjective, comparative 'bigger'
JJS adjective, superlative 'biggest'
LS list marker 1)
MD modal could, will
NN noun, singular 'desk'
NNS noun plural 'desks'
NNP proper noun, singular 'Harrison'
NNPS proper noun, plural 'Americans'
PDT predeterminer 'all the kids'
POS possessive ending parent's
PRP personal pronoun I, he, she
PRP$ possessive pronoun my, his, hers
RB adverb very, silently,
RBR adverb, comparative better
RBS adverb, superlative best
RP particle give up
TO to go 'to' the store.
UH interjection errrrrrrrm
VB verb, base form take
VBD verb, past tense took
VBG verb, gerund/present participle taking
VBN verb, past participle taken
VBP verb, sing. present, non-3d take
VBZ verb, 3rd person sing. present takes
WDT wh-determiner which
WP wh-pronoun who, what
WP$ possessive wh-pronoun whose
WRB wh-abverb where, when
""" template.py 0000666 0000000 0000000 00000000121 12632545257 011402 0 ustar 0000000 0000000 # -*- coding: utf-8 -*-
"""
Created on %(date)s
@author: %(username)s
"""
history.py 0000666 0000000 0000000 00000075162 12664374026 011310 0 ustar 0000000 0000000 #Reading the raw data files
train_raw = pd.read_csv("trainfiles/traincsv/train.csv")
internship = pd.read_csv("trainfiles/Internship/Internship.csv")
student = pd.read_csv("trainfiles/Student/student.csv")
#Removing duplicates from the student file
student = student.drop_duplicates("Student_ID")
#Merging all data to evaluate features and train the data
merged = pd.merge(train_raw,student, on='Student_ID',how='left')
merged = pd.merge(merged,internship,on='Internship_ID',how='left')
len(merged.columns)
merged.Percentage_PG = merged.Performance_PG/merged.PG_scale
merged.Percentage_UG = merged.Performance_UG/merged.UG_Scale
merged['Percentage_PG']=merged.Percentage_PG
merged['Percentage_UG']=merged.Percentage_UG
merged = merged.drop(merged.columns[[14,15,16,17,18]], axis=1)
le = preprocessing.LabelEncoder()
le.fit_transform(merged.Expected_Stipend)
merged['Expected_Stipend'] = le.fit_transform(merged.Expected_Stipend)
merged['Expected_Stipend']
le = preprocessing.LabelEncoder()
merged['Expected_Stipend'] = le.fit_transform(merged.Expected_Stipend)
merged['Preferred_location'] = le.fit_transform(merged.Preferred_location)
merged['Institute_Category'] = le.fit_transform(merged.Institute_Category)
merged['Institute_location'] = le.fit_transform(merged.Institute_location)
merged['hometown'] = le.fit_transform(merged.hometown)
merged['Degree'] = le.fit_transform(merged.Degree)
merged['Current_year'] = le.fit_transform(merged.Current_year)
merged['Experience_Type'] = le.fit_transform(merged.Experience_Type)
merged['Location'] = le.fit_transform(merged.Location)
merged['Internship_Type'] = le.fit_transform(merged.Internship_Type)
merged['Internship_Location'] = le.fit_transform(merged.Internship_Location)
le = preprocessing.LabelEncoder()
merged['Expected_Stipend'] = le.fit_transform(merged.Expected_Stipend)
merged['Preferred_location'] = le.fit_transform(merged.Preferred_location)
merged['Institute_Category'] = le.fit_transform(merged.Institute_Category)
merged['Institute_location'] = le.fit_transform(merged.Institute_location)
merged['hometown'] = le.fit_transform(merged.hometown)
merged['Degree'] = le.fit_transform(merged.Degree)
merged['Current_year'] = le.fit_transform(merged.Current_year)
merged['Experience_Type'] = le.fit_transform(merged.Experience_Type)
merged['Location'] = le.fit_transform(merged.Location)
merged['Internship_Type'] = le.fit_transform(merged.Internship_Type)
merged['Institute_Category'] = le.fit_transform(merged.Institute_Category)
merged['Institute_location'] = le.fit_transform(merged.Institute_location)
merged['hometown'] = le.fit_transform(merged.hometown)
merged['Degree'] = le.fit_transform(merged.Degree)
merged = merged.fillna()
merged = merged.fillna('NA')
merged['Expected_Stipend'] = le.fit_transform(merged.Expected_Stipend)
merged['Preferred_location'] = le.fit_transform(merged.Preferred_location)
merged['Institute_Category'] = le.fit_transform(merged.Institute_Category)
merged['Institute_location'] = le.fit_transform(merged.Institute_location)
merged['hometown'] = le.fit_transform(merged.hometown)
merged['Degree'] = le.fit_transform(merged.Degree)
merged['Current_year'] = le.fit_transform(merged.Current_year)
merged['Experience_Type'] = le.fit_transform(merged.Experience_Type)
merged['Location'] = le.fit_transform(merged.Location)
merged['Internship_Type'] = le.fit_transform(merged.Internship_Type)
merged['Internship_Location'] = le.fit_transform(merged.Internship_Location)
merged.to_csv('check.csv')
from sklearn.preprocessing import OneHotEncoder
enc = OneHotEncoder()
enc.fit_transform(merged.Preferred_location)
enc.fit_transform(merged)
%clear
##---(Sat Feb 27 15:10:07 2016)---
runfile('C:/Users/vatsal/OneDrive/Date_Your_Data/start.py', wdir='C:/Users/vatsal/OneDrive/Date_Your_Data')
merged.columns
merged.Earliest_Start_Date
X_train = pd.read_csv("check.csv")
X_train.Earliest_Start_Date
pd.to_datetime(X_train['Earliest_Start_Date'])
X_train.Earliest_Start_Date=pd.to_datetime(X_train['Earliest_Start_Date'])
X_train.Earliest_Start_Date[0]
X_train.Earliest_Start_Date[0]-X_train.Earliest_Start_Date[1]
z=X_train.Earliest_Start_Date[0]-X_train.Earliest_Start_Date[1]
z[0]
z
print (z)
z.value
z.days
X_train.Start_Date = pd.to_datetime(X_train['Start_Date'])
X_train.Time_Difference = abs(X_train.Earliest_Start_Date-X_train.Start_Date).days
X_train.Time_Difference = abs(X_train.Earliest_Start_Date-X_train.Start_Date)
X_train.Time_Difference
X_train.Time_Difference.days
X_train.Time_Difference[0]
X_train.Time_Difference[0].days
X_train.Time_Difference.apply(lambda x:x.days)
X_train.Time_Difference[0].days
X_train.Time_Difference[0].days()
X_train.Time_Difference.apply(lambda x:x.days())
X_train.Time_Difference.map(lambda x:x.days)
%clear
X_train['Time_Difference'] = X_train.Time_Difference
X_train['Time_Difference']
X_train['Time_Difference'].apply(lambda x:x.days)
X_train['Time_Difference'].apply(lambda x:days)
X_train['Time_Difference'].apply(lambda x:x+1)
%clear
X_train.Time_Difference = abs(X_train.Earliest_Start_Date-X_train.Start_Date)
X_train['Time_Difference'] = X_train.Time_Difference
for i in range(len(X_train)): X_train['Time_Difference'][i]=X_train.Time_Difference[i].days
X_train.Time_Difference = abs(X_train.Earliest_Start_Date-X_train.Start_Date).astype('timedelta64[d]')
import pandas as pd
import numpy as np
from sklearn import preprocessing
#Reading the raw data files
train_raw = pd.read_csv("trainfiles/traincsv/train.csv")
internship = pd.read_csv("trainfiles/Internship/Internship.csv")
student = pd.read_csv("trainfiles/Student/student.csv")
#Removing duplicates from the student file
student = student.drop_duplicates("Student_ID")
#Merging all data to evaluate features and train the data
merged = pd.merge(train_raw,student, on='Student_ID',how='left')
merged = pd.merge(merged,internship,on='Internship_ID',how='left')
len(merged.columns)
merged.Percentage_PG = merged.Performance_PG/merged.PG_scale
merged.Percentage_UG = merged.Performance_UG/merged.UG_Scale
merged['Percentage_PG']=merged.Percentage_PG
merged['Percentage_UG']=merged.Percentage_UG
merged = merged.drop(merged.columns[[14,15,16,17,18]], axis=1)
#numeric_cols = ['Minimum_Duration','Is_Part_Time',
# 'Percentage_PG','Percentage_UG',
# 'Performance_12th','Performance_10th',
# 'No_of_openings','Stipend1','Stipend2',
# 'Internship_Duration(Months)','PR,UI','Marketing','Media','Social','Design','Web,Development','Business','Research','Writing','Plan','Creative','Process','Database','Strategy','Designing','Analysis','Facebook','Communication','Rest','Android','Presentation','Media Marketing','Twitter','Social Media Marketing','Operations','Java','Quality','HTML','Blogs','Digital Marketing','PHP','Market Research','Recruitment','Testing','CSS','Planning','API','Editing','Content Writing','Innovative','Lead Generation','Mobile App','SQL','Accounts','Reporting','JavaScript','Documentation','iOS','Branding','ACTING','Analytics','Initiative','Advertising','Cold Calling','Sourcing','ERP','NGO','Environment','Networking','Production','MySQL','ISO','Marketing Strategy','Survey','Visio','App Development','Front End','web development','Integration','HTML5','jQuery','Server','Coding','MBA','Content Creation','Reading','B2B','Content Development','Storm','E-commerce','Databases','Programming','Wordpress','Outreach','NABL','Web Design','Architecture','Web Application','Adobe','Scala','UI/UX','Python','Mac','Retail','Digital Media','Product Development','Data Collection','Algorithm','LESS','Email Marketing','Screening','Bootstrap','Finance','Content Marketing','Communication Skil','Hiring','Negotiation','Administration','Communication Skills','CSS3','Infographic','Youtube','CRM','CAD','Infographics','Access','Editorial','ARM,AJAX','.NET','Co-ordination','Ownership','Algorithms','Node','Drafting','Blogging','Animation','Teaching','Blogger','Relationship Management','3d','HTTP','Press Release','Accounting','Android App Development','Adobe Photoshop','Photography','Software Development','Social Networking','AngularJS','AWS','Secondary Research','Recruiting','Client Servicing','Leadership','Content Writer','Web Services','Payroll','Prospecting','Graphic Designing','Proofreading','Data Entry','Flex','Creativity','Data Management','Convincing','GATE','Social Media Management','Machine Learning','Client Relations','Web Applications','XML','MVC','HTML/CSS','Google+','Typing','Sketch','UI Design','Visual Design','Creative Writing','Graphic Designer','Product Design','PERL','Hindi','Chef',Sales Process,ASP.NET,Django,Public Relations,CMS,Vendor Management,Content Strategy,Client Relationship,Creative Design,C#,JSON,Linux,Client Interaction,Manufacturing,Customer Relationship Management,Recruitment Process,Business Relation,Talent Acquisition,CorelDRAW,Big Data,Material Design,Market Analysis,Adobe Illustrator,REST API,Tally,Electronics,Bee,C++,Online Research,Mockups,Front End Development,Gif,Product Management,MongoDB,Primary Research,Healthcare,Data Analytics,Google Analytics,Core PHP,B2B Sales,Social Media Tools,Node.js,Ruby,Drawing,Brand Promotion,Mechanical,Automobile,Lifestyle,Writing Blogs,CodeIgniter,Writing Skills,SQL Server,Logo Design,Project Management,API Integration,Client Communication,Growth Hacking,Interior Design,Personality,SAP,Scripting,Android Application Development,Event Management,Blog Writing,Statistics,Typography,Scalability,Assembly,Conceptualization,Microsoft,MVC Framework,PSD,Web Technologies,Web Application Development,Joomla,Image Processing,REST APIs,Data Structure,Confidence,Electrical,Counseling,Inside Sales,Organizational Skills,Video Editing,Data Structures,Mobile Application Development,PhoneGap,Storytelling,MySQL.,Ionic,Design Skills,Corporate Sales,Entrepreneurship,Films,Foundation,Payment Gateway']
"""
Drop Profile
Start Date
Stop Date
Internship Profile
Skills Required
Stipend
"""
#Converting categorical variables to numeric attributes
merged = merged.fillna('NA')
le = preprocessing.LabelEncoder()
merged['Expected_Stipend'] = le.fit_transform(merged.Expected_Stipend)
merged['Preferred_location'] = le.fit_transform(merged.Preferred_location)
merged['Institute_Category'] = le.fit_transform(merged.Institute_Category)
merged['Institute_location'] = le.fit_transform(merged.Institute_location)
merged['hometown'] = le.fit_transform(merged.hometown)
merged['Degree'] = le.fit_transform(merged.Degree)
merged['Current_year'] = le.fit_transform(merged.Current_year)
merged['Experience_Type'] = le.fit_transform(merged.Experience_Type)
merged['Location'] = le.fit_transform(merged.Location)
merged['Internship_Type'] = le.fit_transform(merged.Internship_Type)
merged['Internship_Location'] = le.fit_transform(merged.Internship_Location)
#merged.to_csv('check.csv')
#Removing unwanted features and cleaning earliest start date
X_train = pd.read_csv("check.csv")
X_train.Earliest_Start_Date=pd.to_datetime(X_train['Earliest_Start_Date'])
X_train.Start_Date = pd.to_datetime(X_train['Start_Date'])
X_train.Time_Difference = abs(X_train.Earliest_Start_Date-X_train.Start_Date).astype('timedelta64[d]')
X_train.Time_Difference = abs(X_train.Earliest_Start_Date-X_train.Start_Date).astype('timedelta64[d]')
X_train.Time_Difference = abs(X_train.Earliest_Start_Date-X_train.Start_Date).astype('timedelta64')
X_train.Time_Difference
X_train.Time_Difference = abs(X_train.Earliest_Start_Date-X_train.Start_Date).astype('timedelta64[days]')
X_train.Time_Difference = abs(X_train.Earliest_Start_Date-X_train.Start_Date)
X_train.Time_Difference
X_train.Time_Difference.astype['timedelta64[days]']
X_train.Time_Difference[0]
X_train.Time_Difference[0].days
Diff = [X_train.Time_Difference[num].days for num in range(len(X_train))]
X_train['Time_Difference'] = Diff
X_train['Time_Difference']
X_train = X_train.drop('Is_Shortlisted')
X_train = X_train.drop('Is_Shortlisted',axis=1)
%reset
y
%reset
%clear
X_train = pd.read_csv("check.csv")
X_train.Earliest_Start_Date=pd.to_datetime(X_train['Earliest_Start_Date'])
X_train.Start_Date = pd.to_datetime(X_train['Start_Date'])
X_train.Time_Difference = abs(X_train.Earliest_Start_Date-X_train.Start_Date)
#Calculating Time_Difference feature between earliest start date and Internship start date
Diff = [X_train.Time_Difference[num].days for num in range(len(X_train))]
X_train['Time_Difference'] = Diff
import pandas as pd
X_train = pd.read_csv("check.csv")
X_train.Earliest_Start_Date=pd.to_datetime(X_train['Earliest_Start_Date'])
X_train.Start_Date = pd.to_datetime(X_train['Start_Date'])
X_train.Time_Difference = abs(X_train.Earliest_Start_Date-X_train.Start_Date)
#Calculating Time_Difference feature between earliest start date and Internship start date
Diff = [X_train.Time_Difference[num].days for num in range(len(X_train))]
X_train['Time_Difference'] = Diff
y_train = X_train.Is_Shortlisted
y_train
X_train = X_train.drop('Is_Shortlisted')
X_train = X_train.drop('Is_Shortlisted',axis=1)
X_train = X_train.drop(['Is_Shortlisted','Earliest_Start_Date','Start_Date'],axis=1)
X_train = X_train.drop(['Earliest_Start_Date','Start_Date'],axis=1)
X_train.to_csv('training_features.csv')
from sklearn import tree
X_train = pd.read_csv('training_features.csv')
decision_tree = tree.DecisionTreeClassifier()
decision_tree.fit(X_train,y_train)
decision_tree.predict(X_train)
from sklearn.metrics import accuracy_score
accuracy_score(y_train,predictions)
predictions = decision_tree.predict(X_train)
from sklearn.metrics import accuracy_score
accuracy_score(y_train,predictions)
print (predictions)
predictions
predictions[5]
predictions[1:10]
y_train[1:10]
%clear
testing = pd.read_csv('test-date-your-data/test.csv')
testing.columns
test_raw = pd.read_csv('test-date-your-data/test.csv')
len(test_raw)
test_raw = pd.merge(test_raw,student, on='Student_ID',how='left')
import pandas as pd
import numpy as np
from sklearn import preprocessing
from sklearn import tree
from sklearn.metrics import accuracy_score
#Reading the raw data files
train_raw = pd.read_csv("trainfiles/traincsv/train.csv")
internship = pd.read_csv("trainfiles/Internship/Internship.csv")
student = pd.read_csv("trainfiles/Student/student.csv")
#Removing duplicates from the student file
student = student.drop_duplicates("Student_ID")
#Merging all data to evaluate features and train the data
merged = pd.merge(train_raw,student, on='Student_ID',how='left')
merged = pd.merge(merged,internship,on='Internship_ID',how='left')
len(merged.columns)
merged.Percentage_PG = merged.Performance_PG/merged.PG_scale
merged.Percentage_UG = merged.Performance_UG/merged.UG_Scale
merged['Percentage_PG']=merged.Percentage_PG
merged['Percentage_UG']=merged.Percentage_UG
merged = merged.drop(merged.columns[[14,15,16,17,18]], axis=1)
#Converting categorical variables to numeric attributes
merged = merged.fillna('NA')
le = preprocessing.LabelEncoder()
merged['Expected_Stipend'] = le.fit_transform(merged.Expected_Stipend)
merged['Preferred_location'] = le.fit_transform(merged.Preferred_location)
merged['Institute_Category'] = le.fit_transform(merged.Institute_Category)
merged['Institute_location'] = le.fit_transform(merged.Institute_location)
merged['hometown'] = le.fit_transform(merged.hometown)
merged['Degree'] = le.fit_transform(merged.Degree)
merged['Current_year'] = le.fit_transform(merged.Current_year)
merged['Experience_Type'] = le.fit_transform(merged.Experience_Type)
merged['Location'] = le.fit_transform(merged.Location)
merged['Internship_Type'] = le.fit_transform(merged.Internship_Type)
merged['Internship_Location'] = le.fit_transform(merged.Internship_Location)
test_raw = pd.read_csv('test-date-your-data/test.csv')
len(test_raw)
test_raw = pd.merge(test_raw,student, on='Student_ID',how='left')
len(test_raw)
test_raw = pd.merge(test_raw,internship,on='Internship_ID',how='left')
len(test_raw)
test_raw.columns
test_raw = pd.read_csv('test-date-your-data/test.csv')
test_raw = pd.merge(test_raw,student, on='Student_ID',how='left')
test_raw = pd.merge(test_raw,internship,on='Internship_ID',how='left')
test_raw.Percentage_PG = test_raw.Performance_PG/test_raw.PG_scale
test_raw.Percentage_UG = test_raw.Performance_UG/test_raw.UG_Scale
test_raw['Percentage_PG']=test_raw.Percentage_PG
test_raw['Percentage_UG']=test_raw.Percentage_UG
test_raw = test_raw.drop(test_raw.columns[[14,15,16,17,18]], axis=1)
#Converting categorical variables to numeric attributes
test_raw = test_raw.fillna('NA')
le = preprocessing.LabelEncoder()
test_raw['Expected_Stipend'] = le.fit_transform(test_raw.Expected_Stipend)
test_raw['Preferred_location'] = le.fit_transform(test_raw.Preferred_location)
test_raw['Institute_Category'] = le.fit_transform(test_raw.Institute_Category)
test_raw['Institute_location'] = le.fit_transform(test_raw.Institute_location)
test_raw['hometown'] = le.fit_transform(test_raw.hometown)
test_raw['Degree'] = le.fit_transform(test_raw.Degree)
test_raw['Current_year'] = le.fit_transform(test_raw.Current_year)
test_raw['Experience_Type'] = le.fit_transform(test_raw.Experience_Type)
test_raw['Location'] = le.fit_transform(test_raw.Location)
test_raw['Internship_Type'] = le.fit_transform(test_raw.Internship_Type)
test_raw['Internship_Location'] = le.fit_transform(test_raw.Internship_Location)
len(X_train)
len(X_train.columns)
test_raw = pd.read_csv('test-date-your-data/test.csv')
test_raw = pd.merge(test_raw,student, on='Student_ID',how='left')
test_raw = pd.merge(test_raw,internship,on='Internship_ID',how='left')
test_raw.Percentage_PG = test_raw.Performance_PG/test_raw.PG_scale
test_raw.Percentage_UG = test_raw.Performance_UG/test_raw.UG_Scale
test_raw['Percentage_PG']=test_raw.Percentage_PG
test_raw['Percentage_UG']=test_raw.Percentage_UG
test_raw = test_raw.drop(test_raw.columns[[14,15,16,17,18]], axis=1)
#Converting categorical variables to numeric attributes
test_raw = test_raw.fillna('NA')
le = preprocessing.LabelEncoder()
test_raw['Expected_Stipend'] = le.fit_transform(test_raw.Expected_Stipend)
test_raw['Preferred_location'] = le.fit_transform(test_raw.Preferred_location)
test_raw['Institute_Category'] = le.fit_transform(test_raw.Institute_Category)
test_raw['Institute_location'] = le.fit_transform(test_raw.Institute_location)
test_raw['hometown'] = le.fit_transform(test_raw.hometown)
test_raw['Degree'] = le.fit_transform(test_raw.Degree)
test_raw['Current_year'] = le.fit_transform(test_raw.Current_year)
test_raw['Experience_Type'] = le.fit_transform(test_raw.Experience_Type)
test_raw['Location'] = le.fit_transform(test_raw.Location)
test_raw['Internship_Type'] = le.fit_transform(test_raw.Internship_Type)
test_raw['Internship_Location'] = le.fit_transform(test_raw.Internship_Location)
test_raw.Earliest_Start_Date=pd.to_datetime(test_raw['Earliest_Start_Date'])
test_raw.Start_Date = pd.to_datetime(test_raw['Start_Date'])
test_raw.Time_Difference = abs(test_raw.Earliest_Start_Date-test_raw.Start_Date)
Diff_Test = [test_raw.Time_Difference[num].days for num in range(len(test_raw))]
test_raw['Time_Difference'] = Diff_Test
test_raw = pd.read_csv('test-date-your-data/test.csv')
test_raw = pd.merge(test_raw,student, on='Student_ID',how='left')
test_raw = pd.merge(test_raw,internship,on='Internship_ID',how='left')
test_raw.Percentage_PG = test_raw.Performance_PG/test_raw.PG_scale
test_raw.Percentage_UG = test_raw.Performance_UG/test_raw.UG_Scale
test_raw['Percentage_PG']=test_raw.Percentage_PG
test_raw['Percentage_UG']=test_raw.Percentage_UG
test_raw = test_raw.drop(test_raw.columns[[14,15,16,17,18]], axis=1)
#Converting categorical variables to numeric attributes
test_raw = test_raw.fillna('NA')
le = preprocessing.LabelEncoder()
test_raw['Expected_Stipend'] = le.fit_transform(test_raw.Expected_Stipend)
test_raw['Preferred_location'] = le.fit_transform(test_raw.Preferred_location)
test_raw['Institute_Category'] = le.fit_transform(test_raw.Institute_Category)
test_raw['Institute_location'] = le.fit_transform(test_raw.Institute_location)
test_raw['hometown'] = le.fit_transform(test_raw.hometown)
test_raw['Degree'] = le.fit_transform(test_raw.Degree)
test_raw['Current_year'] = le.fit_transform(test_raw.Current_year)
test_raw['Experience_Type'] = le.fit_transform(test_raw.Experience_Type)
test_raw['Location'] = le.fit_transform(test_raw.Location)
test_raw['Internship_Type'] = le.fit_transform(test_raw.Internship_Type)
test_raw['Internship_Location'] = le.fit_transform(test_raw.Internship_Location)
test_raw.Earliest_Start_Date=pd.to_datetime(test_raw['Earliest_Start_Date'])
test_raw.Start_Date = pd.to_datetime(test_raw['Start_Date'])
test_raw.Time_Difference = abs(test_raw.Earliest_Start_Date-test_raw.Start_Date)
Diff_Test = [test_raw.Time_Difference[num].days for num in range(len(test_raw))]
test_raw['Time_Difference'] = Diff_Test