Imports


In [1]:
import warnings

import numpy as np
import pandas as pd
import matplotlib as mpl
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.ensemble import RandomForestRegressor
from sklearn.linear_model import LinearRegression
from sklearn.linear_model import Lasso
from sklearn.linear_model import Ridge
from sklearn.metrics import mean_squared_error
from sklearn.preprocessing import normalize
from sklearn.preprocessing import scale

pd.set_option('display.max_columns', 1800)
plt.style.use('ggplot')
warnings.filterwarnings('ignore')

%matplotlib inline

In [2]:
mpl.rc('savefig', dpi=200)
params = {'figure.dpi' : 200,
          'figure.figsize' : (12, 10),
          'axes.axisbelow' : True,
          'lines.antialiased' : True,
          'axes.titlesize' : 'xx-large',
          'axes.labelsize' : 'x-large',
          'xtick.labelsize' : 'large',
          'ytick.labelsize' : 'large'}

for (k, v) in params.items():
    plt.rcParams[k] = v

Data


In [3]:
data = pd.DataFrame()
files = ['data/MERGED2003_PP.csv', 'data/MERGED2005_PP.csv',
         'data/MERGED2007_PP.csv']
dfs = []
for file in files:
    df = pd.read_csv(file, low_memory=False)
    print(df.shape)
    df['type'] = 'training'
    dfs.append(df)

data = pd.concat(dfs)


(6585, 1729)
(6824, 1729)
(6890, 1729)

In [4]:
testing = pd.read_csv('data/MERGED2011_PP.csv', low_memory=False)
print(testing.shape)
testing['type'] = 'testing'


(7675, 1729)

In [5]:
data = pd.concat([data, testing])

In [6]:
data.shape


Out[6]:
(27974, 1730)

In [7]:
data.head(3)


Out[7]:
UNITID OPEID opeid6 INSTNM CITY STABBR ZIP AccredAgency INSTURL NPCURL sch_deg HCM2 main NUMBRANCH PREDDEG HIGHDEG CONTROL st_fips region LOCALE locale2 LATITUDE LONGITUDE CCBASIC CCUGPROF CCSIZSET HBCU PBI ANNHI TRIBAL AANAPII HSI NANTI MENONLY WOMENONLY RELAFFIL ADM_RATE ADM_RATE_ALL SATVR25 SATVR75 SATMT25 SATMT75 SATWR25 SATWR75 SATVRMID SATMTMID SATWRMID ACTCM25 ACTCM75 ACTEN25 ACTEN75 ACTMT25 ACTMT75 ACTWR25 ACTWR75 ACTCMMID ACTENMID ACTMTMID ACTWRMID SAT_AVG SAT_AVG_ALL PCIP01 PCIP03 PCIP04 PCIP05 PCIP09 PCIP10 PCIP11 PCIP12 PCIP13 PCIP14 PCIP15 PCIP16 PCIP19 PCIP22 PCIP23 PCIP24 PCIP25 PCIP26 PCIP27 PCIP29 PCIP30 PCIP31 PCIP38 PCIP39 PCIP40 PCIP41 PCIP42 PCIP43 PCIP44 PCIP45 PCIP46 PCIP47 PCIP48 PCIP49 PCIP50 PCIP51 PCIP52 PCIP54 CIP01CERT1 CIP01CERT2 CIP01ASSOC CIP01CERT4 CIP01BACHL CIP03CERT1 CIP03CERT2 CIP03ASSOC CIP03CERT4 CIP03BACHL CIP04CERT1 CIP04CERT2 CIP04ASSOC CIP04CERT4 CIP04BACHL CIP05CERT1 CIP05CERT2 CIP05ASSOC CIP05CERT4 CIP05BACHL CIP09CERT1 CIP09CERT2 CIP09ASSOC CIP09CERT4 CIP09BACHL CIP10CERT1 CIP10CERT2 CIP10ASSOC CIP10CERT4 CIP10BACHL CIP11CERT1 CIP11CERT2 CIP11ASSOC CIP11CERT4 CIP11BACHL CIP12CERT1 CIP12CERT2 CIP12ASSOC CIP12CERT4 CIP12BACHL CIP13CERT1 CIP13CERT2 CIP13ASSOC CIP13CERT4 CIP13BACHL CIP14CERT1 CIP14CERT2 CIP14ASSOC CIP14CERT4 CIP14BACHL CIP15CERT1 CIP15CERT2 CIP15ASSOC CIP15CERT4 CIP15BACHL CIP16CERT1 CIP16CERT2 CIP16ASSOC CIP16CERT4 CIP16BACHL CIP19CERT1 CIP19CERT2 CIP19ASSOC CIP19CERT4 CIP19BACHL CIP22CERT1 CIP22CERT2 CIP22ASSOC CIP22CERT4 CIP22BACHL CIP23CERT1 CIP23CERT2 CIP23ASSOC CIP23CERT4 CIP23BACHL CIP24CERT1 CIP24CERT2 CIP24ASSOC CIP24CERT4 CIP24BACHL CIP25CERT1 CIP25CERT2 CIP25ASSOC CIP25CERT4 CIP25BACHL CIP26CERT1 CIP26CERT2 CIP26ASSOC CIP26CERT4 CIP26BACHL CIP27CERT1 CIP27CERT2 CIP27ASSOC CIP27CERT4 CIP27BACHL CIP29CERT1 CIP29CERT2 CIP29ASSOC CIP29CERT4 CIP29BACHL CIP30CERT1 CIP30CERT2 CIP30ASSOC CIP30CERT4 CIP30BACHL CIP31CERT1 CIP31CERT2 CIP31ASSOC CIP31CERT4 CIP31BACHL CIP38CERT1 CIP38CERT2 CIP38ASSOC CIP38CERT4 CIP38BACHL CIP39CERT1 CIP39CERT2 CIP39ASSOC CIP39CERT4 CIP39BACHL CIP40CERT1 CIP40CERT2 CIP40ASSOC CIP40CERT4 CIP40BACHL CIP41CERT1 CIP41CERT2 CIP41ASSOC CIP41CERT4 CIP41BACHL CIP42CERT1 CIP42CERT2 CIP42ASSOC CIP42CERT4 CIP42BACHL CIP43CERT1 CIP43CERT2 CIP43ASSOC CIP43CERT4 CIP43BACHL CIP44CERT1 CIP44CERT2 CIP44ASSOC CIP44CERT4 CIP44BACHL CIP45CERT1 CIP45CERT2 CIP45ASSOC CIP45CERT4 CIP45BACHL CIP46CERT1 CIP46CERT2 CIP46ASSOC CIP46CERT4 CIP46BACHL CIP47CERT1 CIP47CERT2 CIP47ASSOC CIP47CERT4 CIP47BACHL CIP48CERT1 CIP48CERT2 CIP48ASSOC CIP48CERT4 CIP48BACHL CIP49CERT1 CIP49CERT2 CIP49ASSOC CIP49CERT4 CIP49BACHL CIP50CERT1 CIP50CERT2 CIP50ASSOC CIP50CERT4 CIP50BACHL CIP51CERT1 CIP51CERT2 CIP51ASSOC CIP51CERT4 CIP51BACHL CIP52CERT1 CIP52CERT2 CIP52ASSOC CIP52CERT4 CIP52BACHL CIP54CERT1 CIP54CERT2 CIP54ASSOC CIP54CERT4 CIP54BACHL DISTANCEONLY UGDS UG UGDS_WHITE UGDS_BLACK UGDS_HISP UGDS_ASIAN UGDS_AIAN UGDS_NHPI UGDS_2MOR UGDS_NRA UGDS_UNKN UGDS_WHITENH UGDS_BLACKNH UGDS_API UGDS_AIANOld UGDS_HISPOld UG_NRA UG_UNKN UG_WHITENH UG_BLACKNH UG_API UG_AIANOld UG_HISPOld PPTUG_EF PPTUG_EF2 CURROPER NPT4_PUB NPT4_PRIV NPT4_PROG NPT4_OTHER NPT41_PUB NPT42_PUB NPT43_PUB NPT44_PUB NPT45_PUB NPT41_PRIV NPT42_PRIV NPT43_PRIV NPT44_PRIV NPT45_PRIV NPT41_PROG NPT42_PROG NPT43_PROG NPT44_PROG NPT45_PROG NPT41_OTHER NPT42_OTHER NPT43_OTHER NPT44_OTHER NPT45_OTHER NPT4_048_PUB NPT4_048_PRIV NPT4_048_PROG NPT4_048_OTHER NPT4_3075_PUB NPT4_3075_PRIV NPT4_75UP_PUB NPT4_75UP_PRIV NPT4_3075_PROG NPT4_3075_OTHER NPT4_75UP_PROG NPT4_75UP_OTHER NUM4_PUB NUM4_PRIV NUM4_PROG NUM4_OTHER NUM41_PUB NUM42_PUB NUM43_PUB NUM44_PUB NUM45_PUB NUM41_PRIV NUM42_PRIV NUM43_PRIV NUM44_PRIV NUM45_PRIV NUM41_PROG NUM42_PROG NUM43_PROG NUM44_PROG NUM45_PROG NUM41_OTHER NUM42_OTHER NUM43_OTHER NUM44_OTHER NUM45_OTHER COSTT4_A COSTT4_P TUITIONFEE_IN TUITIONFEE_OUT TUITIONFEE_PROG TUITFTE INEXPFTE AVGFACSAL PFTFAC PCTPELL C150_4 C150_L4 C150_4_POOLED C150_L4_POOLED poolyrs PFTFTUG1_EF D150_4 D150_L4 D150_4_POOLED D150_L4_POOLED C150_4_WHITE C150_4_BLACK C150_4_HISP C150_4_ASIAN C150_4_AIAN C150_4_NHPI C150_4_2MOR C150_4_NRA C150_4_UNKN C150_4_WHITENH C150_4_BLACKNH C150_4_API C150_4_AIANOld C150_4_HISPOld C150_L4_WHITE C150_L4_BLACK C150_L4_HISP C150_L4_ASIAN C150_L4_AIAN C150_L4_NHPI C150_L4_2MOR C150_L4_NRA C150_L4_UNKN C150_L4_WHITENH C150_L4_BLACKNH C150_L4_API C150_L4_AIANOld C150_L4_HISPOld C200_4 C200_L4 D200_4 D200_L4 RET_FT4 RET_FTL4 RET_PT4 RET_PTL4 C200_4_POOLED C200_L4_POOLED poolyrs200 D200_4_POOLED D200_L4_POOLED PCTFLOAN UG25abv CDR2 CDR3 DEATH_YR2_RT COMP_ORIG_YR2_RT COMP_4YR_TRANS_YR2_RT COMP_2YR_TRANS_YR2_RT WDRAW_ORIG_YR2_RT WDRAW_4YR_TRANS_YR2_RT WDRAW_2YR_TRANS_YR2_RT ENRL_ORIG_YR2_RT ENRL_4YR_TRANS_YR2_RT ENRL_2YR_TRANS_YR2_RT UNKN_ORIG_YR2_RT UNKN_4YR_TRANS_YR2_RT UNKN_2YR_TRANS_YR2_RT LO_INC_DEATH_YR2_RT LO_INC_COMP_ORIG_YR2_RT LO_INC_COMP_4YR_TRANS_YR2_RT LO_INC_COMP_2YR_TRANS_YR2_RT LO_INC_WDRAW_ORIG_YR2_RT LO_INC_WDRAW_4YR_TRANS_YR2_RT LO_INC_WDRAW_2YR_TRANS_YR2_RT LO_INC_ENRL_ORIG_YR2_RT LO_INC_ENRL_4YR_TRANS_YR2_RT LO_INC_ENRL_2YR_TRANS_YR2_RT LO_INC_UNKN_ORIG_YR2_RT LO_INC_UNKN_4YR_TRANS_YR2_RT LO_INC_UNKN_2YR_TRANS_YR2_RT MD_INC_DEATH_YR2_RT MD_INC_COMP_ORIG_YR2_RT MD_INC_COMP_4YR_TRANS_YR2_RT MD_INC_COMP_2YR_TRANS_YR2_RT MD_INC_WDRAW_ORIG_YR2_RT MD_INC_WDRAW_4YR_TRANS_YR2_RT MD_INC_WDRAW_2YR_TRANS_YR2_RT MD_INC_ENRL_ORIG_YR2_RT MD_INC_ENRL_4YR_TRANS_YR2_RT MD_INC_ENRL_2YR_TRANS_YR2_RT MD_INC_UNKN_ORIG_YR2_RT MD_INC_UNKN_4YR_TRANS_YR2_RT MD_INC_UNKN_2YR_TRANS_YR2_RT HI_INC_DEATH_YR2_RT HI_INC_COMP_ORIG_YR2_RT HI_INC_COMP_4YR_TRANS_YR2_RT HI_INC_COMP_2YR_TRANS_YR2_RT HI_INC_WDRAW_ORIG_YR2_RT HI_INC_WDRAW_4YR_TRANS_YR2_RT HI_INC_WDRAW_2YR_TRANS_YR2_RT HI_INC_ENRL_ORIG_YR2_RT HI_INC_ENRL_4YR_TRANS_YR2_RT HI_INC_ENRL_2YR_TRANS_YR2_RT HI_INC_UNKN_ORIG_YR2_RT HI_INC_UNKN_4YR_TRANS_YR2_RT HI_INC_UNKN_2YR_TRANS_YR2_RT DEP_DEATH_YR2_RT DEP_COMP_ORIG_YR2_RT DEP_COMP_4YR_TRANS_YR2_RT DEP_COMP_2YR_TRANS_YR2_RT DEP_WDRAW_ORIG_YR2_RT DEP_WDRAW_4YR_TRANS_YR2_RT DEP_WDRAW_2YR_TRANS_YR2_RT DEP_ENRL_ORIG_YR2_RT DEP_ENRL_4YR_TRANS_YR2_RT DEP_ENRL_2YR_TRANS_YR2_RT DEP_UNKN_ORIG_YR2_RT DEP_UNKN_4YR_TRANS_YR2_RT DEP_UNKN_2YR_TRANS_YR2_RT IND_DEATH_YR2_RT IND_COMP_ORIG_YR2_RT IND_COMP_4YR_TRANS_YR2_RT IND_COMP_2YR_TRANS_YR2_RT IND_WDRAW_ORIG_YR2_RT IND_WDRAW_4YR_TRANS_YR2_RT IND_WDRAW_2YR_TRANS_YR2_RT IND_ENRL_ORIG_YR2_RT IND_ENRL_4YR_TRANS_YR2_RT IND_ENRL_2YR_TRANS_YR2_RT IND_UNKN_ORIG_YR2_RT IND_UNKN_4YR_TRANS_YR2_RT IND_UNKN_2YR_TRANS_YR2_RT FEMALE_DEATH_YR2_RT FEMALE_COMP_ORIG_YR2_RT FEMALE_COMP_4YR_TRANS_YR2_RT FEMALE_COMP_2YR_TRANS_YR2_RT FEMALE_WDRAW_ORIG_YR2_RT FEMALE_WDRAW_4YR_TRANS_YR2_RT FEMALE_WDRAW_2YR_TRANS_YR2_RT FEMALE_ENRL_ORIG_YR2_RT FEMALE_ENRL_4YR_TRANS_YR2_RT FEMALE_ENRL_2YR_TRANS_YR2_RT FEMALE_UNKN_ORIG_YR2_RT FEMALE_UNKN_4YR_TRANS_YR2_RT FEMALE_UNKN_2YR_TRANS_YR2_RT MALE_DEATH_YR2_RT MALE_COMP_ORIG_YR2_RT MALE_COMP_4YR_TRANS_YR2_RT MALE_COMP_2YR_TRANS_YR2_RT MALE_WDRAW_ORIG_YR2_RT MALE_WDRAW_4YR_TRANS_YR2_RT MALE_WDRAW_2YR_TRANS_YR2_RT MALE_ENRL_ORIG_YR2_RT MALE_ENRL_4YR_TRANS_YR2_RT MALE_ENRL_2YR_TRANS_YR2_RT MALE_UNKN_ORIG_YR2_RT MALE_UNKN_4YR_TRANS_YR2_RT MALE_UNKN_2YR_TRANS_YR2_RT PELL_DEATH_YR2_RT PELL_COMP_ORIG_YR2_RT PELL_COMP_4YR_TRANS_YR2_RT PELL_COMP_2YR_TRANS_YR2_RT PELL_WDRAW_ORIG_YR2_RT PELL_WDRAW_4YR_TRANS_YR2_RT PELL_WDRAW_2YR_TRANS_YR2_RT PELL_ENRL_ORIG_YR2_RT PELL_ENRL_4YR_TRANS_YR2_RT PELL_ENRL_2YR_TRANS_YR2_RT PELL_UNKN_ORIG_YR2_RT PELL_UNKN_4YR_TRANS_YR2_RT PELL_UNKN_2YR_TRANS_YR2_RT NOPELL_DEATH_YR2_RT NOPELL_COMP_ORIG_YR2_RT NOPELL_COMP_4YR_TRANS_YR2_RT NOPELL_COMP_2YR_TRANS_YR2_RT NOPELL_WDRAW_ORIG_YR2_RT NOPELL_WDRAW_4YR_TRANS_YR2_RT NOPELL_WDRAW_2YR_TRANS_YR2_RT NOPELL_ENRL_ORIG_YR2_RT NOPELL_ENRL_4YR_TRANS_YR2_RT NOPELL_ENRL_2YR_TRANS_YR2_RT NOPELL_UNKN_ORIG_YR2_RT NOPELL_UNKN_4YR_TRANS_YR2_RT NOPELL_UNKN_2YR_TRANS_YR2_RT LOAN_DEATH_YR2_RT LOAN_COMP_ORIG_YR2_RT LOAN_COMP_4YR_TRANS_YR2_RT LOAN_COMP_2YR_TRANS_YR2_RT LOAN_WDRAW_ORIG_YR2_RT LOAN_WDRAW_4YR_TRANS_YR2_RT LOAN_WDRAW_2YR_TRANS_YR2_RT LOAN_ENRL_ORIG_YR2_RT LOAN_ENRL_4YR_TRANS_YR2_RT LOAN_ENRL_2YR_TRANS_YR2_RT LOAN_UNKN_ORIG_YR2_RT LOAN_UNKN_4YR_TRANS_YR2_RT LOAN_UNKN_2YR_TRANS_YR2_RT NOLOAN_DEATH_YR2_RT NOLOAN_COMP_ORIG_YR2_RT NOLOAN_COMP_4YR_TRANS_YR2_RT NOLOAN_COMP_2YR_TRANS_YR2_RT NOLOAN_WDRAW_ORIG_YR2_RT NOLOAN_WDRAW_4YR_TRANS_YR2_RT NOLOAN_WDRAW_2YR_TRANS_YR2_RT NOLOAN_ENRL_ORIG_YR2_RT NOLOAN_ENRL_4YR_TRANS_YR2_RT NOLOAN_ENRL_2YR_TRANS_YR2_RT NOLOAN_UNKN_ORIG_YR2_RT NOLOAN_UNKN_4YR_TRANS_YR2_RT NOLOAN_UNKN_2YR_TRANS_YR2_RT FIRSTGEN_DEATH_YR2_RT FIRSTGEN_COMP_ORIG_YR2_RT FIRSTGEN_COMP_4YR_TRANS_YR2_RT FIRSTGEN_COMP_2YR_TRANS_YR2_RT FIRSTGEN_WDRAW_ORIG_YR2_RT FIRSTGEN_WDRAW_4YR_TRANS_YR2_RT FIRSTGEN_WDRAW_2YR_TRANS_YR2_RT FIRSTGEN_ENRL_ORIG_YR2_RT FIRSTGEN_ENRL_4YR_TRANS_YR2_RT FIRSTGEN_ENRL_2YR_TRANS_YR2_RT FIRSTGEN_UNKN_ORIG_YR2_RT FIRSTGEN_UNKN_4YR_TRANS_YR2_RT FIRSTGEN_UNKN_2YR_TRANS_YR2_RT NOT1STGEN_DEATH_YR2_RT NOT1STGEN_COMP_ORIG_YR2_RT NOT1STGEN_COMP_4YR_TRANS_YR2_RT NOT1STGEN_COMP_2YR_TRANS_YR2_RT NOT1STGEN_WDRAW_ORIG_YR2_RT NOT1STGEN_WDRAW_4YR_TRANS_YR2_RT NOT1STGEN_WDRAW_2YR_TRANS_YR2_RT NOT1STGEN_ENRL_ORIG_YR2_RT NOT1STGEN_ENRL_4YR_TRANS_YR2_RT NOT1STGEN_ENRL_2YR_TRANS_YR2_RT NOT1STGEN_UNKN_ORIG_YR2_RT NOT1STGEN_UNKN_4YR_TRANS_YR2_RT NOT1STGEN_UNKN_2YR_TRANS_YR2_RT DEATH_YR3_RT COMP_ORIG_YR3_RT COMP_4YR_TRANS_YR3_RT COMP_2YR_TRANS_YR3_RT WDRAW_ORIG_YR3_RT WDRAW_4YR_TRANS_YR3_RT WDRAW_2YR_TRANS_YR3_RT ENRL_ORIG_YR3_RT ENRL_4YR_TRANS_YR3_RT ENRL_2YR_TRANS_YR3_RT UNKN_ORIG_YR3_RT UNKN_4YR_TRANS_YR3_RT UNKN_2YR_TRANS_YR3_RT LO_INC_DEATH_YR3_RT LO_INC_COMP_ORIG_YR3_RT LO_INC_COMP_4YR_TRANS_YR3_RT LO_INC_COMP_2YR_TRANS_YR3_RT LO_INC_WDRAW_ORIG_YR3_RT LO_INC_WDRAW_4YR_TRANS_YR3_RT LO_INC_WDRAW_2YR_TRANS_YR3_RT LO_INC_ENRL_ORIG_YR3_RT LO_INC_ENRL_4YR_TRANS_YR3_RT LO_INC_ENRL_2YR_TRANS_YR3_RT LO_INC_UNKN_ORIG_YR3_RT LO_INC_UNKN_4YR_TRANS_YR3_RT LO_INC_UNKN_2YR_TRANS_YR3_RT MD_INC_DEATH_YR3_RT MD_INC_COMP_ORIG_YR3_RT MD_INC_COMP_4YR_TRANS_YR3_RT MD_INC_COMP_2YR_TRANS_YR3_RT MD_INC_WDRAW_ORIG_YR3_RT MD_INC_WDRAW_4YR_TRANS_YR3_RT MD_INC_WDRAW_2YR_TRANS_YR3_RT MD_INC_ENRL_ORIG_YR3_RT MD_INC_ENRL_4YR_TRANS_YR3_RT MD_INC_ENRL_2YR_TRANS_YR3_RT MD_INC_UNKN_ORIG_YR3_RT MD_INC_UNKN_4YR_TRANS_YR3_RT MD_INC_UNKN_2YR_TRANS_YR3_RT HI_INC_DEATH_YR3_RT HI_INC_COMP_ORIG_YR3_RT HI_INC_COMP_4YR_TRANS_YR3_RT HI_INC_COMP_2YR_TRANS_YR3_RT HI_INC_WDRAW_ORIG_YR3_RT HI_INC_WDRAW_4YR_TRANS_YR3_RT HI_INC_WDRAW_2YR_TRANS_YR3_RT HI_INC_ENRL_ORIG_YR3_RT HI_INC_ENRL_4YR_TRANS_YR3_RT HI_INC_ENRL_2YR_TRANS_YR3_RT HI_INC_UNKN_ORIG_YR3_RT HI_INC_UNKN_4YR_TRANS_YR3_RT HI_INC_UNKN_2YR_TRANS_YR3_RT DEP_DEATH_YR3_RT DEP_COMP_ORIG_YR3_RT DEP_COMP_4YR_TRANS_YR3_RT DEP_COMP_2YR_TRANS_YR3_RT DEP_WDRAW_ORIG_YR3_RT DEP_WDRAW_4YR_TRANS_YR3_RT DEP_WDRAW_2YR_TRANS_YR3_RT DEP_ENRL_ORIG_YR3_RT DEP_ENRL_4YR_TRANS_YR3_RT DEP_ENRL_2YR_TRANS_YR3_RT DEP_UNKN_ORIG_YR3_RT DEP_UNKN_4YR_TRANS_YR3_RT DEP_UNKN_2YR_TRANS_YR3_RT IND_DEATH_YR3_RT IND_COMP_ORIG_YR3_RT IND_COMP_4YR_TRANS_YR3_RT IND_COMP_2YR_TRANS_YR3_RT IND_WDRAW_ORIG_YR3_RT IND_WDRAW_4YR_TRANS_YR3_RT IND_WDRAW_2YR_TRANS_YR3_RT IND_ENRL_ORIG_YR3_RT IND_ENRL_4YR_TRANS_YR3_RT IND_ENRL_2YR_TRANS_YR3_RT IND_UNKN_ORIG_YR3_RT IND_UNKN_4YR_TRANS_YR3_RT IND_UNKN_2YR_TRANS_YR3_RT FEMALE_DEATH_YR3_RT FEMALE_COMP_ORIG_YR3_RT FEMALE_COMP_4YR_TRANS_YR3_RT FEMALE_COMP_2YR_TRANS_YR3_RT FEMALE_WDRAW_ORIG_YR3_RT FEMALE_WDRAW_4YR_TRANS_YR3_RT FEMALE_WDRAW_2YR_TRANS_YR3_RT FEMALE_ENRL_ORIG_YR3_RT FEMALE_ENRL_4YR_TRANS_YR3_RT FEMALE_ENRL_2YR_TRANS_YR3_RT FEMALE_UNKN_ORIG_YR3_RT FEMALE_UNKN_4YR_TRANS_YR3_RT FEMALE_UNKN_2YR_TRANS_YR3_RT MALE_DEATH_YR3_RT MALE_COMP_ORIG_YR3_RT MALE_COMP_4YR_TRANS_YR3_RT MALE_COMP_2YR_TRANS_YR3_RT MALE_WDRAW_ORIG_YR3_RT MALE_WDRAW_4YR_TRANS_YR3_RT MALE_WDRAW_2YR_TRANS_YR3_RT MALE_ENRL_ORIG_YR3_RT MALE_ENRL_4YR_TRANS_YR3_RT MALE_ENRL_2YR_TRANS_YR3_RT MALE_UNKN_ORIG_YR3_RT MALE_UNKN_4YR_TRANS_YR3_RT MALE_UNKN_2YR_TRANS_YR3_RT PELL_DEATH_YR3_RT PELL_COMP_ORIG_YR3_RT PELL_COMP_4YR_TRANS_YR3_RT PELL_COMP_2YR_TRANS_YR3_RT PELL_WDRAW_ORIG_YR3_RT PELL_WDRAW_4YR_TRANS_YR3_RT PELL_WDRAW_2YR_TRANS_YR3_RT PELL_ENRL_ORIG_YR3_RT PELL_ENRL_4YR_TRANS_YR3_RT PELL_ENRL_2YR_TRANS_YR3_RT PELL_UNKN_ORIG_YR3_RT PELL_UNKN_4YR_TRANS_YR3_RT PELL_UNKN_2YR_TRANS_YR3_RT NOPELL_DEATH_YR3_RT NOPELL_COMP_ORIG_YR3_RT NOPELL_COMP_4YR_TRANS_YR3_RT NOPELL_COMP_2YR_TRANS_YR3_RT NOPELL_WDRAW_ORIG_YR3_RT NOPELL_WDRAW_4YR_TRANS_YR3_RT NOPELL_WDRAW_2YR_TRANS_YR3_RT NOPELL_ENRL_ORIG_YR3_RT NOPELL_ENRL_4YR_TRANS_YR3_RT NOPELL_ENRL_2YR_TRANS_YR3_RT NOPELL_UNKN_ORIG_YR3_RT NOPELL_UNKN_4YR_TRANS_YR3_RT NOPELL_UNKN_2YR_TRANS_YR3_RT LOAN_DEATH_YR3_RT LOAN_COMP_ORIG_YR3_RT LOAN_COMP_4YR_TRANS_YR3_RT LOAN_COMP_2YR_TRANS_YR3_RT LOAN_WDRAW_ORIG_YR3_RT LOAN_WDRAW_4YR_TRANS_YR3_RT LOAN_WDRAW_2YR_TRANS_YR3_RT LOAN_ENRL_ORIG_YR3_RT LOAN_ENRL_4YR_TRANS_YR3_RT LOAN_ENRL_2YR_TRANS_YR3_RT LOAN_UNKN_ORIG_YR3_RT LOAN_UNKN_4YR_TRANS_YR3_RT LOAN_UNKN_2YR_TRANS_YR3_RT NOLOAN_DEATH_YR3_RT NOLOAN_COMP_ORIG_YR3_RT NOLOAN_COMP_4YR_TRANS_YR3_RT NOLOAN_COMP_2YR_TRANS_YR3_RT NOLOAN_WDRAW_ORIG_YR3_RT NOLOAN_WDRAW_4YR_TRANS_YR3_RT NOLOAN_WDRAW_2YR_TRANS_YR3_RT NOLOAN_ENRL_ORIG_YR3_RT NOLOAN_ENRL_4YR_TRANS_YR3_RT NOLOAN_ENRL_2YR_TRANS_YR3_RT NOLOAN_UNKN_ORIG_YR3_RT NOLOAN_UNKN_4YR_TRANS_YR3_RT NOLOAN_UNKN_2YR_TRANS_YR3_RT FIRSTGEN_DEATH_YR3_RT FIRSTGEN_COMP_ORIG_YR3_RT FIRSTGEN_COMP_4YR_TRANS_YR3_RT FIRSTGEN_COMP_2YR_TRANS_YR3_RT FIRSTGEN_WDRAW_ORIG_YR3_RT FIRSTGEN_WDRAW_4YR_TRANS_YR3_RT FIRSTGEN_WDRAW_2YR_TRANS_YR3_RT FIRSTGEN_ENRL_ORIG_YR3_RT FIRSTGEN_ENRL_4YR_TRANS_YR3_RT FIRSTGEN_ENRL_2YR_TRANS_YR3_RT FIRSTGEN_UNKN_ORIG_YR3_RT FIRSTGEN_UNKN_4YR_TRANS_YR3_RT FIRSTGEN_UNKN_2YR_TRANS_YR3_RT NOT1STGEN_DEATH_YR3_RT NOT1STGEN_COMP_ORIG_YR3_RT NOT1STGEN_COMP_4YR_TRANS_YR3_RT NOT1STGEN_COMP_2YR_TRANS_YR3_RT NOT1STGEN_WDRAW_ORIG_YR3_RT NOT1STGEN_WDRAW_4YR_TRANS_YR3_RT NOT1STGEN_WDRAW_2YR_TRANS_YR3_RT NOT1STGEN_ENRL_ORIG_YR3_RT NOT1STGEN_ENRL_4YR_TRANS_YR3_RT NOT1STGEN_ENRL_2YR_TRANS_YR3_RT NOT1STGEN_UNKN_ORIG_YR3_RT NOT1STGEN_UNKN_4YR_TRANS_YR3_RT NOT1STGEN_UNKN_2YR_TRANS_YR3_RT DEATH_YR4_RT COMP_ORIG_YR4_RT COMP_4YR_TRANS_YR4_RT COMP_2YR_TRANS_YR4_RT WDRAW_ORIG_YR4_RT WDRAW_4YR_TRANS_YR4_RT WDRAW_2YR_TRANS_YR4_RT ENRL_ORIG_YR4_RT ENRL_4YR_TRANS_YR4_RT ENRL_2YR_TRANS_YR4_RT UNKN_ORIG_YR4_RT UNKN_4YR_TRANS_YR4_RT UNKN_2YR_TRANS_YR4_RT LO_INC_DEATH_YR4_RT LO_INC_COMP_ORIG_YR4_RT LO_INC_COMP_4YR_TRANS_YR4_RT LO_INC_COMP_2YR_TRANS_YR4_RT LO_INC_WDRAW_ORIG_YR4_RT LO_INC_WDRAW_4YR_TRANS_YR4_RT LO_INC_WDRAW_2YR_TRANS_YR4_RT LO_INC_ENRL_ORIG_YR4_RT LO_INC_ENRL_4YR_TRANS_YR4_RT LO_INC_ENRL_2YR_TRANS_YR4_RT LO_INC_UNKN_ORIG_YR4_RT LO_INC_UNKN_4YR_TRANS_YR4_RT LO_INC_UNKN_2YR_TRANS_YR4_RT MD_INC_DEATH_YR4_RT MD_INC_COMP_ORIG_YR4_RT MD_INC_COMP_4YR_TRANS_YR4_RT MD_INC_COMP_2YR_TRANS_YR4_RT MD_INC_WDRAW_ORIG_YR4_RT MD_INC_WDRAW_4YR_TRANS_YR4_RT MD_INC_WDRAW_2YR_TRANS_YR4_RT MD_INC_ENRL_ORIG_YR4_RT MD_INC_ENRL_4YR_TRANS_YR4_RT MD_INC_ENRL_2YR_TRANS_YR4_RT MD_INC_UNKN_ORIG_YR4_RT MD_INC_UNKN_4YR_TRANS_YR4_RT MD_INC_UNKN_2YR_TRANS_YR4_RT HI_INC_DEATH_YR4_RT HI_INC_COMP_ORIG_YR4_RT HI_INC_COMP_4YR_TRANS_YR4_RT HI_INC_COMP_2YR_TRANS_YR4_RT HI_INC_WDRAW_ORIG_YR4_RT HI_INC_WDRAW_4YR_TRANS_YR4_RT HI_INC_WDRAW_2YR_TRANS_YR4_RT HI_INC_ENRL_ORIG_YR4_RT HI_INC_ENRL_4YR_TRANS_YR4_RT HI_INC_ENRL_2YR_TRANS_YR4_RT HI_INC_UNKN_ORIG_YR4_RT HI_INC_UNKN_4YR_TRANS_YR4_RT HI_INC_UNKN_2YR_TRANS_YR4_RT DEP_DEATH_YR4_RT DEP_COMP_ORIG_YR4_RT DEP_COMP_4YR_TRANS_YR4_RT DEP_COMP_2YR_TRANS_YR4_RT DEP_WDRAW_ORIG_YR4_RT DEP_WDRAW_4YR_TRANS_YR4_RT DEP_WDRAW_2YR_TRANS_YR4_RT DEP_ENRL_ORIG_YR4_RT DEP_ENRL_4YR_TRANS_YR4_RT DEP_ENRL_2YR_TRANS_YR4_RT DEP_UNKN_ORIG_YR4_RT DEP_UNKN_4YR_TRANS_YR4_RT DEP_UNKN_2YR_TRANS_YR4_RT IND_DEATH_YR4_RT IND_COMP_ORIG_YR4_RT IND_COMP_4YR_TRANS_YR4_RT IND_COMP_2YR_TRANS_YR4_RT IND_WDRAW_ORIG_YR4_RT IND_WDRAW_4YR_TRANS_YR4_RT IND_WDRAW_2YR_TRANS_YR4_RT IND_ENRL_ORIG_YR4_RT IND_ENRL_4YR_TRANS_YR4_RT IND_ENRL_2YR_TRANS_YR4_RT IND_UNKN_ORIG_YR4_RT IND_UNKN_4YR_TRANS_YR4_RT IND_UNKN_2YR_TRANS_YR4_RT FEMALE_DEATH_YR4_RT FEMALE_COMP_ORIG_YR4_RT FEMALE_COMP_4YR_TRANS_YR4_RT FEMALE_COMP_2YR_TRANS_YR4_RT FEMALE_WDRAW_ORIG_YR4_RT FEMALE_WDRAW_4YR_TRANS_YR4_RT FEMALE_WDRAW_2YR_TRANS_YR4_RT FEMALE_ENRL_ORIG_YR4_RT FEMALE_ENRL_4YR_TRANS_YR4_RT FEMALE_ENRL_2YR_TRANS_YR4_RT FEMALE_UNKN_ORIG_YR4_RT FEMALE_UNKN_4YR_TRANS_YR4_RT FEMALE_UNKN_2YR_TRANS_YR4_RT MALE_DEATH_YR4_RT MALE_COMP_ORIG_YR4_RT MALE_COMP_4YR_TRANS_YR4_RT MALE_COMP_2YR_TRANS_YR4_RT MALE_WDRAW_ORIG_YR4_RT MALE_WDRAW_4YR_TRANS_YR4_RT MALE_WDRAW_2YR_TRANS_YR4_RT MALE_ENRL_ORIG_YR4_RT MALE_ENRL_4YR_TRANS_YR4_RT MALE_ENRL_2YR_TRANS_YR4_RT MALE_UNKN_ORIG_YR4_RT MALE_UNKN_4YR_TRANS_YR4_RT MALE_UNKN_2YR_TRANS_YR4_RT PELL_DEATH_YR4_RT PELL_COMP_ORIG_YR4_RT PELL_COMP_4YR_TRANS_YR4_RT PELL_COMP_2YR_TRANS_YR4_RT PELL_WDRAW_ORIG_YR4_RT PELL_WDRAW_4YR_TRANS_YR4_RT PELL_WDRAW_2YR_TRANS_YR4_RT PELL_ENRL_ORIG_YR4_RT PELL_ENRL_4YR_TRANS_YR4_RT PELL_ENRL_2YR_TRANS_YR4_RT PELL_UNKN_ORIG_YR4_RT PELL_UNKN_4YR_TRANS_YR4_RT PELL_UNKN_2YR_TRANS_YR4_RT NOPELL_DEATH_YR4_RT NOPELL_COMP_ORIG_YR4_RT NOPELL_COMP_4YR_TRANS_YR4_RT NOPELL_COMP_2YR_TRANS_YR4_RT NOPELL_WDRAW_ORIG_YR4_RT NOPELL_WDRAW_4YR_TRANS_YR4_RT NOPELL_WDRAW_2YR_TRANS_YR4_RT NOPELL_ENRL_ORIG_YR4_RT NOPELL_ENRL_4YR_TRANS_YR4_RT NOPELL_ENRL_2YR_TRANS_YR4_RT NOPELL_UNKN_ORIG_YR4_RT NOPELL_UNKN_4YR_TRANS_YR4_RT NOPELL_UNKN_2YR_TRANS_YR4_RT LOAN_DEATH_YR4_RT LOAN_COMP_ORIG_YR4_RT LOAN_COMP_4YR_TRANS_YR4_RT LOAN_COMP_2YR_TRANS_YR4_RT LOAN_WDRAW_ORIG_YR4_RT LOAN_WDRAW_4YR_TRANS_YR4_RT LOAN_WDRAW_2YR_TRANS_YR4_RT LOAN_ENRL_ORIG_YR4_RT LOAN_ENRL_4YR_TRANS_YR4_RT LOAN_ENRL_2YR_TRANS_YR4_RT LOAN_UNKN_ORIG_YR4_RT LOAN_UNKN_4YR_TRANS_YR4_RT LOAN_UNKN_2YR_TRANS_YR4_RT NOLOAN_DEATH_YR4_RT NOLOAN_COMP_ORIG_YR4_RT NOLOAN_COMP_4YR_TRANS_YR4_RT NOLOAN_COMP_2YR_TRANS_YR4_RT NOLOAN_WDRAW_ORIG_YR4_RT NOLOAN_WDRAW_4YR_TRANS_YR4_RT NOLOAN_WDRAW_2YR_TRANS_YR4_RT NOLOAN_ENRL_ORIG_YR4_RT NOLOAN_ENRL_4YR_TRANS_YR4_RT NOLOAN_ENRL_2YR_TRANS_YR4_RT NOLOAN_UNKN_ORIG_YR4_RT NOLOAN_UNKN_4YR_TRANS_YR4_RT NOLOAN_UNKN_2YR_TRANS_YR4_RT FIRSTGEN_DEATH_YR4_RT FIRSTGEN_COMP_ORIG_YR4_RT FIRSTGEN_COMP_4YR_TRANS_YR4_RT FIRSTGEN_COMP_2YR_TRANS_YR4_RT FIRSTGEN_WDRAW_ORIG_YR4_RT FIRSTGEN_WDRAW_4YR_TRANS_YR4_RT FIRSTGEN_WDRAW_2YR_TRANS_YR4_RT FIRSTGEN_ENRL_ORIG_YR4_RT FIRSTGEN_ENRL_4YR_TRANS_YR4_RT FIRSTGEN_ENRL_2YR_TRANS_YR4_RT FIRSTGEN_UNKN_ORIG_YR4_RT FIRSTGEN_UNKN_4YR_TRANS_YR4_RT FIRSTGEN_UNKN_2YR_TRANS_YR4_RT NOT1STGEN_DEATH_YR4_RT NOT1STGEN_COMP_ORIG_YR4_RT NOT1STGEN_COMP_4YR_TRANS_YR4_RT NOT1STGEN_COMP_2YR_TRANS_YR4_RT NOT1STGEN_WDRAW_ORIG_YR4_RT NOT1STGEN_WDRAW_4YR_TRANS_YR4_RT NOT1STGEN_WDRAW_2YR_TRANS_YR4_RT NOT1STGEN_ENRL_ORIG_YR4_RT NOT1STGEN_ENRL_4YR_TRANS_YR4_RT NOT1STGEN_ENRL_2YR_TRANS_YR4_RT NOT1STGEN_UNKN_ORIG_YR4_RT NOT1STGEN_UNKN_4YR_TRANS_YR4_RT NOT1STGEN_UNKN_2YR_TRANS_YR4_RT DEATH_YR6_RT COMP_ORIG_YR6_RT COMP_4YR_TRANS_YR6_RT COMP_2YR_TRANS_YR6_RT WDRAW_ORIG_YR6_RT WDRAW_4YR_TRANS_YR6_RT WDRAW_2YR_TRANS_YR6_RT ENRL_ORIG_YR6_RT ENRL_4YR_TRANS_YR6_RT ENRL_2YR_TRANS_YR6_RT UNKN_ORIG_YR6_RT UNKN_4YR_TRANS_YR6_RT UNKN_2YR_TRANS_YR6_RT LO_INC_DEATH_YR6_RT LO_INC_COMP_ORIG_YR6_RT LO_INC_COMP_4YR_TRANS_YR6_RT LO_INC_COMP_2YR_TRANS_YR6_RT LO_INC_WDRAW_ORIG_YR6_RT LO_INC_WDRAW_4YR_TRANS_YR6_RT LO_INC_WDRAW_2YR_TRANS_YR6_RT LO_INC_ENRL_ORIG_YR6_RT LO_INC_ENRL_4YR_TRANS_YR6_RT LO_INC_ENRL_2YR_TRANS_YR6_RT LO_INC_UNKN_ORIG_YR6_RT LO_INC_UNKN_4YR_TRANS_YR6_RT LO_INC_UNKN_2YR_TRANS_YR6_RT MD_INC_DEATH_YR6_RT MD_INC_COMP_ORIG_YR6_RT MD_INC_COMP_4YR_TRANS_YR6_RT MD_INC_COMP_2YR_TRANS_YR6_RT MD_INC_WDRAW_ORIG_YR6_RT MD_INC_WDRAW_4YR_TRANS_YR6_RT MD_INC_WDRAW_2YR_TRANS_YR6_RT MD_INC_ENRL_ORIG_YR6_RT MD_INC_ENRL_4YR_TRANS_YR6_RT MD_INC_ENRL_2YR_TRANS_YR6_RT MD_INC_UNKN_ORIG_YR6_RT MD_INC_UNKN_4YR_TRANS_YR6_RT MD_INC_UNKN_2YR_TRANS_YR6_RT HI_INC_DEATH_YR6_RT HI_INC_COMP_ORIG_YR6_RT HI_INC_COMP_4YR_TRANS_YR6_RT HI_INC_COMP_2YR_TRANS_YR6_RT HI_INC_WDRAW_ORIG_YR6_RT HI_INC_WDRAW_4YR_TRANS_YR6_RT HI_INC_WDRAW_2YR_TRANS_YR6_RT HI_INC_ENRL_ORIG_YR6_RT HI_INC_ENRL_4YR_TRANS_YR6_RT HI_INC_ENRL_2YR_TRANS_YR6_RT HI_INC_UNKN_ORIG_YR6_RT HI_INC_UNKN_4YR_TRANS_YR6_RT HI_INC_UNKN_2YR_TRANS_YR6_RT DEP_DEATH_YR6_RT DEP_COMP_ORIG_YR6_RT DEP_COMP_4YR_TRANS_YR6_RT DEP_COMP_2YR_TRANS_YR6_RT DEP_WDRAW_ORIG_YR6_RT DEP_WDRAW_4YR_TRANS_YR6_RT DEP_WDRAW_2YR_TRANS_YR6_RT DEP_ENRL_ORIG_YR6_RT DEP_ENRL_4YR_TRANS_YR6_RT DEP_ENRL_2YR_TRANS_YR6_RT DEP_UNKN_ORIG_YR6_RT DEP_UNKN_4YR_TRANS_YR6_RT DEP_UNKN_2YR_TRANS_YR6_RT IND_DEATH_YR6_RT IND_COMP_ORIG_YR6_RT IND_COMP_4YR_TRANS_YR6_RT IND_COMP_2YR_TRANS_YR6_RT IND_WDRAW_ORIG_YR6_RT IND_WDRAW_4YR_TRANS_YR6_RT IND_WDRAW_2YR_TRANS_YR6_RT IND_ENRL_ORIG_YR6_RT IND_ENRL_4YR_TRANS_YR6_RT IND_ENRL_2YR_TRANS_YR6_RT IND_UNKN_ORIG_YR6_RT IND_UNKN_4YR_TRANS_YR6_RT IND_UNKN_2YR_TRANS_YR6_RT FEMALE_DEATH_YR6_RT FEMALE_COMP_ORIG_YR6_RT FEMALE_COMP_4YR_TRANS_YR6_RT FEMALE_COMP_2YR_TRANS_YR6_RT FEMALE_WDRAW_ORIG_YR6_RT FEMALE_WDRAW_4YR_TRANS_YR6_RT FEMALE_WDRAW_2YR_TRANS_YR6_RT FEMALE_ENRL_ORIG_YR6_RT FEMALE_ENRL_4YR_TRANS_YR6_RT FEMALE_ENRL_2YR_TRANS_YR6_RT FEMALE_UNKN_ORIG_YR6_RT FEMALE_UNKN_4YR_TRANS_YR6_RT FEMALE_UNKN_2YR_TRANS_YR6_RT MALE_DEATH_YR6_RT MALE_COMP_ORIG_YR6_RT MALE_COMP_4YR_TRANS_YR6_RT MALE_COMP_2YR_TRANS_YR6_RT MALE_WDRAW_ORIG_YR6_RT MALE_WDRAW_4YR_TRANS_YR6_RT MALE_WDRAW_2YR_TRANS_YR6_RT MALE_ENRL_ORIG_YR6_RT MALE_ENRL_4YR_TRANS_YR6_RT MALE_ENRL_2YR_TRANS_YR6_RT MALE_UNKN_ORIG_YR6_RT MALE_UNKN_4YR_TRANS_YR6_RT MALE_UNKN_2YR_TRANS_YR6_RT PELL_DEATH_YR6_RT PELL_COMP_ORIG_YR6_RT PELL_COMP_4YR_TRANS_YR6_RT PELL_COMP_2YR_TRANS_YR6_RT PELL_WDRAW_ORIG_YR6_RT PELL_WDRAW_4YR_TRANS_YR6_RT PELL_WDRAW_2YR_TRANS_YR6_RT PELL_ENRL_ORIG_YR6_RT PELL_ENRL_4YR_TRANS_YR6_RT PELL_ENRL_2YR_TRANS_YR6_RT PELL_UNKN_ORIG_YR6_RT PELL_UNKN_4YR_TRANS_YR6_RT PELL_UNKN_2YR_TRANS_YR6_RT NOPELL_DEATH_YR6_RT NOPELL_COMP_ORIG_YR6_RT NOPELL_COMP_4YR_TRANS_YR6_RT NOPELL_COMP_2YR_TRANS_YR6_RT NOPELL_WDRAW_ORIG_YR6_RT NOPELL_WDRAW_4YR_TRANS_YR6_RT NOPELL_WDRAW_2YR_TRANS_YR6_RT NOPELL_ENRL_ORIG_YR6_RT NOPELL_ENRL_4YR_TRANS_YR6_RT NOPELL_ENRL_2YR_TRANS_YR6_RT NOPELL_UNKN_ORIG_YR6_RT NOPELL_UNKN_4YR_TRANS_YR6_RT NOPELL_UNKN_2YR_TRANS_YR6_RT LOAN_DEATH_YR6_RT LOAN_COMP_ORIG_YR6_RT LOAN_COMP_4YR_TRANS_YR6_RT LOAN_COMP_2YR_TRANS_YR6_RT LOAN_WDRAW_ORIG_YR6_RT LOAN_WDRAW_4YR_TRANS_YR6_RT LOAN_WDRAW_2YR_TRANS_YR6_RT LOAN_ENRL_ORIG_YR6_RT LOAN_ENRL_4YR_TRANS_YR6_RT LOAN_ENRL_2YR_TRANS_YR6_RT LOAN_UNKN_ORIG_YR6_RT LOAN_UNKN_4YR_TRANS_YR6_RT LOAN_UNKN_2YR_TRANS_YR6_RT NOLOAN_DEATH_YR6_RT NOLOAN_COMP_ORIG_YR6_RT NOLOAN_COMP_4YR_TRANS_YR6_RT NOLOAN_COMP_2YR_TRANS_YR6_RT NOLOAN_WDRAW_ORIG_YR6_RT NOLOAN_WDRAW_4YR_TRANS_YR6_RT NOLOAN_WDRAW_2YR_TRANS_YR6_RT NOLOAN_ENRL_ORIG_YR6_RT NOLOAN_ENRL_4YR_TRANS_YR6_RT NOLOAN_ENRL_2YR_TRANS_YR6_RT NOLOAN_UNKN_ORIG_YR6_RT NOLOAN_UNKN_4YR_TRANS_YR6_RT NOLOAN_UNKN_2YR_TRANS_YR6_RT FIRSTGEN_DEATH_YR6_RT FIRSTGEN_COMP_ORIG_YR6_RT FIRSTGEN_COMP_4YR_TRANS_YR6_RT FIRSTGEN_COMP_2YR_TRANS_YR6_RT FIRSTGEN_WDRAW_ORIG_YR6_RT FIRSTGEN_WDRAW_4YR_TRANS_YR6_RT FIRSTGEN_WDRAW_2YR_TRANS_YR6_RT FIRSTGEN_ENRL_ORIG_YR6_RT FIRSTGEN_ENRL_4YR_TRANS_YR6_RT FIRSTGEN_ENRL_2YR_TRANS_YR6_RT FIRSTGEN_UNKN_ORIG_YR6_RT FIRSTGEN_UNKN_4YR_TRANS_YR6_RT FIRSTGEN_UNKN_2YR_TRANS_YR6_RT NOT1STGEN_DEATH_YR6_RT NOT1STGEN_COMP_ORIG_YR6_RT NOT1STGEN_COMP_4YR_TRANS_YR6_RT NOT1STGEN_COMP_2YR_TRANS_YR6_RT NOT1STGEN_WDRAW_ORIG_YR6_RT NOT1STGEN_WDRAW_4YR_TRANS_YR6_RT NOT1STGEN_WDRAW_2YR_TRANS_YR6_RT NOT1STGEN_ENRL_ORIG_YR6_RT NOT1STGEN_ENRL_4YR_TRANS_YR6_RT NOT1STGEN_ENRL_2YR_TRANS_YR6_RT NOT1STGEN_UNKN_ORIG_YR6_RT NOT1STGEN_UNKN_4YR_TRANS_YR6_RT NOT1STGEN_UNKN_2YR_TRANS_YR6_RT DEATH_YR8_RT COMP_ORIG_YR8_RT COMP_4YR_TRANS_YR8_RT COMP_2YR_TRANS_YR8_RT WDRAW_ORIG_YR8_RT WDRAW_4YR_TRANS_YR8_RT WDRAW_2YR_TRANS_YR8_RT ENRL_ORIG_YR8_RT ENRL_4YR_TRANS_YR8_RT ENRL_2YR_TRANS_YR8_RT UNKN_ORIG_YR8_RT UNKN_4YR_TRANS_YR8_RT UNKN_2YR_TRANS_YR8_RT LO_INC_DEATH_YR8_RT LO_INC_COMP_ORIG_YR8_RT LO_INC_COMP_4YR_TRANS_YR8_RT LO_INC_COMP_2YR_TRANS_YR8_RT LO_INC_WDRAW_ORIG_YR8_RT LO_INC_WDRAW_4YR_TRANS_YR8_RT LO_INC_WDRAW_2YR_TRANS_YR8_RT LO_INC_ENRL_ORIG_YR8_RT LO_INC_ENRL_4YR_TRANS_YR8_RT LO_INC_ENRL_2YR_TRANS_YR8_RT LO_INC_UNKN_ORIG_YR8_RT LO_INC_UNKN_4YR_TRANS_YR8_RT LO_INC_UNKN_2YR_TRANS_YR8_RT MD_INC_DEATH_YR8_RT MD_INC_COMP_ORIG_YR8_RT MD_INC_COMP_4YR_TRANS_YR8_RT MD_INC_COMP_2YR_TRANS_YR8_RT MD_INC_WDRAW_ORIG_YR8_RT MD_INC_WDRAW_4YR_TRANS_YR8_RT MD_INC_WDRAW_2YR_TRANS_YR8_RT MD_INC_ENRL_ORIG_YR8_RT MD_INC_ENRL_4YR_TRANS_YR8_RT MD_INC_ENRL_2YR_TRANS_YR8_RT MD_INC_UNKN_ORIG_YR8_RT MD_INC_UNKN_4YR_TRANS_YR8_RT MD_INC_UNKN_2YR_TRANS_YR8_RT HI_INC_DEATH_YR8_RT HI_INC_COMP_ORIG_YR8_RT HI_INC_COMP_4YR_TRANS_YR8_RT HI_INC_COMP_2YR_TRANS_YR8_RT HI_INC_WDRAW_ORIG_YR8_RT HI_INC_WDRAW_4YR_TRANS_YR8_RT HI_INC_WDRAW_2YR_TRANS_YR8_RT HI_INC_ENRL_ORIG_YR8_RT HI_INC_ENRL_4YR_TRANS_YR8_RT HI_INC_ENRL_2YR_TRANS_YR8_RT HI_INC_UNKN_ORIG_YR8_RT HI_INC_UNKN_4YR_TRANS_YR8_RT HI_INC_UNKN_2YR_TRANS_YR8_RT DEP_DEATH_YR8_RT DEP_COMP_ORIG_YR8_RT DEP_COMP_4YR_TRANS_YR8_RT DEP_COMP_2YR_TRANS_YR8_RT DEP_WDRAW_ORIG_YR8_RT DEP_WDRAW_4YR_TRANS_YR8_RT DEP_WDRAW_2YR_TRANS_YR8_RT DEP_ENRL_ORIG_YR8_RT DEP_ENRL_4YR_TRANS_YR8_RT DEP_ENRL_2YR_TRANS_YR8_RT DEP_UNKN_ORIG_YR8_RT DEP_UNKN_4YR_TRANS_YR8_RT DEP_UNKN_2YR_TRANS_YR8_RT IND_DEATH_YR8_RT IND_COMP_ORIG_YR8_RT IND_COMP_4YR_TRANS_YR8_RT IND_COMP_2YR_TRANS_YR8_RT IND_WDRAW_ORIG_YR8_RT IND_WDRAW_4YR_TRANS_YR8_RT IND_WDRAW_2YR_TRANS_YR8_RT IND_ENRL_ORIG_YR8_RT IND_ENRL_4YR_TRANS_YR8_RT IND_ENRL_2YR_TRANS_YR8_RT IND_UNKN_ORIG_YR8_RT IND_UNKN_4YR_TRANS_YR8_RT IND_UNKN_2YR_TRANS_YR8_RT FEMALE_DEATH_YR8_RT FEMALE_COMP_ORIG_YR8_RT FEMALE_COMP_4YR_TRANS_YR8_RT FEMALE_COMP_2YR_TRANS_YR8_RT FEMALE_WDRAW_ORIG_YR8_RT FEMALE_WDRAW_4YR_TRANS_YR8_RT FEMALE_WDRAW_2YR_TRANS_YR8_RT FEMALE_ENRL_ORIG_YR8_RT FEMALE_ENRL_4YR_TRANS_YR8_RT FEMALE_ENRL_2YR_TRANS_YR8_RT FEMALE_UNKN_ORIG_YR8_RT FEMALE_UNKN_4YR_TRANS_YR8_RT FEMALE_UNKN_2YR_TRANS_YR8_RT MALE_DEATH_YR8_RT MALE_COMP_ORIG_YR8_RT MALE_COMP_4YR_TRANS_YR8_RT MALE_COMP_2YR_TRANS_YR8_RT MALE_WDRAW_ORIG_YR8_RT MALE_WDRAW_4YR_TRANS_YR8_RT MALE_WDRAW_2YR_TRANS_YR8_RT MALE_ENRL_ORIG_YR8_RT MALE_ENRL_4YR_TRANS_YR8_RT MALE_ENRL_2YR_TRANS_YR8_RT MALE_UNKN_ORIG_YR8_RT MALE_UNKN_4YR_TRANS_YR8_RT MALE_UNKN_2YR_TRANS_YR8_RT PELL_DEATH_YR8_RT PELL_COMP_ORIG_YR8_RT PELL_COMP_4YR_TRANS_YR8_RT PELL_COMP_2YR_TRANS_YR8_RT PELL_WDRAW_ORIG_YR8_RT PELL_WDRAW_4YR_TRANS_YR8_RT PELL_WDRAW_2YR_TRANS_YR8_RT PELL_ENRL_ORIG_YR8_RT PELL_ENRL_4YR_TRANS_YR8_RT PELL_ENRL_2YR_TRANS_YR8_RT PELL_UNKN_ORIG_YR8_RT PELL_UNKN_4YR_TRANS_YR8_RT PELL_UNKN_2YR_TRANS_YR8_RT NOPELL_DEATH_YR8_RT NOPELL_COMP_ORIG_YR8_RT NOPELL_COMP_4YR_TRANS_YR8_RT NOPELL_COMP_2YR_TRANS_YR8_RT NOPELL_WDRAW_ORIG_YR8_RT NOPELL_WDRAW_4YR_TRANS_YR8_RT NOPELL_WDRAW_2YR_TRANS_YR8_RT NOPELL_ENRL_ORIG_YR8_RT NOPELL_ENRL_4YR_TRANS_YR8_RT NOPELL_ENRL_2YR_TRANS_YR8_RT NOPELL_UNKN_ORIG_YR8_RT NOPELL_UNKN_4YR_TRANS_YR8_RT NOPELL_UNKN_2YR_TRANS_YR8_RT LOAN_DEATH_YR8_RT LOAN_COMP_ORIG_YR8_RT LOAN_COMP_4YR_TRANS_YR8_RT LOAN_COMP_2YR_TRANS_YR8_RT LOAN_WDRAW_ORIG_YR8_RT LOAN_WDRAW_4YR_TRANS_YR8_RT LOAN_WDRAW_2YR_TRANS_YR8_RT LOAN_ENRL_ORIG_YR8_RT LOAN_ENRL_4YR_TRANS_YR8_RT LOAN_ENRL_2YR_TRANS_YR8_RT LOAN_UNKN_ORIG_YR8_RT LOAN_UNKN_4YR_TRANS_YR8_RT LOAN_UNKN_2YR_TRANS_YR8_RT NOLOAN_DEATH_YR8_RT NOLOAN_COMP_ORIG_YR8_RT NOLOAN_COMP_4YR_TRANS_YR8_RT NOLOAN_COMP_2YR_TRANS_YR8_RT NOLOAN_WDRAW_ORIG_YR8_RT NOLOAN_WDRAW_4YR_TRANS_YR8_RT NOLOAN_WDRAW_2YR_TRANS_YR8_RT NOLOAN_ENRL_ORIG_YR8_RT NOLOAN_ENRL_4YR_TRANS_YR8_RT NOLOAN_ENRL_2YR_TRANS_YR8_RT NOLOAN_UNKN_ORIG_YR8_RT NOLOAN_UNKN_4YR_TRANS_YR8_RT NOLOAN_UNKN_2YR_TRANS_YR8_RT FIRSTGEN_DEATH_YR8_RT FIRSTGEN_COMP_ORIG_YR8_RT FIRSTGEN_COMP_4YR_TRANS_YR8_RT FIRSTGEN_COMP_2YR_TRANS_YR8_RT FIRSTGEN_WDRAW_ORIG_YR8_RT FIRSTGEN_WDRAW_4YR_TRANS_YR8_RT FIRSTGEN_WDRAW_2YR_TRANS_YR8_RT FIRSTGEN_ENRL_ORIG_YR8_RT FIRSTGEN_ENRL_4YR_TRANS_YR8_RT FIRSTGEN_ENRL_2YR_TRANS_YR8_RT FIRSTGEN_UNKN_ORIG_YR8_RT FIRSTGEN_UNKN_4YR_TRANS_YR8_RT FIRSTGEN_UNKN_2YR_TRANS_YR8_RT NOT1STGEN_DEATH_YR8_RT NOT1STGEN_COMP_ORIG_YR8_RT NOT1STGEN_COMP_4YR_TRANS_YR8_RT NOT1STGEN_COMP_2YR_TRANS_YR8_RT NOT1STGEN_WDRAW_ORIG_YR8_RT NOT1STGEN_WDRAW_4YR_TRANS_YR8_RT NOT1STGEN_WDRAW_2YR_TRANS_YR8_RT NOT1STGEN_ENRL_ORIG_YR8_RT NOT1STGEN_ENRL_4YR_TRANS_YR8_RT NOT1STGEN_ENRL_2YR_TRANS_YR8_RT NOT1STGEN_UNKN_ORIG_YR8_RT NOT1STGEN_UNKN_4YR_TRANS_YR8_RT NOT1STGEN_UNKN_2YR_TRANS_YR8_RT RPY_1YR_RT COMPL_RPY_1YR_RT NONCOM_RPY_1YR_RT LO_INC_RPY_1YR_RT MD_INC_RPY_1YR_RT HI_INC_RPY_1YR_RT DEP_RPY_1YR_RT IND_RPY_1YR_RT PELL_RPY_1YR_RT NOPELL_RPY_1YR_RT FEMALE_RPY_1YR_RT MALE_RPY_1YR_RT FIRSTGEN_RPY_1YR_RT NOTFIRSTGEN_RPY_1YR_RT RPY_3YR_RT COMPL_RPY_3YR_RT NONCOM_RPY_3YR_RT LO_INC_RPY_3YR_RT MD_INC_RPY_3YR_RT HI_INC_RPY_3YR_RT DEP_RPY_3YR_RT IND_RPY_3YR_RT PELL_RPY_3YR_RT NOPELL_RPY_3YR_RT FEMALE_RPY_3YR_RT MALE_RPY_3YR_RT FIRSTGEN_RPY_3YR_RT NOTFIRSTGEN_RPY_3YR_RT RPY_5YR_RT COMPL_RPY_5YR_RT NONCOM_RPY_5YR_RT LO_INC_RPY_5YR_RT MD_INC_RPY_5YR_RT HI_INC_RPY_5YR_RT DEP_RPY_5YR_RT IND_RPY_5YR_RT PELL_RPY_5YR_RT NOPELL_RPY_5YR_RT FEMALE_RPY_5YR_RT MALE_RPY_5YR_RT FIRSTGEN_RPY_5YR_RT NOTFIRSTGEN_RPY_5YR_RT RPY_7YR_RT COMPL_RPY_7YR_RT NONCOM_RPY_7YR_RT LO_INC_RPY_7YR_RT MD_INC_RPY_7YR_RT HI_INC_RPY_7YR_RT DEP_RPY_7YR_RT IND_RPY_7YR_RT PELL_RPY_7YR_RT NOPELL_RPY_7YR_RT FEMALE_RPY_7YR_RT MALE_RPY_7YR_RT FIRSTGEN_RPY_7YR_RT NOTFIRSTGEN_RPY_7YR_RT INC_PCT_LO DEP_STAT_PCT_IND DEP_INC_PCT_LO IND_INC_PCT_LO PAR_ED_PCT_1STGEN INC_PCT_M1 INC_PCT_M2 INC_PCT_H1 INC_PCT_H2 DEP_INC_PCT_M1 DEP_INC_PCT_M2 DEP_INC_PCT_H1 DEP_INC_PCT_H2 IND_INC_PCT_M1 IND_INC_PCT_M2 IND_INC_PCT_H1 IND_INC_PCT_H2 PAR_ED_PCT_MS PAR_ED_PCT_HS PAR_ED_PCT_PS APPL_SCH_PCT_GE2 APPL_SCH_PCT_GE3 APPL_SCH_PCT_GE4 APPL_SCH_PCT_GE5 DEP_INC_AVG IND_INC_AVG OVERALL_YR2_N LO_INC_YR2_N MD_INC_YR2_N HI_INC_YR2_N DEP_YR2_N IND_YR2_N FEMALE_YR2_N MALE_YR2_N PELL_YR2_N NOPELL_YR2_N LOAN_YR2_N NOLOAN_YR2_N FIRSTGEN_YR2_N 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NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 0.558041958 0.363836164 0.43718593 0.769357496 0.394413408 0.159240759 0.14025974 0.102497503 0.03996004 0.176507538 0.17839196 0.148555276 0.059359297 0.129049973 0.073585942 0.021965953 0.006040637 0.021452514 0.372960894 0.605586592 0.370569494 0.187574671 0.111509359 0.065312625 60267.1455 27188.24048 2065 1151 643 PrivacySuppressed 1265 PrivacySuppressed 1340 690 1263 802 1880 185 746 1077 1950 1152 588 PrivacySuppressed 1211 PrivacySuppressed 1258 671 1209 741 1749 201 717 1022 1776 1073 536 PrivacySuppressed 1048 PrivacySuppressed 1146 592 1111 665 1593 183 663 898 1913 1205 554 141 1136 764 1009 567 1186 727 1643 270 651 1030 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 10500 14100 10250 10500 9581.5 7500 8500 11032 11619.5 7000 10500 8095 9676 10145.5 4533 191 4343 2316 1382 482 2612 1569 2530 1667 2573 1406 1522 2214 156.538905 4533 28937 19167 4125 2241 5005 3184 1821 5005 4475 5022 17111 16806 3733 4577 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 5022 0.91000002622604 0.61000001430511 22.5499992370605 538.719970703125 0.25999999046325 0.64999997615814 0.11999999731779 0.63999998569488 0.02999999932944 0.38999998569488 48224.03 33303 27183.849609375 10.4700002670288 9.90999984741211 67.0500030517578 29.7399997711181 1.04999995231628 2.08999991416931 15.6700000762939 8.42000007629394 96.5699996948242 54604.91 11.3900003433227 3.56999993324279 10.8400001525878 1.80 0.61 0.20 0.08 0.05 0.07 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 230 2349 37500 34300 7600 20000 48800 65500 32100 1184 722 429 477 1283 1052 1452 897 0.67134952545166 34700 40200 40400 31500 34600 40900 34200 42900 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 10500 14100 156.538905 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN training
2 100690 02503400 25034 SOUTHERN CHRISTIAN UNIVERSITY MONTGOMERY AL 36117-3553 NaN NaN NaN 3 0 1 1 3 4 2 1 5 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 0.0000 0.0000 0.0000 0 0.0000 0.0000 0.0000 0 0.0000 0.0000 0.0000 0.0000 0.0000 0 0.0000 0.6880 0 0.0000 0.0000 0 0 0 0.0000 0.312 0.0000 0 0.0000 0.0000 0.0000 0.0000 0 0 0 0 0.0000 0.0000 0.0000 0.0000 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NaN 336 NaN NaN NaN NaN NaN NaN NaN NaN 0.0000 0.0744 0.6637 0.2411 0.0000 0.0000 0.0208 NaN NaN NaN NaN NaN NaN NaN 0.0208 NaN 1 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 9440 9440 NaN 6970 3485 3043 0.8158 NaN NaN NaN NaN NaN NaN 1.0000 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 0.8720 0.028 NaN 0 0.141104294479 PrivacySuppressed 0 0.245398773006 PrivacySuppressed PrivacySuppressed 0.472392638037 PrivacySuppressed PrivacySuppressed PrivacySuppressed PrivacySuppressed 0 0 0.128205128205 PrivacySuppressed 0 0.307692307692 PrivacySuppressed PrivacySuppressed PrivacySuppressed PrivacySuppressed 0 PrivacySuppressed PrivacySuppressed 0 0 0.180555555556 0 0 PrivacySuppressed PrivacySuppressed 0 0.541666666667 PrivacySuppressed PrivacySuppressed PrivacySuppressed PrivacySuppressed 0 0 0 PrivacySuppressed 0 PrivacySuppressed 0 PrivacySuppressed PrivacySuppressed PrivacySuppressed PrivacySuppressed 0 0 0 0 PrivacySuppressed PrivacySuppressed 0 PrivacySuppressed PrivacySuppressed PrivacySuppressed PrivacySuppressed PrivacySuppressed PrivacySuppressed PrivacySuppressed PrivacySuppressed 0 0 PrivacySuppressed PrivacySuppressed 0 PrivacySuppressed PrivacySuppressed PrivacySuppressed PrivacySuppressed PrivacySuppressed PrivacySuppressed PrivacySuppressed PrivacySuppressed 0 0 PrivacySuppressed PrivacySuppressed 0 0.169014084507 PrivacySuppressed PrivacySuppressed 0.549295774648 PrivacySuppressed PrivacySuppressed PrivacySuppressed PrivacySuppressed 0 0 PrivacySuppressed PrivacySuppressed 0 0.304347826087 PrivacySuppressed PrivacySuppressed 0.413043478261 PrivacySuppressed PrivacySuppressed PrivacySuppressed PrivacySuppressed 0 0 PrivacySuppressed PrivacySuppressed 0 PrivacySuppressed PrivacySuppressed PrivacySuppressed 0.453781512605 PrivacySuppressed PrivacySuppressed PrivacySuppressed PrivacySuppressed 0 0 PrivacySuppressed PrivacySuppressed 0 PrivacySuppressed PrivacySuppressed PrivacySuppressed 0.522727272727 PrivacySuppressed PrivacySuppressed PrivacySuppressed PrivacySuppressed 0 0 PrivacySuppressed PrivacySuppressed 0 PrivacySuppressed PrivacySuppressed PrivacySuppressed PrivacySuppressed PrivacySuppressed PrivacySuppressed PrivacySuppressed PrivacySuppressed 0 0 PrivacySuppressed PrivacySuppressed 0 PrivacySuppressed PrivacySuppressed PrivacySuppressed PrivacySuppressed PrivacySuppressed PrivacySuppressed PrivacySuppressed PrivacySuppressed 0 0 0.1375 PrivacySuppressed 0 PrivacySuppressed 0 PrivacySuppressed 0.4625 PrivacySuppressed PrivacySuppressed PrivacySuppressed PrivacySuppressed 0 0 PrivacySuppressed PrivacySuppressed 0 0.203125 PrivacySuppressed PrivacySuppressed PrivacySuppressed PrivacySuppressed PrivacySuppressed PrivacySuppressed PrivacySuppressed 0 0 0.210526315789 PrivacySuppressed 0 0.443609022556 PrivacySuppressed PrivacySuppressed 0.157894736842 PrivacySuppressed PrivacySuppressed PrivacySuppressed PrivacySuppressed 0 0 0.171052631579 PrivacySuppressed 0 0.434210526316 PrivacySuppressed 0 PrivacySuppressed PrivacySuppressed PrivacySuppressed PrivacySuppressed PrivacySuppressed 0 0 PrivacySuppressed PrivacySuppressed 0 PrivacySuppressed PrivacySuppressed PrivacySuppressed PrivacySuppressed PrivacySuppressed PrivacySuppressed PrivacySuppressed 0 0 0 PrivacySuppressed 0 0 PrivacySuppressed 0 PrivacySuppressed PrivacySuppressed 0 0 0 PrivacySuppressed 0 0 PrivacySuppressed PrivacySuppressed 0 PrivacySuppressed PrivacySuppressed PrivacySuppressed 0 0 PrivacySuppressed 0 PrivacySuppressed 0 0 PrivacySuppressed PrivacySuppressed 0 PrivacySuppressed PrivacySuppressed PrivacySuppressed PrivacySuppressed PrivacySuppressed PrivacySuppressed PrivacySuppressed PrivacySuppressed 0 0 PrivacySuppressed PrivacySuppressed 0 PrivacySuppressed PrivacySuppressed PrivacySuppressed PrivacySuppressed PrivacySuppressed PrivacySuppressed 0 PrivacySuppressed 0 0 0.222222222222 PrivacySuppressed 0 0.402777777778 PrivacySuppressed PrivacySuppressed 0.152777777778 PrivacySuppressed PrivacySuppressed PrivacySuppressed PrivacySuppressed 0 0 0.189473684211 PrivacySuppressed 0 0.442105263158 PrivacySuppressed PrivacySuppressed PrivacySuppressed PrivacySuppressed PrivacySuppressed PrivacySuppressed PrivacySuppressed 0 0 0.263157894737 PrivacySuppressed 0 0.447368421053 PrivacySuppressed PrivacySuppressed PrivacySuppressed PrivacySuppressed PrivacySuppressed PrivacySuppressed PrivacySuppressed 0 0 PrivacySuppressed PrivacySuppressed 0 PrivacySuppressed PrivacySuppressed PrivacySuppressed PrivacySuppressed PrivacySuppressed PrivacySuppressed PrivacySuppressed PrivacySuppressed 0 0 PrivacySuppressed PrivacySuppressed 0 PrivacySuppressed PrivacySuppressed PrivacySuppressed PrivacySuppressed PrivacySuppressed PrivacySuppressed PrivacySuppressed PrivacySuppressed 0 0 0.237288135593 PrivacySuppressed 0 0.457627118644 PrivacySuppressed PrivacySuppressed PrivacySuppressed 0 PrivacySuppressed PrivacySuppressed PrivacySuppressed 0 0 PrivacySuppressed 0 0 0.363636363636 PrivacySuppressed PrivacySuppressed PrivacySuppressed PrivacySuppressed PrivacySuppressed PrivacySuppressed PrivacySuppressed 0 0 0.366972477064 PrivacySuppressed 0 0.366972477064 PrivacySuppressed PrivacySuppressed PrivacySuppressed PrivacySuppressed PrivacySuppressed PrivacySuppressed PrivacySuppressed 0 0 0.358490566038 PrivacySuppressed 0 PrivacySuppressed PrivacySuppressed PrivacySuppressed PrivacySuppressed PrivacySuppressed PrivacySuppressed PrivacySuppressed 0 0 0 PrivacySuppressed 0 0 0.5 PrivacySuppressed PrivacySuppressed PrivacySuppressed PrivacySuppressed PrivacySuppressed 0 PrivacySuppressed 0 0 PrivacySuppressed PrivacySuppressed 0 PrivacySuppressed 0 0 PrivacySuppressed 0 0 0 PrivacySuppressed 0 0 0 PrivacySuppressed 0 0 PrivacySuppressed PrivacySuppressed PrivacySuppressed PrivacySuppressed PrivacySuppressed 0 PrivacySuppressed 0 0 PrivacySuppressed PrivacySuppressed 0 PrivacySuppressed PrivacySuppressed PrivacySuppressed PrivacySuppressed PrivacySuppressed PrivacySuppressed PrivacySuppressed PrivacySuppressed 0 0 PrivacySuppressed PrivacySuppressed 0 PrivacySuppressed PrivacySuppressed PrivacySuppressed PrivacySuppressed PrivacySuppressed PrivacySuppressed 0 PrivacySuppressed 0 0 0.388059701493 PrivacySuppressed 0 0.298507462687 PrivacySuppressed PrivacySuppressed PrivacySuppressed PrivacySuppressed PrivacySuppressed PrivacySuppressed PrivacySuppressed 0 0 PrivacySuppressed PrivacySuppressed 0 0.246376811594 PrivacySuppressed PrivacySuppressed PrivacySuppressed PrivacySuppressed PrivacySuppressed PrivacySuppressed PrivacySuppressed 0 0 PrivacySuppressed PrivacySuppressed 0 0.575 PrivacySuppressed PrivacySuppressed PrivacySuppressed PrivacySuppressed PrivacySuppressed PrivacySuppressed PrivacySuppressed 0 0 PrivacySuppressed PrivacySuppressed 0 PrivacySuppressed PrivacySuppressed PrivacySuppressed PrivacySuppressed PrivacySuppressed PrivacySuppressed PrivacySuppressed PrivacySuppressed 0 0 PrivacySuppressed PrivacySuppressed 0 PrivacySuppressed PrivacySuppressed PrivacySuppressed PrivacySuppressed PrivacySuppressed PrivacySuppressed PrivacySuppressed PrivacySuppressed 0 0 0.4375 PrivacySuppressed 0 0.270833333333 PrivacySuppressed PrivacySuppressed PrivacySuppressed PrivacySuppressed PrivacySuppressed PrivacySuppressed PrivacySuppressed 0 0 PrivacySuppressed PrivacySuppressed 0 0.459459459459 PrivacySuppressed PrivacySuppressed PrivacySuppressed PrivacySuppressed PrivacySuppressed PrivacySuppressed PrivacySuppressed 0 0 0.324324324324 PrivacySuppressed 0 PrivacySuppressed PrivacySuppressed 0 PrivacySuppressed PrivacySuppressed PrivacySuppressed PrivacySuppressed 0 0 0 PrivacySuppressed 0 0 PrivacySuppressed PrivacySuppressed 0 PrivacySuppressed PrivacySuppressed PrivacySuppressed PrivacySuppressed 0 0 0 PrivacySuppressed PrivacySuppressed 0 PrivacySuppressed PrivacySuppressed 0 0 0 0 0 0 0 0 PrivacySuppressed PrivacySuppressed 0 PrivacySuppressed 0 0 PrivacySuppressed PrivacySuppressed PrivacySuppressed 0 0 0 0 PrivacySuppressed PrivacySuppressed 0 0 PrivacySuppressed 0 PrivacySuppressed PrivacySuppressed PrivacySuppressed 0 0 0 0 PrivacySuppressed PrivacySuppressed 0 PrivacySuppressed PrivacySuppressed 0 PrivacySuppressed PrivacySuppressed PrivacySuppressed PrivacySuppressed 0 0 0 PrivacySuppressed PrivacySuppressed 0 PrivacySuppressed 0 0 PrivacySuppressed PrivacySuppressed PrivacySuppressed 0 0 0 0 PrivacySuppressed PrivacySuppressed 0 PrivacySuppressed PrivacySuppressed 0 PrivacySuppressed PrivacySuppressed PrivacySuppressed PrivacySuppressed 0 0 0 PrivacySuppressed PrivacySuppressed 0 PrivacySuppressed PrivacySuppressed 0 PrivacySuppressed PrivacySuppressed PrivacySuppressed PrivacySuppressed 0 0 0 PrivacySuppressed PrivacySuppressed 0 PrivacySuppressed PrivacySuppressed 0 PrivacySuppressed PrivacySuppressed PrivacySuppressed PrivacySuppressed 0 0 0 PrivacySuppressed PrivacySuppressed 0 PrivacySuppressed PrivacySuppressed 0 PrivacySuppressed PrivacySuppressed PrivacySuppressed PrivacySuppressed 0 0 0 PrivacySuppressed PrivacySuppressed 0 PrivacySuppressed PrivacySuppressed 0 PrivacySuppressed PrivacySuppressed PrivacySuppressed PrivacySuppressed 0 0 0 PrivacySuppressed 0 0 PrivacySuppressed PrivacySuppressed 0 PrivacySuppressed PrivacySuppressed PrivacySuppressed PrivacySuppressed 0 0 0 PrivacySuppressed 0 0 PrivacySuppressed 0 0 PrivacySuppressed PrivacySuppressed PrivacySuppressed 0 0 0 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN PrivacySuppressed 0.948220065 PrivacySuppressed PrivacySuppressed 0.454901961 PrivacySuppressed PrivacySuppressed PrivacySuppressed PrivacySuppressed PrivacySuppressed PrivacySuppressed PrivacySuppressed PrivacySuppressed PrivacySuppressed PrivacySuppressed PrivacySuppressed PrivacySuppressed 0.043137255 0.411764706 0.545098039 0.294871795 0.125 0.070512821 PrivacySuppressed 69110.74519 46084.73263 163 78 72 13 PrivacySuppressed PrivacySuppressed 71 92 119 44 149 14 80 64 133 76 44 PrivacySuppressed PrivacySuppressed 122 51 72 95 38 PrivacySuppressed PrivacySuppressed 59 44 109 53 36 PrivacySuppressed PrivacySuppressed 95 28 67 69 40 PrivacySuppressed PrivacySuppressed 48 37 37 20 PrivacySuppressed PrivacySuppressed PrivacySuppressed PrivacySuppressed PrivacySuppressed 19 PrivacySuppressed PrivacySuppressed PrivacySuppressed PrivacySuppressed 17 PrivacySuppressed NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 10158 PrivacySuppressed 10174 9052 10500 PrivacySuppressed PrivacySuppressed 10345.5 10500 PrivacySuppressed 10500 9604.5 10500 8878 272 10 263 145 107 PrivacySuppressed PrivacySuppressed 254 189 PrivacySuppressed 114 148 127 102 PrivacySuppressed 272 24644 16092.5 5730.5 2998 PrivacySuppressed 16 293 PrivacySuppressed 255 312 16962 16548 235 289 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 312 0.93000000715255 0.74000000953674 32.9599990844726 1154.31994628906 0.87000000476837 0.51999998092651 0.68999999761581 0.05000000074505 0.25999999046325 0.44999998807907 47269.17 35283 46077.05859375 10.4700002670288 10.4499998092651 72.9800033569336 19.3799991607666 2.05999994277954 7.03000020980835 14.9300003051757 7.90000009536743 93.1299972534179 56889.14 10.0200004577636 3.53999996185302 10.8999996185302 1.54 0.70 0.18 0.06 0.04 0.03 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 5 23 PrivacySuppressed PrivacySuppressed PrivacySuppressed PrivacySuppressed PrivacySuppressed PrivacySuppressed PrivacySuppressed PrivacySuppressed PrivacySuppressed PrivacySuppressed PrivacySuppressed PrivacySuppressed PrivacySuppressed PrivacySuppressed PrivacySuppressed PrivacySuppressed PrivacySuppressed PrivacySuppressed PrivacySuppressed PrivacySuppressed PrivacySuppressed PrivacySuppressed PrivacySuppressed PrivacySuppressed NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 10158 PrivacySuppressed PrivacySuppressed NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN training

In [8]:
cols_target = ['md_earn_wne_p6']

cols_school = ['PREDDEG', 'HIGHDEG', 'CONTROL', 'NUMBRANCH', 'AVGFACSAL']
#                'PCIP01', 'PCIP03', 'PCIP04', 'PCIP05', 'PCIP09', 'PCIP10',
#                'PCIP11', 'PCIP12', 'PCIP13', 'PCIP14', 'PCIP15', 'PCIP16',
#                'PCIP19', 'PCIP22', 'PCIP23', 'PCIP24', 'PCIP25', 'PCIP26',
#                'PCIP27', 'PCIP29', 'PCIP30', 'PCIP31', 'PCIP38', 'PCIP39',
#                'PCIP40', 'PCIP41', 'PCIP42', 'PCIP43', 'PCIP44', 'PCIP45',
#                'PCIP46', 'PCIP47', 'PCIP48', 'PCIP49', 'PCIP50', 'PCIP51',
#                'PCIP52', 'PCIP54']

# cols_admissions = ['ADM_RATE', 'SATVR25', 'SATVR75', 'SATMT25', 'SATMT75', 'SAT_AVG']

cols_costs = ['TUITFTE']

cols_studentbody = ['UGDS', 'UGDS_NRA', 'PPTUG_EF', 'UG25abv',
                    'PAR_ED_PCT_1STGEN', 'DEP_INC_AVG', 'IND_INC_AVG',
                    'COMP_ORIG_YR2_RT', 'WDRAW_ORIG_YR2_RT', 'ENRL_ORIG_YR2_RT',
                    'COMP_ORIG_YR4_RT', 'WDRAW_ORIG_YR4_RT', 'ENRL_ORIG_YR4_RT',
                    'OVERALL_YR2_N', 'OVERALL_YR3_N', 'OVERALL_YR4_N',
                    'OVERALL_YR6_N', 'OVERALL_YR8_N', 'count_nwne_p6']

cols_financialaid = ['DEBT_MDN', 'GRAD_DEBT_MDN', 'WDRAW_DEBT_MDN']

cols_other = ['type']

In [9]:
data.shape


Out[9]:
(27974, 1730)

In [10]:
data_reduced = data[cols_target+cols_school+cols_costs+\
                    cols_studentbody+cols_financialaid+cols_other]

In [11]:
for c in data_reduced.columns:
    if (data_reduced[c].dtype == object) and (c != 'type'):
        data_reduced[c] = data_reduced[c].apply(lambda x: float(x) if x != 'PrivacySuppressed' else np.nan)
        data_reduced[c] = data_reduced[c].astype(float)

In [12]:
data_reduced.describe()


Out[12]:
md_earn_wne_p6 PREDDEG HIGHDEG CONTROL NUMBRANCH AVGFACSAL TUITFTE UGDS UGDS_NRA PPTUG_EF UG25abv PAR_ED_PCT_1STGEN DEP_INC_AVG IND_INC_AVG COMP_ORIG_YR2_RT WDRAW_ORIG_YR2_RT ENRL_ORIG_YR2_RT COMP_ORIG_YR4_RT WDRAW_ORIG_YR4_RT ENRL_ORIG_YR4_RT OVERALL_YR2_N OVERALL_YR3_N OVERALL_YR4_N OVERALL_YR6_N OVERALL_YR8_N count_nwne_p6 DEBT_MDN GRAD_DEBT_MDN WDRAW_DEBT_MDN
count 22394.000000 27974.000000 27974.000000 27974.000000 27974.000000 17094.000000 26958.000000 26084.000000 26084.000000 25961.000000 25830.000000 24461.000000 25166.000000 25303.000000 22303.000000 22473.000000 20116.000000 22184.000000 21849.000000 19316.000000 26230.000000 25815.000000 25535.000000 24948.000000 18614.000000 24423.000000 22847.000000 21627.000000 21315.000000
mean 29062.007681 1.857582 2.213877 2.118932 4.037070 5348.674798 11011.289970 2222.314944 0.014165 0.223849 0.413015 0.493549 52556.296987 22942.376603 0.278013 0.205554 0.238424 0.374718 0.205404 0.040188 2614.042242 2266.867209 1965.423497 1385.139370 1334.820297 249.651435 8994.602180 12483.039742 6405.177668
std 10822.399280 1.008607 1.274660 0.841436 12.977136 1935.658093 172001.637782 4855.392337 0.043849 0.240339 0.220127 0.135478 24402.757134 9926.116828 0.254066 0.135533 0.195519 0.243561 0.144668 0.050813 14366.819640 13225.712340 10592.497586 5225.296605 5179.278829 891.605076 4821.270423 7030.661573 3447.324241
min 7000.000000 0.000000 0.000000 1.000000 1.000000 42.000000 0.000000 0.000000 0.000000 0.000000 0.000400 0.000000 443.174960 215.042694 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 10.000000 10.000000 10.000000 10.000000 10.000000 0.000000 124.000000 654.000000 520.000000
25% 22000.000000 1.000000 1.000000 1.000000 1.000000 4088.250000 2537.750000 127.000000 0.000000 0.000000 0.253025 0.413534 33732.107442 16257.267275 0.062918 0.106195 0.060261 0.155203 0.101010 0.000000 119.000000 116.000000 114.000000 107.000000 111.000000 23.000000 5400.000000 6625.000000 3750.000000
50% 27500.000000 2.000000 2.000000 2.000000 1.000000 5160.000000 5909.000000 522.000000 0.000000 0.145700 0.411000 0.510294 49126.867425 21397.270020 0.160684 0.198300 0.204279 0.378328 0.195652 0.025147 450.000000 420.000000 407.000000 373.000000 369.000000 66.000000 7905.000000 11119.000000 5500.000000
75% 34400.000000 3.000000 4.000000 3.000000 2.000000 6376.000000 10877.750000 2067.000000 0.011200 0.386400 0.563400 0.581560 68785.735878 27541.043335 0.514689 0.293532 0.391884 0.571936 0.295954 0.066380 1350.000000 1230.000000 1139.500000 994.000000 968.000000 186.000000 12000.000000 17125.000000 8250.000000
max 133600.000000 4.000000 4.000000 3.000000 128.000000 24699.000000 26670163.000000 249604.000000 1.000000 1.000000 1.000000 1.000000 181008.007100 79375.209910 1.000000 0.781513 1.000000 1.000000 0.842161 0.694737 237888.000000 222715.000000 170316.000000 72057.000000 70824.000000 12567.000000 95984.000000 47186.500000 33125.000000

In [13]:
data_reduced.dropna(inplace=True)

In [14]:
data_reduced.shape


Out[14]:
(6717, 30)

In [15]:
6809 / 27974


Out[15]:
0.24340458997640665

In [16]:
data_reduced.type.value_counts()


Out[16]:
training    4173
testing     2544
dtype: int64

In [17]:
training = data_reduced[data_reduced['type'] == 'training']
testing = data_reduced[data_reduced['type'] == 'testing']

In [18]:
training.md_earn_wne_p6.values


Out[18]:
array([ 25700.,  35100.,  40600., ...,  36500.,  36500.,  36500.])

In [19]:
X_train = normalize(training.iloc[:, 1:-1].values)
y_train = training.md_earn_wne_p6.values

In [20]:
X_test = normalize(testing.iloc[:, 1:-1].values)
y_test = testing.md_earn_wne_p6.values

In [21]:
names = data_reduced.columns[1:-1]

In [22]:
len(names)


Out[22]:
28

Random Forest


In [23]:
rf = RandomForestRegressor(random_state=1868)
rf.fit(X_train, y_train)


Out[23]:
RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,
           max_features='auto', max_leaf_nodes=None, min_samples_leaf=1,
           min_samples_split=2, min_weight_fraction_leaf=0.0,
           n_estimators=10, n_jobs=1, oob_score=False, random_state=1868,
           verbose=0, warm_start=False)

In [24]:
print("Features sorted by their score:")
print(sorted(zip(map(lambda x: round(x, 2), rf.feature_importances_), names), reverse=True))


Features sorted by their score:
[(0.20999999999999999, 'count_nwne_p6'), (0.19, 'NUMBRANCH'), (0.10000000000000001, 'CONTROL'), (0.089999999999999997, 'PAR_ED_PCT_1STGEN'), (0.059999999999999998, 'PREDDEG'), (0.040000000000000001, 'GRAD_DEBT_MDN'), (0.040000000000000001, 'AVGFACSAL'), (0.029999999999999999, 'WDRAW_ORIG_YR4_RT'), (0.02, 'WDRAW_ORIG_YR2_RT'), (0.02, 'TUITFTE'), (0.02, 'IND_INC_AVG'), (0.02, 'DEP_INC_AVG'), (0.02, 'DEBT_MDN'), (0.02, 'COMP_ORIG_YR4_RT'), (0.01, 'WDRAW_DEBT_MDN'), (0.01, 'UGDS'), (0.01, 'UG25abv'), (0.01, 'PPTUG_EF'), (0.01, 'OVERALL_YR8_N'), (0.01, 'OVERALL_YR6_N'), (0.01, 'OVERALL_YR3_N'), (0.01, 'OVERALL_YR2_N'), (0.01, 'HIGHDEG'), (0.01, 'ENRL_ORIG_YR4_RT'), (0.01, 'ENRL_ORIG_YR2_RT'), (0.01, 'COMP_ORIG_YR2_RT'), (0.0, 'UGDS_NRA'), (0.0, 'OVERALL_YR4_N')]

In [25]:
yhat = rf.predict(X_test)

In [26]:
mean_squared_error(y_test, yhat)**0.5


Out[26]:
8085.3402528722918

In [27]:
plt.figure(figsize=(12, 12))

sns.regplot(x=y_test, y=yhat, color='#348ABD');

plt.title('Predicted versus Actual Earnings')
plt.xlabel('Actual')
plt.ylabel('Predicted')
plt.xlim(0, 100000);
plt.ylim(0, 100000);

plt.gca().get_xaxis().set_major_formatter(
    mpl.ticker.FuncFormatter(lambda x, p: format(int(x), ','))
)
plt.gca().get_yaxis().set_major_formatter(
    mpl.ticker.FuncFormatter(lambda y, p: format(int(y), ','))
)


Multiple Regression


In [28]:
features_use = ['count_nwne_p6', 'NUMBRANCH', 'CONTROL', 'PAR_ED_PCT_1STGEN',
                'PREDDEG', 'GRAD_DEBT_MDN', 'AVGFACSAL', 'WDRAW_ORIG_YR4_RT',
                'WDRAW_ORIG_YR2_RT', 'TUITFTE', 'IND_INC_AVG', 'DEP_INC_AVG',
                'DEBT_MDN', 'COMP_ORIG_YR4_RT']

In [29]:
data_reduced = data[cols_target+features_use+cols_other]

for c in data_reduced.columns:
    if (data_reduced[c].dtype == object) and (c != 'type'):
        data_reduced[c] = data_reduced[c].apply(lambda x: float(x) if x != 'PrivacySuppressed' else np.nan)
        data_reduced[c] = data_reduced[c].astype(float)

data_reduced.dropna(inplace=True)

training = data_reduced[data_reduced['type'] == 'training']
testing = data_reduced[data_reduced['type'] == 'testing']

X_train = scale(training.iloc[:, 1:-1].values)
y_train = training.md_earn_wne_p6.values

X_test = scale(testing.iloc[:, 1:-1].values)
y_test = testing.md_earn_wne_p6.values

OLS


In [30]:
ols = LinearRegression()

In [31]:
ols.fit(X_train, y_train)


Out[31]:
LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)

In [32]:
yhat = ols.predict(X_test)

In [33]:
mean_squared_error(y_test, yhat)**0.5


Out[33]:
7162.4807037804103

In [34]:
plt.figure(figsize=(12, 12))

sns.regplot(x=y_test, y=yhat, color='#348ABD');

plt.title('Predicted versus Actual Earnings')
plt.xlabel('Actual')
plt.ylabel('Predicted')
plt.xlim(0, 100000);
plt.ylim(0, 100000);

plt.gca().get_xaxis().set_major_formatter(
    mpl.ticker.FuncFormatter(lambda x, p: format(int(x), ','))
)
plt.gca().get_yaxis().set_major_formatter(
    mpl.ticker.FuncFormatter(lambda y, p: format(int(y), ','))
)


Random Forest


In [35]:
rf = RandomForestRegressor(random_state=1868)
rf.fit(X_train, y_train)

yhat = rf.predict(X_test)
mean_squared_error(y_test, yhat)**0.5


Out[35]:
6754.289052133895

In [36]:
plt.figure(figsize=(12, 12))

sns.regplot(x=y_test, y=yhat, color='#348ABD');

plt.title('Predicted versus Actual Earnings')
plt.xlabel('Actual')
plt.ylabel('Predicted')
plt.xlim(0, 100000);
plt.ylim(0, 100000);

plt.gca().get_xaxis().set_major_formatter(
    mpl.ticker.FuncFormatter(lambda x, p: format(int(x), ','))
)
plt.gca().get_yaxis().set_major_formatter(
    mpl.ticker.FuncFormatter(lambda y, p: format(int(y), ','))
)



In [ ]: