Initial deployment of CausalBox app

Dockerfile

@@ -1,21 +1,38 @@
+# Use a Python base image with a good balance of size and features
 FROM python:3.9-slim
 
+# Set the working directory inside the container
 WORKDIR /app
 
-RUN apt-get update && apt-get install -y \
-    build-essential \
-    curl \
-    software-properties-common \
-    git \
-    && rm -rf /var/lib/apt/lists/*
+# Install system dependencies (Nginx, etc.)
+# Run apt-get update first, then install packages, then clean up apt cache
+RUN apt-get update && \
+    apt-get install -y --no-install-recommends nginx && \
+    rm -rf /var/lib/apt/lists/*
 
-COPY requirements.txt ./
-COPY src/ ./src/
+# Copy the combined requirements file and install Python dependencies
+COPY requirements.txt .
+RUN pip install --no-cache-dir -r requirements.txt
 
-RUN pip3 install -r requirements.txt
+# Copy your Flask backend and Streamlit frontend code
+COPY flask_backend/ ./flask_backend/
+COPY streamlit_frontend/ ./streamlit_frontend/
+# If you have a sample dataset you want to include
+COPY data/ ./data/
 
-EXPOSE 8501
+# Copy Nginx configuration and startup script
+COPY nginx.conf /etc/nginx/sites-available/default
+COPY start.sh .
 
-HEALTHCHECK CMD curl --fail http://localhost:8501/_stcore/health
+# Ensure Nginx uses our config
+RUN ln -sf /etc/nginx/sites-available/default /etc/nginx/sites-enabled/default && \
+    rm -rf /etc/nginx/sites-enabled/default.bak
 
-ENTRYPOINT ["streamlit", "run", "src/streamlit_app.py", "--server.port=8501", "--server.address=0.0.0.0"]
+# Make the startup script executable
+RUN chmod +x start.sh
+
+# Expose the port Nginx will listen on (Hugging Face Spaces will expose this to the internet)
+EXPOSE 7860
+
+# Command to run on container startup
+CMD ["./start.sh"]

LICENSE

@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2025 Arya Patil
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

README.md

@@ -1,20 +1 @@
----
-title: CausalBox
-emoji: 🚀
-colorFrom: red
-colorTo: red
-sdk: docker
-app_port: 8501
-tags:
-- streamlit
-pinned: false
-short_description: This project is designed for data scientists, researchers, a
-license: mit
----
-
-# Welcome to Streamlit!
-
-Edit `/src/streamlit_app.py` to customize this app to your heart's desire. :heart:
-
-If you have any questions, checkout our [documentation](https://docs.streamlit.io) and [community
-forums](https://discuss.streamlit.io).
+# casualbox

data/sample_dataset.csv

@@ -0,0 +1,1001 @@
+StudyHours,TuitionHours,ParentalEducation,SchoolType,FinalExamScore
+10.993428306022466,6.399355436586002,Medium,Public,91.29923038773146
+9.723471397657631,5.924633682912769,Low,Public,75.91360822304543
+11.295377076201385,5.059630369920174,Medium,Public,69.87670713396345
+13.046059712816051,4.353063222294426,Low,Public,85.51675275982006
+9.531693250553328,5.69822331361359,Low,Public,84.56054181454806
+9.531726086101639,5.39348538542175,Low,Public,75.63488134273703
+13.158425631014783,5.895193220027732,Medium,Private,81.63445369137285
+11.534869458305817,5.635171801681969,High,Private,79.3361995922461
+9.061051228130095,6.049552715319335,High,Private,81.44745836489537
+11.08512008717193,4.464764788439432,Medium,Public,76.5950826505781
+9.073164614375075,6.317394065634326,High,Private,79.26622182891586
+9.068540492859487,5.1975996046924,High,Public,76.07249497145726
+10.48392454313207,7.0752608726252655,Low,Public,81.20384233308822
+6.173439510684404,4.310812181910432,Low,Public,72.31955892799562
+6.5501643349739345,6.73596380316525,Medium,Public,70.81137487537477
+8.875424941518055,5.197910783462648,Medium,Private,73.39478325731868
+7.974337759331153,4.348581996385551,High,Public,76.45956276323939
+10.628494665190548,4.5161141659456785,Low,Private,73.09653395845635
+8.183951848957578,4.67965269180568,Medium,Public,68.98628178151203
+7.175392597329417,5.424165946401916,Medium,Private,66.18546265754375
+12.931297537843108,5.5228354880355,Medium,Private,88.39190287412222
+9.548447399026928,4.426299996061421,Medium,Private,75.21015995173964
+10.135056409375848,4.975645407739017,Medium,Private,71.16550519996488
+7.150503627573086,7.142270358611864,Medium,Private,79.28345560460932
+8.911234550949635,6.727543170100711,High,Private,75.13607304917315
+10.221845179419732,5.4363236696740325,Low,Public,74.23978546916608
+7.698012845155395,5.0380034781682,Low,Private,71.97463811317833
+10.751396036691343,5.120031326718261,Medium,Public,86.57645711079954
+8.79872262016239,5.613517972730417,Low,Private,74.04291121693757
+9.416612500413446,3.9772074348015947,Low,Public,77.67573047360722
+8.796586775541206,4.742623462576655,Low,Private,78.34250884720215
+13.704556369017876,3.3314159261224106,Medium,Private,84.6226031172514
+9.973005550524132,5.399223122605279,Medium,Public,84.4002040333275
+7.884578142088199,5.647195939702741,Low,Private,70.70437017056912
+11.645089824206378,4.516813537700626,Low,Private,74.84872573971201
+7.5583127000579555,6.573986763290033,Medium,Private,69.66277226492598
+10.417727190009511,3.774234336997659,High,Public,75.77446380901655
+6.080659752240448,3.53562511979018,Low,Private,62.72952389496353
+7.3436279022031385,5.224451818559972,High,Public,73.26163809157288
+10.393722471738247,6.047098302612154,Low,Private,80.24303449601918
+11.476933159990821,6.6839276914578685,High,Private,88.07262437309865
+10.34273656237994,4.54111573713106,High,Public,78.95286827163666
+9.76870343522352,6.078680833431296,High,Private,76.57466884128674
+9.397792608821423,4.961491530499293,Medium,Private,78.29206853555519
+7.042956019265145,4.82737270005918,High,Public,66.17600098912936
+8.560311583210582,5.883659937486672,High,Private,75.75540963252959
+9.078722458080424,5.65232287841342,High,Public,78.23293540175888
+12.11424445243783,3.4236078430157506,Low,Public,80.01048866014992
+10.687236579136922,6.476540349725896,High,Public,83.1646091575426
+6.473919689274532,6.380091354147451,Low,Private,69.29774633120628
+10.648167938789591,4.374437298490471,High,Public,83.61814883936246
+9.229835439167367,5.395803533437409,High,Public,85.66422092546938
+8.646155999388082,5.494030186282739,Medium,Private,67.51997655390731
+11.223352577681736,5.260673765823907,Low,Public,74.25972582432809
+12.061999044991902,4.44969484615458,High,Private,81.01395539933759
+11.862560238232398,4.328376632061948,Low,Private,78.93493052179335
+8.321564953554724,4.974445929008555,Low,Private,83.5868608294862
+9.38157524829757,6.172729019259364,Medium,Private,79.12116564260648
+10.662526862807129,5.543600154594432,Medium,Public,86.60318022613569
+11.951090254244718,4.629385667913894,Medium,Public,70.60063957809228
+9.04165152430942,5.771698710648344,Medium,Public,80.81169853125313
+9.628682046672365,2.1514573793699245,Medium,Public,68.90696606610801
+7.787330051987944,6.1487657003721035,Medium,Public,71.14009580643575
+7.607586751838658,3.2602862212219,Medium,Private,68.86381270344955
+11.625051644788396,4.637559058596868,Medium,Public,73.08352367881665
+12.712480057141645,3.880330105373856,High,Public,83.45938289895334
+9.855979756839332,3.705318524279094,Low,Public,76.22351213368128
+12.007065795784047,6.16082678737852,Medium,Private,84.58633301357222
+10.723272050095268,4.5322987986881085,Medium,Private,82.68014740888722
+8.709760490789751,5.346503881730715,Low,Public,83.01318703514633
+10.722791211016828,4.953079421120816,High,Private,77.05664630827714
+13.076073132931938,5.477040827223122,Low,Public,86.75446070538094
+9.928347921780096,5.076821891060256,Low,Public,78.93686972182546
+13.129287311628012,3.7170077758114304,Medium,Private,83.72215881861787
+4.760509791820511,5.99626681944714,Low,Private,70.47840813514165
+11.643805008750448,4.506243416838688,Low,Public,87.99949186558312
+10.174094136476343,3.443418101340249,Medium,Public,73.21493108251659
+9.401985299068265,4.571884839034087,Medium,Private,68.03311834066925
+10.183521553071005,6.50075979063431,Medium,Public,76.62881394901405
+6.024862170798214,5.850221742113493,Medium,Private,74.61139162244369
+9.560656224324976,4.6513478655921405,Low,Public,76.21498179027404
+10.714225143023492,4.650742295681571,Low,Private,76.06656702709435
+12.955788089483033,4.678364948782617,High,Public,77.54663726009179
+8.963459563452705,7.076747983560841,High,Public,79.64847171089932
+8.383012794213625,5.381935452231553,Low,Private,78.06146014314363
+8.996485912830927,5.43004164719107,High,Public,77.66739457192861
+11.830804235404148,6.030283454031842,Low,Public,81.44038199608131
+10.657502219319369,5.238789159026513,High,Private,79.90639459866581
+8.940479592465923,4.740957854179297,Medium,Public,75.69661079898786
+11.026534866226712,4.803650150915022,High,Public,80.20702961491426
+10.194155098696081,4.928398740585936,Medium,Public,71.85938355317546
+11.937289981065778,4.962777763490158,Medium,Private,82.30537836044118
+8.595893812245295,5.72762954363698,Medium,Private,73.5802495976151
+9.344675706804464,5.051945885807299,Medium,Private,81.47036327236867
+9.215783693735684,5.73264007721558,High,Public,77.69512071204612
+7.072970103735763,4.919283419891418,Low,Public,75.70226144642311
+10.592240554129152,5.07863519031609,Medium,Public,77.29352695848367
+10.522110544359778,3.0017993154678924,High,Public,74.21835087386411
+10.010226913284923,5.916327674702481,Medium,Private,86.36620156990523
+9.530825733249706,5.346488475897993,High,Public,79.26306012242395
+7.1692585158991715,5.998010109859652,High,Public,69.76295953455913
+9.158709354469282,2.1037446218063107,High,Public,75.9579078347504
+9.31457096694646,7.088374704780728,Low,Private,76.59518032392467
+8.395445461556761,4.860410371844826,High,Public,75.20999716993838
+9.677428576667982,6.108182816737516,High,Public,87.70050453273926
+10.808101713629076,3.960094072874469,Low,Public,70.39993259837959
+13.77237180242106,5.61277390506257,Low,Public,85.08499485360329
+10.349155625663679,3.9465844368263436,High,Public,78.65954611107702
+10.515100781445529,4.376231039304195,Low,Private,81.41803979517071
+9.851108168467665,6.914031353866786,Medium,Public,73.86709383093114
+6.1624575694019175,4.809317599236653,Medium,Public,61.859940194783725
+9.946972249101567,5.217432873179723,Low,Public,83.17384056792484
+10.120460419882052,5.8700677306887545,Low,Public,75.44394815177805
+14.926484224970572,5.495681887972605,High,Public,84.28507874191348
+9.615278070437755,5.150418905143576,Low,Public,84.70743286245171
+10.603094684667225,5.3649610024662255,High,Public,79.6021424466929
+9.930576460589513,7.403415585238275,Medium,Public,81.34746743744431
+7.6626439247609355,4.9423812029664145,Low,Public,71.83986242821382
+12.285645629030041,5.201099046714967,High,Public,76.25302949441668
+11.503866065373549,6.050654396007612,High,Private,85.46219847173046
+11.582063894086094,6.105525932957984,Medium,Private,84.36966929723727
+8.181225090410521,6.187030305560381,Low,Private,76.48645226585877
+12.805588621872198,5.638730222029185,Medium,Private,83.48765738040687
+7.196297874415438,3.8569950872695165,High,Public,68.7164901330437
+11.17371418760054,6.6334315323010955,High,Private,86.8599926213898
+14.380911251619956,3.853654606981312,Low,Private,83.8358390976814
+8.018927349738624,5.3026354652390335,High,Public,73.67150270466635
+8.867404540794457,4.245724149980642,Low,Private,76.75286108097875
+10.199302730175283,4.935861653407008,Medium,Private,78.84220259386301
+8.993048691767601,5.32876241030348,Low,Public,84.19062128669262
+6.898673137867735,5.3213572154502735,Low,Public,62.972548211544364
+10.137125949612054,5.421920754235285,High,Public,79.90667022241335
+7.87539257254779,6.613711269058647,High,Private,72.1433231463227
+10.947184861270363,5.453534301513979,Low,Private,75.36423850103705
+8.161151531532393,4.755843364709351,High,Public,71.17074707990709
+13.09986881003508,5.964087168288358,High,Public,85.4192654149115
+8.433493415327526,6.18947048892464,High,Private,76.64653794365906
+9.35587696758865,3.7723921848630075,Low,Public,66.78381113407934
+11.62703443473934,5.597400069849858,High,Private,78.94474982586438
+7.53827136713209,5.701172742310899,Low,Public,75.03440960511283
+10.45491986920826,4.702436496640008,High,Public,73.57884222945826
+12.614285508564857,6.37570681331162,Medium,Private,79.21821171351456
+6.785033530877545,4.849944412967473,High,Public,59.56432214374158
+10.36926771706461,5.125576453471543,High,Public,75.11093287245974
+10.519765588496847,4.82692817571613,Low,Public,72.53177744581382
+11.56364574355462,5.015579047581467,Low,Private,75.73099564293115
+7.526098578243836,3.9037249131047984,High,Public,69.5486830097466
+7.359086773831447,3.5599491174057056,Medium,Public,67.05895284105696
+11.043883131233795,6.594505063224972,High,Public,76.14833438800852
+10.593969346466372,4.153038651681673,High,Public,66.83828884428911
+10.500985700691754,4.008607650543693,Medium,Public,78.52643940293727
+10.692896418993952,2.846609886396285,Low,Public,79.4198437366247
+8.639950556843019,4.361038252310498,High,Private,71.8377335971799
+10.464507394322007,3.6769102065925017,Low,Private,75.11550161626137
+10.586144946597363,6.642015160136369,Low,Public,81.1493652118299
+8.571297163947264,6.009817089342635,Medium,Private,84.05123784496975
+13.731549022289514,4.311849654822276,Medium,Private,76.09233355768359
+10.947665841823575,7.252435805344309,Low,Public,80.70153676230076
+7.617393005594703,5.981765486991594,High,Private,65.5975694210132
+11.31310721726766,4.675168616491377,Low,Private,81.45055965295018
+8.050636659545358,2.500594285126427,High,Private,69.1323727778403
+11.574169207484903,7.290942572590742,High,Private,85.0997128385363
+12.317191158014808,3.610427533236404,Medium,Private,80.8156364288396
+8.358635363296578,3.354601253043108,High,Private,76.7027138746754
+11.926752258488644,6.0225704320096405,High,Private,85.57802498783408
+10.825561853872996,7.439752406339273,Low,Private,86.96329210555535
+11.64412031998898,6.384272818510518,Medium,Public,77.53697641825902
+13.793585965307894,5.56390912005236,High,Public,91.89888993557005
+9.509223767994259,5.594754341665286,High,Private,73.79876535104144
+8.492527671285021,5.853415558780307,Medium,Private,72.44083096762058
+8.220971140748954,5.758928589786765,Low,Public,76.43454805294303
+8.368379430069123,5.281191423969376,Medium,Private,76.1681373113387
+9.845796581171792,5.104201103951876,Medium,Private,71.98861080710901
+10.682303949633289,4.937406872100477,High,Public,77.9043217485478
+10.553381598660039,4.246035411133324,High,Public,84.91699289836744
+11.654366498072047,4.719324923133226,Low,Public,73.04603757317346
+10.026003783755813,3.3070431858046145,High,Private,82.43866855128218
+12.907068154314633,4.90166037320525,High,Private,81.95813461135468
+9.470686333524087,4.011408892849083,Medium,Public,63.26971183914603
+15.440338333179238,3.896410682784229,High,Public,79.8916340231203
+11.251334695530012,5.179894151153478,Low,Private,75.45512292911428
+8.285684887167434,6.392002286344599,Medium,Public,73.03972669993806
+7.858215003877776,5.918316606002313,Low,Private,74.07033862363232
+10.96494483048637,3.4294993963768228,Medium,Private,63.55673810673012
+9.553074429348298,4.010371863429662,High,Public,76.05572861622936
+11.428000988184184,5.940771187988216,Medium,Public,76.49170621672627
+10.94647524914709,4.017512606461691,Medium,Private,84.87183226599201
+9.854342174686254,4.775366850002976,High,Public,75.52085102065709
+8.30641256386319,5.550052099024552,High,Public,79.65213413492687
+6.970305550628271,4.031655545136365,Low,Public,75.53983931853256
+9.106970095865957,5.10537550614596,Low,Public,81.28836966828194
+11.712797588646945,3.6659745050817953,High,Private,76.09252322010416
+10.428187488260408,4.398632356048786,High,Private,74.55832272198738
+7.508522442576024,5.3197819341425365,High,Private,71.87185128992428
+10.346361851702364,3.407006266468331,Medium,Private,78.98441998000185
+10.770634759457673,5.440474737960399,Medium,Public,77.14850527708512
+8.232285127597734,4.980362201073318,Medium,Public,73.3388011888542
+10.307450211891055,5.5524899544171475,High,Private,80.31160443710507
+10.116417436892,5.223914134064661,Low,Private,73.54424644464166
+7.714059404338753,6.364140429973844,High,Public,70.13378801199269
+10.715574720696566,5.1252245026479075,Medium,Private,78.32766678834504
+11.121569052736469,4.570594458874747,Medium,Private,78.96992386854328
+12.166102486350553,5.122297503032284,High,Public,84.18507862725775
+12.107604104069805,5.543298029036388,High,Private,85.88958812645198
+7.244661264085818,5.048860070325705,Medium,Public,66.56729853080195
+8.124349920169754,5.040591691203883,Medium,Public,69.74916271742751
+11.03007053441732,4.298008312193245,Medium,Private,73.653392099309
+11.027571901824418,4.337099082449052,High,Public,85.62302943527952
+11.030095372612095,3.5973947282003067,Medium,Private,76.85492988651198
+17.70546298130944,6.749576743186939,Medium,Public,95.61593755362438
+11.141781021386334,3.7561367646087147,Low,Private,78.20663900975332
+12.271131280361198,4.307094802204093,High,Private,77.42455469544034
+11.908003526986406,4.281592734430802,Low,Private,88.05974375034917
+11.302782502611596,5.8949243769444415,Low,Public,83.50308484473348
+9.369461510719308,4.705050321707972,Medium,Public,75.9944511528903
+11.517938440986535,6.247742072673734,High,Private,76.88120788523084
+8.454349570924856,4.326509375756132,Medium,Public,67.58880074720268
+9.526362786519982,5.278994162223802,High,Private,75.42667915834683
+9.029272904341793,4.164652946740243,Low,Private,70.80556626093481
+10.163748278772646,7.145149127773388,High,Public,75.70799852147262
+14.629317133347017,3.812401580970487,Medium,Private,83.12244654029199
+6.265469614816504,5.309820710227028,Medium,Public,69.41353015359411
+11.372520380749027,5.633776880932098,Medium,Private,81.90463318519754
+6.774568257620697,5.4137990974514985,High,Public,69.15023780454733
+9.056136268421133,4.814712341122291,Low,Private,70.29633757522555
+12.177901193934732,4.870179300266574,Low,Private,76.79646690831309
+10.128560038190926,5.04381147197911,High,Public,83.88770093048146
+7.844510444141388,4.852997998371511,Medium,Private,66.70972945403133
+8.569392581480063,5.96387911679491,Low,Private,77.90051532960884
+11.35919549786935,7.210523001591351,Medium,Public,86.13190714699772
+8.539266736565727,4.442508214992166,High,Private,73.13591841644137
+10.43291717916395,3.630197020420922,Medium,Public,85.15027518098518
+10.091143679807628,4.911717951648745,Low,Private,80.05845925222688
+8.696799304788366,7.579709337654318,Low,Public,87.97767785758145
+14.28788817865065,4.196325432030269,Medium,Private,81.61494449577233
+11.267838044636022,6.6391168054440675,High,Private,79.32462424797104
+5.949714826684786,6.677700814076441,Medium,Private,74.55366666788431
+10.372908629538856,4.4464117582920055,High,Public,76.10810670991971
+8.676427070463223,5.5689830791852755,Medium,Public,84.13465588416443
+11.704866669592448,6.628396623128538,Low,Public,85.06083019203238
+8.4149585231346,4.620872259012513,High,Public,68.5383543355044
+9.770527117066202,4.796419639897638,High,Private,68.05160165125872
+11.009974557960915,4.418319086051357,High,Private,78.25623478328254
+11.731510388340244,3.985243269307133,Low,Public,78.995270032975
+7.599407185888447,4.350722454933993,Medium,Public,73.29372767071982
+9.330997528318104,3.7760597332377435,Low,Public,80.37953752527234
+9.050109377678087,5.034083468259226,High,Private,81.04691047771465
+8.693341534852577,4.230026768941358,Medium,Public,70.83721806143504
+13.530908480562193,5.233785911790748,Low,Public,78.4433988197884
+10.80996342192191,3.444104353796373,High,Private,70.61547769949303
+7.478232091329909,5.330880232214457,High,Private,69.26877600437462
+11.835723894109552,5.833528961608924,Low,Public,80.58808349043738
+14.244312394025266,3.0062643574285444,Low,Private,84.52333201901918
+12.064930521102294,5.374056569847572,Low,Public,78.18610791249606
+6.961260068091973,6.227668992473292,High,Private,74.66598348046047
+9.031531854267497,3.7903589813087946,Low,Public,75.55880290979344
+12.533822298373245,6.672572386837704,High,Public,85.79322258998249
+8.584661068762438,5.419019009366045,Low,Public,69.65473566651046
+10.887638856292456,4.294988144241591,High,Public,80.55689355512709
+11.549268106858673,4.944230922342294,High,Private,90.9212307410644
+8.146139056843834,5.558326912521722,Medium,Private,75.85925261703478
+9.8809492878764,5.076005391412411,Low,Private,62.610531159615064
+3.517465319861855,5.538755992446369,Low,Private,71.54746248862777
+7.951224717331421,4.079326406920634,High,Public,68.50770016154686
+9.49486369721368,5.169360824036078,High,Public,88.07233208295995
+7.504433636070301,3.58628550309502,High,Private,70.97011278361295
+13.26482260786327,4.888773938651299,Medium,Private,75.43930265377435
+7.139717244078735,4.09609235853521,Medium,Private,60.100085576166535
+9.119911026606033,4.264470057410233,High,Private,82.33523095270681
+10.261481154572182,6.236093175210438,Low,Private,85.40662700154638
+12.882546578132231,6.091310120594269,Low,Public,96.03215276598932
+7.128275697641121,5.609138120907177,Medium,Public,72.72488100451078
+12.32632750430992,3.907687235327421,Medium,Public,80.23153644471256
+10.020466122039174,4.683591550305198,High,Private,75.15272560874337
+8.036982697904097,6.213097699979183,Low,Private,72.10216073948087
+10.92420694852654,5.141716913639905,High,Private,77.59884149522387
+10.39811939114694,7.319329539981695,Medium,Private,82.86254975388658
+8.79956624568241,5.3933178393943715,Low,Public,71.46960531589588
+10.139604169980037,5.192049116490032,Low,Public,69.7208352803252
+9.22937280627648,4.690883535797498,Medium,Private,76.29445408632009
+10.227034690502496,5.133540904782282,High,Public,73.82340274471125
+11.324261349042093,4.8475301636194565,High,Public,63.72262931881302
+13.172033632290704,5.708108676688591,Low,Private,87.66906723289937
+7.524369002346302,5.956702316794584,Medium,Private,68.44349628929935
+14.266066749312532,4.21401053949205,Low,Private,88.1620965813168
+6.095824400954996,3.668767046442222,Medium,Private,70.88403990415323
+9.696429809928834,3.1637946268967037,High,Private,75.55650161781688
+11.176634412969154,5.507991326912544,Medium,Private,93.0993450180896
+10.561983735470065,3.8966333938261264,High,Public,76.27865831181744
+8.754600960358813,2.847109407047517,Medium,Public,70.35166819379596
+9.58375549928545,5.388578604530964,Low,Private,80.66874101245195
+9.013998130682335,7.492999517432791,High,Public,78.9902587269449
+8.821270486111576,4.993929088460106,Low,Private,80.93590229188335
+11.69920419404205,5.838490774451642,Low,Private,75.81211501188395
+10.714030971930095,5.081829358547565,High,Private,72.78993374435984
+8.614180809478691,4.901110346808857,High,Private,78.40907459580175
+11.799199750866501,5.9190764829877685,Low,Public,76.65295952952164
+10.614599041753218,4.709725455811348,Low,Private,81.33927929943702
+11.62572423767792,5.267392314071903,Low,Public,83.00255117751553
+11.259257683847224,5.321697806196013,Low,Private,77.02862064685014
+8.342009978155856,4.331909546356951,High,Private,75.01773507701431
+8.879637919606061,5.992042349536523,High,Private,70.57437713459572
+11.494587210246523,4.825040243304591,Low,Public,75.22723515413144
+11.22074053086693,4.244254840507155,Medium,Private,77.62192463043426
+9.958196812071703,5.536509843570403,Medium,Private,78.61579748486011
+10.234654766617563,4.101532018208963,High,Private,75.10341487560929
+12.555329791576849,5.028181157369128,Low,Public,84.81851113857296
+8.81685722232834,4.990881003355796,Low,Public,77.06236802493183
+11.094194762340075,6.085895565831578,High,Public,88.96406691516823
+9.595614695132213,5.474698232879953,Medium,Private,76.92685000385933
+9.564637593545559,4.974973041254856,Medium,Public,82.56455190489403
+12.19755370397438,5.817766299200287,Medium,Private,87.72538442121865
+11.65083269797606,6.390207549017314,High,Public,94.35686313594383
+11.627019272001277,5.557810306351862,Medium,Public,80.95446390401898
+12.610957614308658,5.01035261984338,High,Private,80.63565055111745
+10.042007683265519,3.6881637676498404,High,Public,81.90473212922447
+11.363905942589927,3.9348863369246274,Low,Private,80.5573042492731
+9.379466486813088,4.694775295645332,Medium,Private,82.20939200101367
+10.648332704976884,4.390487797840716,High,Public,81.46639109666233
+9.73971389126463,4.813028697014554,Medium,Private,66.16516037105362
+10.193991929985437,5.0566499248971075,Medium,Private,79.85456763247245
+11.190314050873827,5.529692754356731,Low,Public,82.23124672304019
+8.363558633533055,4.9295012219409085,Medium,Public,74.78690361229728
+14.184774551370921,5.486501643826202,Low,Private,85.93808629786422
+7.987965237000596,5.06447441481012,High,Public,65.61192445724473
+7.571622774424536,3.0245334336461287,Medium,Private,74.05093028550988
+12.316221747000135,4.060664606181246,High,Private,82.59228254373961
+11.583325387925871,4.855912444278385,Medium,Public,85.96030292925643
+11.24823963410431,3.7903052571588924,High,Public,70.48815953257069
+11.25669101852856,5.599928729996204,Medium,Public,83.44894757977777
+9.97550645430617,6.530750832343697,Medium,Public,78.57384246939507
+8.205491257028337,6.218761851697317,Medium,Private,76.4580653412303
+10.151609116387453,4.786557129069792,Medium,Private,76.36725575547514
+8.645676576975777,6.490726136849952,Medium,Public,79.74548199662713
+11.950239466835502,5.148667457202111,Medium,Public,80.80911451451627
+9.705885236995723,4.662914028881961,Medium,Private,71.14656689203595
+8.349005606414977,4.38659733645906,Medium,Private,73.7100343114495
+9.357228316694012,4.697530313511872,Medium,Private,74.68987941797833
+10.82586290855125,4.611823181458771,High,Private,78.15249248364624
+8.87255089439205,5.170416222375488,Low,Public,59.561668742146615
+8.355559208867136,5.160573981049865,Medium,Private,77.8651598071526
+10.487374422983825,5.003046020153414,Low,Private,76.90599978946386
+10.489933142217446,5.43693816974635,Medium,Public,72.39450566896309
+8.98611364925774,6.190646274786341,Medium,Private,81.8710142949357
+9.057923388763355,5.949554135441324,High,Private,80.22200661945335
+10.464099874715272,3.5151020315500965,High,Public,74.56425181538141
+7.103831317005351,2.446078865098045,Low,Public,62.11593403405768
+7.1850724512468895,5.934319911239274,High,Public,74.16177844274955
+8.563111557495128,3.6331213015845787,High,Private,73.19143549030517
+9.573105696576306,4.775234598107317,Low,Private,80.9307347052725
+10.621815131196009,3.8298869739406403,High,Public,74.76978314900053
+12.950712433899104,3.1980195624199803,Medium,Public,86.67306488575316
+11.71531924640404,5.541462728204788,Low,Private,79.04156756843642
+9.680122940073145,5.7591551603026465,Low,Public,71.26981969788815
+9.961967584194623,4.423489599273281,Medium,Public,70.89775578991276
+7.994941270724382,2.40895770785509,High,Public,69.29126140755332
+9.96297372801522,4.453755551747517,Low,Private,76.27900823835806
+9.422682722159724,5.3918040054025465,Low,Private,78.57237715617714
+10.64543712067618,3.5210884312258712,Medium,Private,76.12063673259895
+8.345538112895355,5.183359919779155,Medium,Private,79.3527086857811
+11.038693028482344,4.984690150827005,High,Private,78.24895079089231
+13.065477826005155,5.579291499601088,Low,Private,74.51851157705488
+9.782479703086285,5.119580368414153,High,Public,76.63884651646482
+10.803423444197882,4.026931059118445,High,Private,67.61335003824102
+11.380287983422225,6.196571501664492,Medium,Private,83.41238812432967
+9.197559056228327,4.84147042697303,Low,Public,80.94328874784078
+10.448184963620834,4.972695460490068,Low,Private,73.42429068656418
+10.02518480156359,4.066732040926804,Low,Private,83.24325109647663
+10.195352197097664,4.55671774880849,Medium,Public,81.4399372410502
+8.453980432289066,4.115197285973099,Medium,Public,79.11315757061304
+10.049020348517885,4.827053940076577,Medium,Public,78.28644088932379
+10.9959965824909,6.7117084801423434,Medium,Private,77.09573000643621
+12.902287215590084,3.6280988570686437,Medium,Public,85.15079985585383
+11.918541652170413,3.386438602123259,Low,Private,75.79373667368402
+14.306364915023114,6.471170327493608,High,Private,88.9089078518034
+8.4653048742239,4.7906763228481335,Low,Private,73.25986926870192
+11.744641273441356,4.330927261340084,High,Private,85.60915272287733
+10.366684011476703,6.039904687396076,Medium,Public,83.44486008366417
+14.379605866435345,4.394384460263136,High,Public,84.94795943170354
+8.383403429289697,6.826009713546936,Low,Private,67.24778546119904
+8.320556315638449,5.677925871339269,Low,Public,71.98186410563979
+8.801214709111957,4.5120885918542974,High,Private,77.89167375342745
+5.752208551380386,7.1573082132655035,Medium,Private,73.64093072789196
+8.948489956638477,4.3942850769966615,Medium,Private,66.97229785670764
+8.481734676892604,5.742095372009307,Low,Public,66.54367254945471
+10.300787572952416,5.299292580406492,High,Private,79.52805976730185
+10.683511951554319,6.301741289398798,Medium,Private,75.53434999862796
+13.752341678431772,6.561511196726853,Medium,Public,82.54296784776595
+11.9008476763721,5.0320041490657665,Low,Public,84.71274022025612
+8.846192688675194,4.246582129653817,Medium,Public,80.07345247390948
+8.203170657303284,5.459972142939116,High,Public,70.84278939383375
+10.983838343013012,4.322284630247894,Low,Private,78.227302093337
+7.359533585958715,7.013387247526623,Low,Public,74.86791471328556
+13.662917531708707,5.1365353310827375,High,Private,84.20961753655894
+12.358880241442574,4.634678448687891,Low,Public,81.87277053669848
+9.061648695790591,5.184680305864909,Medium,Public,79.68281248957933
+6.573730941818245,3.6528737104703035,Medium,Public,65.25715071569397
+12.707744748330825,4.028385961489349,High,Public,73.83168076631534
+9.770920309494764,6.200413907944426,Medium,Public,87.29251114892497
+12.475632623946924,4.343105721028604,High,Public,86.913033451887
+6.811144682411266,3.95308901731806,High,Private,64.03373353455264
+8.801249954092455,5.536652752527286,Medium,Public,76.58563908085561
+10.010487399436366,6.1857041546629965,High,Public,84.35304185379778
+10.093961187529484,5.718953310665067,Medium,Public,82.51157206368677
+9.099869057041513,5.9960476858058165,Low,Public,79.50387619243818
+11.245699864694998,4.2432049114305395,Low,Private,78.00507263857709
+7.864759141234812,3.5781893332581562,High,Public,79.73332190766665
+9.715241029957413,6.501333651955487,Medium,Public,80.28677690014534
+10.240591263423799,4.677320161157864,Medium,Public,74.92236647198094
+11.028877668117499,4.749166983544953,Low,Public,83.53688445578041
+11.42322975617778,6.328194141849058,Low,Private,82.81904393488624
+7.750715816324261,5.55623000940066,Medium,Public,75.58476528301762
+6.931771658528755,5.455887772081607,High,Private,73.06312098313596
+12.555353643797018,7.165002344901084,Low,Public,88.34208867378022
+10.664628023959184,4.356481769619857,High,Private,72.97169743535211
+8.503026926886893,5.927840128081237,High,Private,68.64228211004558
+13.102303951045046,5.057013124897084,Low,Public,90.96079120560363
+10.231349268585717,5.268592278863257,High,Public,79.61350976441216
+12.358594368127653,6.528468426412532,High,Private,88.364127762917
+10.135036962820218,5.507835756289624,Medium,Private,69.73303145918675
+14.121495849763974,5.538296079049633,High,Public,91.88385136167491
+13.510681684886409,6.072507337309802,Medium,Private,83.52269614895869
+9.502071703041853,4.635047270457115,Low,Private,71.92562347772416
+11.94314190190871,4.160790332660993,Low,Private,79.81060346464898
+11.290751899170296,3.955190805978966,Medium,Public,73.62438895459641
+12.737263115064698,3.0336434106239984,Low,Private,80.01633285493736
+8.070153078839791,7.056207129940322,High,Private,76.04292511751764
+11.372102919996879,3.896791634278317,High,Private,83.89137281758065
+12.116848973699176,4.778746376414228,High,Public,88.96854002271704
+6.482521027153771,4.723186700111079,Low,Public,77.45740106868207
+7.63348297466845,5.307406697809101,High,Private,61.90550109906998
+5.921535644479799,5.815737212539297,Low,Public,65.89084295855211
+9.461186331110884,5.860473488359745,High,Private,66.33864907564957
+11.435084511591924,4.416922561431061,Medium,Public,73.52784149315528
+13.004714104192056,4.832878286213589,Low,Public,80.01115458318533
+10.148189560839551,5.2825799504883,Low,Private,80.27305712354628
+13.257231091142584,4.751308873816453,High,Public,81.04175932054714
+7.239797083570217,6.607345576019993,Low,Public,71.93026217642719
+6.593235121289691,5.490974951638251,Low,Public,72.33175608603557
+9.888904602206763,5.734877786049817,Medium,Private,69.83357765744921
+10.768130897878615,5.662881268667412,Medium,Public,75.83761793393903
+9.934610503811815,6.173473857485325,High,Public,76.61164866476474
+5.865115799920247,5.181021558703707,Medium,Private,68.48975399473933
+9.821759920974424,3.7031680520441537,High,Public,75.5830418711428
+7.391060998990294,5.399687951752641,High,Public,80.38490287427474
+11.339345097660077,4.348643106208171,High,Public,83.74333566003789
+10.733196492193697,4.471383318250243,Low,Public,74.32691783930309
+8.120240427345289,5.586364018755093,Medium,Public,73.31338158556588
+8.972266165326612,6.238283071433636,High,Private,75.65476125234306
+7.881572956222097,5.021271576736062,Medium,Private,70.72429069494457
+9.874641805453656,5.308833012598964,High,Public,79.07511225891031
+11.910284641002477,6.702214944635238,Medium,Public,74.57499393410326
+8.028547907328912,5.240753179488569,Medium,Public,78.32179752392311
+11.008093031035688,7.601683114180394,High,Private,91.88646468691873
+8.939484763255118,5.565509645631544,Low,Private,74.87803129159846
+8.414254335475311,3.239237240844182,Medium,Public,70.24054240662775
+9.785939280090885,5.753341621104532,Low,Public,78.98711222788192
+7.929515355161252,5.381158384882777,Low,Private,79.02769817244773
+8.892701389305635,6.289752754082746,High,Public,75.59856332564917
+7.6042442148223035,5.6731813512699585,High,Private,78.38798628705823
+13.929450265832779,4.861544016016226,High,Private,87.59368991795611
+10.070527103943457,3.775701763710634,Medium,Public,87.61266407347632
+8.600548984014829,4.7909767427139744,High,Private,72.74425300628913
+10.427959821468445,4.1494795457907,High,Public,67.20013672301879
+9.77534390061834,4.419476550195277,Medium,Public,69.01520702310202
+9.558060800933553,5.58857840442061,Medium,Public,77.5586303954988
+11.22833340008685,6.669904504351406,High,Public,87.60093402680457
+11.51501542009461,5.394671529822981,High,Public,77.85949243282008
+8.938997704778945,3.8041169379648156,High,Public,72.42950856712845
+8.84836351871064,5.44460266822014,High,Private,79.28784472640938
+9.449896605696711,6.196631486645651,Low,Private,73.3685774369696
+5.39615767052883,4.390217095680618,Low,Private,64.3212939886045
+6.9696178756028955,4.865982833327083,High,Private,65.39957656314512
+12.733748534889049,5.014688194729197,Low,Public,88.8806698262847
+13.289935427002568,4.21510168691776,Medium,Private,90.61088744336362
+9.501927920887244,5.648280433843859,Low,Private,69.57920154507497
+11.153113926111534,4.8790518513922185,Low,Private,78.91387813437026
+10.622500309087073,5.419532443907028,High,Public,86.41059415935942
+16.157761616910477,4.1125078221979,Low,Private,90.47776088522899
+12.239149822869154,4.56254169967659,Low,Public,78.87591603025771
+9.744164817038467,5.722381357827771,Low,Private,70.3410910727641
+8.088919118799149,4.627166847908233,Low,Public,78.88331126857497
+6.787107359484855,6.726963851233405,Medium,Public,75.4672365705423
+10.406927271734446,4.600363814385398,High,Private,70.34946832633821
+8.487298509431394,5.224684725751886,High,Public,80.76107937686558
+7.155492580804651,5.93259083187309,Medium,Public,65.20785360062958
+8.706854231514948,3.5816342670669683,Low,Private,71.68372909279329
+7.8369039927712105,3.239191188019224,Medium,Private,69.20952125071061
+13.37428327014513,3.474343685429905,Low,Private,90.05198871267429
+11.763279513898901,6.262584103427182,Medium,Public,81.61520422353782
+9.984054717366766,4.448141854107553,High,Public,75.31710878439125
+12.959888277780053,7.558199285668252,High,Public,80.44168375957402
+10.154736615295237,4.4357524013426115,High,Private,73.76111343731137
+8.277431597343472,5.184551303197037,Low,Public,70.24521284507928
+13.046248154539315,6.542109952567875,Low,Public,82.07954940790698
+11.077820087369318,7.006092888159123,Medium,Public,82.69620213143669
+7.925507691347088,7.061503576636397,Medium,Public,64.2723888184358
+9.619322643832783,6.208366230593143,High,Public,71.00004340647078
+8.248763493230486,6.024062525811463,Low,Public,74.9785264071424
+7.234400538071328,5.592526949201183,Medium,Public,73.46386491546409
+11.852355095063283,5.778361076166638,Low,Private,81.20508781115704
+13.818833280940261,4.448814283671466,Low,Private,84.10691553846291
+7.202864852361717,4.1818011165776925,High,Private,68.63284831131371
+11.125938473381142,4.996625542589048,High,Public,79.65429224449754
+8.698714861756347,4.829815377475072,Low,Public,76.81286389103637
+9.025749232470607,4.546771950672725,Low,Private,77.00732503438861
+8.815212151522262,5.696387447456715,Low,Public,72.73021341693712
+8.272018460640368,5.955305208570511,Low,Private,80.05246843205124
+10.097043255889654,5.088406886291847,High,Public,78.61224031373366
+8.338099767177924,6.477530081055262,Medium,Public,82.55356443763758
+10.540913651559677,3.858310885866495,High,Private,84.0336557882545
+9.899523781101726,4.8063405407202815,Medium,Private,69.79492476641765
+9.52210390626718,4.283177679397195,Low,Private,71.1738366372866
+8.184872675916804,3.13346338292694,Medium,Private,75.13768062607677
+8.846457338863335,4.917319314157301,High,Private,66.74443154831788
+11.510782451651512,4.878252491616716,High,Public,87.43982845419495
+11.001834375248762,6.5134497432421306,Low,Public,80.05443239129741
+8.044889510402898,5.630811684554778,High,Public,77.41116172318306
+10.19866461085845,3.975813175670795,Low,Private,75.7790956709192
+11.502774246743577,6.854092566334188,High,Private,76.32473970407001
+6.6611894377572565,6.221033695525353,High,Private,77.93165242946242
+11.08672038475987,5.582097703468616,Low,Private,94.30020247740931
+8.674752482108307,4.773515901150168,Medium,Private,71.9347374341634
+11.141197337186318,4.04056076327652,High,Private,76.7314430650612
+8.473481686914965,4.627793223928947,Medium,Public,60.64315549338805
+6.390235798670962,6.088748619704186,High,Public,78.67110904417882
+6.744915124233675,6.884586305934609,High,Public,78.37553632157928
+10.096169893322765,6.543243528272189,High,Public,71.34028576877925
+10.519445003442964,4.511150578213726,High,Public,67.4811885639721
+8.191366749791182,3.880382566710989,Medium,Private,62.457150933822525
+11.277184917554749,5.1408862469112035,Medium,Private,83.49154483234038
+6.67695987546208,3.231560634437148,High,Private,71.92621780065029
+9.867840402705367,5.323167714184781,High,Private,73.10376265484396
+7.577967600475087,4.852397413701332,Medium,Private,74.93083147841615
+8.696327784395681,4.533963498131758,Low,Private,74.98798642094475
+10.094797342632829,3.4052969327332043,Low,Public,73.46119155009437
+8.279173269432095,5.513600106569717,Medium,Private,79.67973083214525
+9.23088891154035,4.467299160338088,High,Private,69.13804855744763
+12.012585618428881,3.8300831642897117,High,Private,85.50799205126181
+8.846216260953703,2.1277377867183507,Low,Private,67.24143143191317
+11.671384224130284,4.972485094949364,Low,Public,89.70917950051984
+7.740586290684764,6.772251596669744,Medium,Public,70.61301985896681
+11.059608355830566,6.66125921454787,Low,Private,80.35324514048229
+12.8831372413158,4.542903743413728,Medium,Public,82.87356578546395
+5.056710999745421,4.39778793963822,Low,Public,66.97169664806059
+8.406209489059046,5.468774256075024,Low,Private,77.4697228653211
+11.15414425436108,4.001614560811708,Low,Private,71.88229825870634
+9.593909227914015,5.301791899819466,High,Private,76.95394426432405
+10.742291746742618,5.76608027838469,High,Private,80.82436244307587
+8.79202962656836,6.226933223884307,High,Private,74.28576796882254
+10.1731795749458,4.899845923520047,Low,Public,79.71642112246876
+9.68864552921584,4.796326249823034,Medium,Private,69.83993086572733
+12.335564123319614,4.122017413243439,Medium,Private,79.22188492125098
+10.508841686602427,4.17311964596014,Medium,Private,73.51144222012672
+10.675205324150404,4.773521108098473,Medium,Public,76.89353766270342
+9.176246067755065,5.367365506816178,High,Private,83.63871769534042
+9.024787551855013,5.913584626233249,High,Private,84.25223067384691
+9.134883624360759,4.196821049319655,Low,Private,80.9170811256071
+10.788904284756594,6.4926885689595215,Low,Public,79.00555502254822
+9.158031038359475,4.728876398957431,Medium,Private,79.94746805915614
+10.579549713792826,4.978632705651564,High,Private,79.18885912069203
+14.150801597290878,4.252788320852177,Medium,Private,84.90873419337507
+11.742249406863385,2.5757597397270584,Low,Public,76.96133168522152
+9.347952935664317,5.884045396361049,Low,Private,86.31406325276583
+12.402427844327889,5.736843897092434,Medium,Private,90.23931456207676
+9.183849253956897,4.718672442881787,Low,Private,79.17018926447196
+5.923750929644292,5.066990717224872,Medium,Public,68.59648310757277
+7.983827378165191,5.5159392177041,High,Private,74.43936737415933
+6.258416157948289,3.4374541431134573,Medium,Private,70.32584655325543
+9.296973031917382,4.470947322653705,High,Private,72.69232248854522
+10.036836758379103,5.794264679621827,High,Public,83.45664911601347
+13.352874624550566,3.7457105768307506,Low,Public,77.71808955713273
+10.653854747528325,5.293557932166629,Low,Private,81.20726483329362
+9.561798942382271,3.643418195376595,Low,Public,63.140147428866285
+11.658811162366979,5.466429982433526,Medium,Public,78.7641746653736
+5.57772938198423,4.964358517702516,Medium,Public,78.99038220511946
+10.471229116217131,3.3848681840944908,Low,Private,81.20071798053556
+11.541730387773933,6.164739354457171,Medium,Private,90.00791205690624
+7.0428275084403165,4.265408423201951,Low,Public,67.14722931491166
+12.287508086413858,4.189747562985619,Medium,Public,81.37850170130832
+10.676992814988829,5.2005691972309425,High,Public,85.56480069129559
+9.169424172198397,6.148637349428715,High,Private,80.72727904206675
+11.26556373221257,3.984178181021603,Low,Private,81.05022412274914
+14.541385715608792,5.061679849847971,Medium,Public,94.82820105763986
+10.363732510116991,5.428816500330573,Medium,Public,73.6684149426602
+10.496441172600672,5.693105607263144,High,Public,78.61135055886854
+9.081278200919511,5.176441555277226,High,Public,67.15679092140809
+8.300311261070416,4.632972160916678,Low,Private,68.07455050383784
+11.66067163308849,4.17240977935128,High,Public,84.95088489609876
+8.287832348182265,5.0861438830463745,Low,Private,69.61813418997346
+10.143132474438785,3.927860988014393,High,Public,79.85567770152464
+9.044685106469768,2.0786495165053926,High,Public,79.49976969753803
+10.957959651492784,5.4365598041576675,Medium,Public,79.18106813426238
+10.667324210573897,5.903934841086195,High,Public,82.78986788177615
+12.075079888515798,2.6370675644581616,High,Public,81.82232177143345
+8.979967202290506,3.9902692952900622,Low,Private,67.8378630321833
+9.460250129413257,5.619154261005198,High,Public,74.26696992382907
+8.042472568435386,7.057495469711698,Low,Public,71.65288228307931
+9.111413479847776,5.020793707675408,High,Public,73.07040197079353
+10.754600986089704,4.271997058770865,High,Private,81.4977510507412
+11.513977233290703,4.81710355889516,Low,Private,77.27762615021268
+8.155669351644748,6.374876419148185,Medium,Private,75.69788719735269
+11.73921184021132,4.354035820875465,Low,Private,74.79716932707966
+12.711275717609901,4.200807993349747,Low,Public,87.85587424791294
+10.826869806447402,4.517256477817968,Low,Public,75.70407437850507
+13.753591625116131,4.046671391741287,Medium,Public,91.19209309739395
+8.452421601792853,5.122670315994796,High,Private,74.8934941468247
+7.510690593377166,6.62467845846956,Low,Public,67.92340098464327
+6.442559502191443,5.323079273116029,Low,Public,70.0146041813288
+12.992088622978367,4.7476464978737125,Medium,Private,77.79176285543649
+11.308731312708115,4.708188731084456,Low,Private,82.62649260893369
+9.88883065817909,3.436809191303343,High,Private,73.62606119167886
+10.55993725263964,5.883109777147159,Medium,Public,80.7605904461368
+7.7490219054032465,4.922162802987198,High,Public,71.03452246505447
+14.891503959233653,4.819520086409375,High,Private,84.23557652876475
+10.258442363950454,8.19310756784486,Medium,Public,77.93843603159006
+10.218789589209786,5.298752908365686,Low,Private,82.56682322507574
+11.451533247797384,4.248208949904816,High,Private,78.4096144114535
+10.962018463473427,4.573642403794457,High,Public,77.21104606414663
+10.447768048558263,6.148445709196755,High,Private,79.12713516870086
+8.419051089109375,5.113270410537765,High,Private,71.11369882659652
+10.942936714271992,3.561722021682633,Medium,Private,75.67169932718781
+13.764048992950068,5.919228934575203,Medium,Public,87.43509046123037
+12.690840092309955,4.331855914461006,High,Private,76.7087790610276
+13.186373253278795,6.873297776217545,Low,Private,82.23593320548787
+8.97756864713763,6.0800480657146485,High,Private,83.9666298609606
+8.020790359482838,4.552678104341249,Low,Private,71.05681521147976
+9.748426159800704,6.281016361735113,High,Private,76.16057281758403
+10.11144982457739,5.067855510527403,Low,Private,71.10532488714951
+12.188383036941897,5.8527736751222195,High,Private,87.40931477393833
+6.615070740570362,5.484732817294475,Low,Public,66.17761163952822
+13.059100638921228,4.153643345009127,High,Public,82.27220299054886
+9.683984202842021,4.356450167682538,High,Private,67.3617183106249
+9.14623786010516,6.029960817581161,Low,Public,81.42828094840264
+7.975791249479665,4.665224667893459,Medium,Public,78.83370071889242
+6.690286656268461,4.596351536758004,Low,Private,72.697702345064
+11.64634116792383,4.044877420650647,Medium,Public,71.68759780979714
+10.146635934376807,5.423599065593461,High,Private,82.62248118668995
+7.420078200517892,7.062524919243145,Low,Private,71.29719878169874
+7.409842455872768,3.932467085745727,Low,Private,64.86352614675606
+9.328430601419743,5.024219456672758,Low,Private,74.27588515906035
+13.338043050578786,6.412220555415429,Medium,Private,84.27757856754828
+9.48081729727279,4.9203586087542845,Medium,Public,73.81692925177558
+6.993714093763788,5.452371796249734,High,Private,78.53435712142564
+9.50851387182811,3.9376064718531203,Medium,Public,84.98256743364905
+9.45455286050466,5.428307097207648,Medium,Private,70.20588368007347
+4.606226714116857,4.812855734995451,High,Private,62.98757505910394
+9.891410266964376,5.985729995679863,Low,Private,79.47515720767333
+9.538130939582643,6.187386051564751,Low,Public,76.41375850182628
+11.392412729626837,7.589563642018614,High,Private,86.16616466867418
+13.69791218989069,5.579633173213575,Low,Private,90.34520091933071
+12.253130059095515,5.325796316582166,Medium,Private,86.24178430441448
+9.462222618890339,5.194384301577336,High,Private,79.76468952939584
+7.786948182516659,4.6468337071893115,High,Public,80.8396245304159
+15.14671960649972,5.338483843461691,High,Private,80.35642856361143
+10.118436868028976,4.704598589860971,Low,Public,84.69431558062249
+10.027858583825893,5.168460976269614,High,Private,75.74345046160748
+9.951749825779993,6.31759753560796,High,Public,83.30552205835006
+10.39616952153571,3.9934574347203586,High,Public,71.63419405375474
+9.711279176152114,6.139878561129045,Low,Private,74.08989423453141
+8.852675986239527,6.3171150673398655,Medium,Private,80.491611030899
+8.906282117519215,4.881931473008922,High,Public,65.49181133080258
+9.934493459568003,2.8781451009914054,Medium,Private,79.13341496206468
+8.91315045773247,4.392178012905568,Low,Private,80.71438544653124
+8.57430843464567,6.296994546561109,Low,Private,77.4830329582778
+10.212860455383794,4.977131937701118,Low,Public,78.60022399521205
+9.49004556515829,4.0006977677050894,Low,Private,70.81295705733194
+13.007985977165378,4.4952250787717825,Medium,Private,86.13105658450678
+4.698060383213976,5.840620026504553,High,Private,69.89537703822191
+12.183013703844924,5.546733568201379,Low,Public,78.07860077987279
+12.492170384995259,4.761067900830306,Low,Private,80.16854287079502
+5.853219535183701,4.6331755881853125,Medium,Public,67.85952532916636
+9.314624811839302,4.608241850956178,Medium,Private,73.57760141751693
+9.257118268008417,4.077589815501497,Low,Public,67.8160641925192
+7.184976610564357,6.615375689469442,High,Private,77.78811829825234
+8.44436662481825,4.6776795307886605,High,Private,72.68770026631002
+7.7788483090683425,6.217158520825496,Low,Public,74.96025827884148
+13.504540886847245,6.521316051922451,Medium,Public,83.89972291459102
+11.871356786294923,5.9983108981479605,Low,Public,85.11653728967174
+12.543110189988317,4.5683796872819515,Low,Private,82.9322708328066
+11.443344128086471,5.40373009033661,Medium,Private,87.05620533732996
+7.741896457565542,4.975804393609084,Low,Private,75.35049307023964
+8.950959467440452,4.096298139269872,Low,Private,73.03294413460016
+10.978749122455836,5.324359281721603,Low,Public,76.24545307998129
+7.555744382216109,3.8209602088401975,Medium,Private,75.17269249727575
+11.425996860344776,6.1876793884615635,High,Private,80.7206323094496
+9.51934920368373,4.535382702701607,High,Private,74.98318462843878
+9.250358384900805,5.201159655872839,Medium,Private,75.4425982221475
+11.421919936406983,5.283287871546142,High,Private,78.76073312684753
+10.88852662297208,4.741095026295922,Low,Private,78.11600081423869
+9.27806766836184,5.586693801475275,High,Private,86.56721385259948
+12.318659606728495,4.525096343377475,Low,Public,76.18585345788475
+7.837873344800205,5.871297297457244,High,Private,70.41017158197823
+11.231871213888528,3.6540203197532515,Medium,Private,84.86639499817818
+11.186202515936767,5.126379579546412,High,Private,78.18795247871779
+9.380907121372019,6.938928999058285,High,Private,76.91710132095699
+10.652266044484236,3.9996686753944255,High,Public,82.47835438592728
+7.497772847229394,4.322255029475149,Medium,Public,64.37795015226274
+11.848054038413801,5.513907849658915,High,Public,86.074241997583
+9.630195727114021,5.179581781939598,Medium,Public,77.25784627374516
+8.95455395896192,5.350630099273559,Low,Public,78.25906372099408
+12.098018451673779,5.489187130147664,High,Public,84.65997237482969
+8.59131261891448,5.63472146856177,Medium,Private,72.49479450825842
+7.183077407272881,6.109699841956223,Medium,Public,78.78662347822963
+6.8867416529521925,5.409818656928513,High,Public,69.1375227339068
+11.21201990269128,4.7587423453814575,Low,Private,81.60639841844846
+7.439141295007434,5.672573701202731,Medium,Public,79.57576556946465
+13.50958836396873,6.899881934895216,High,Private,94.26350812951263
+5.836141184232556,4.867366253617426,Low,Private,67.63770006918779
+13.392912736580076,4.025470695329359,Medium,Public,85.13970788735924
+10.422034934405238,6.107080669286972,Medium,Private,78.20724692172284
+9.806573776259215,4.879618836046402,Low,Public,89.4515569429319
+8.910161826382101,2.827330453985964,Low,Public,75.96194936311224
+10.798272228704143,5.847421677150422,High,Private,80.7879691523186
+9.924730595150303,4.464671814046078,High,Private,78.10927977865843
+12.206603764033042,4.909466717695839,Medium,Public,83.81293468184018
+10.228455297324079,5.3319803305690145,Low,Private,80.63859879441908
+10.300603522923755,5.190499679779067,Low,Private,77.18696547728331
+9.272775575572288,5.70945181713904,Low,Public,75.13809238067726
+9.886108752557865,4.564513629430157,Medium,Public,75.85972114062851
+10.615603537784118,5.513105798176083,Low,Private,73.80240724759632
+6.579663214686748,4.740453322416083,Low,Public,73.86137844786992
+7.303629155788574,5.738810480887503,Medium,Public,69.20694881966352
+11.48652818804543,5.615367484013054,High,Public,76.09635720718686
+10.341730876255884,4.064561297955405,High,Public,76.70853136662413
+9.632033327294641,6.085982116175032,Medium,Public,77.7424266804024
+10.036867866130786,4.464036554885952,Low,Public,78.01796294353544
+10.695163410723342,5.808057798281145,Medium,Public,77.26138667188634
+8.920480639381276,5.367287313401341,High,Private,73.9291794577097
+8.443390549195374,6.838183676795122,High,Public,80.60465413902588
+10.391690510195364,4.776534017628148,Medium,Private,81.10426804930891
+8.043254444769936,4.650683203881803,Medium,Private,76.01015892624596
+10.816505511428943,4.980580394112267,High,Private,79.27371967578371
+6.594832791524319,4.696820217165724,Medium,Public,71.0795090254586
+12.058311274651288,5.79994190194205,High,Public,83.96872984076816
+10.945194964826086,3.3836893915634123,Medium,Private,73.68571531795241
+10.51205946862775,3.9463175808536457,Medium,Public,84.31382830926648
+11.965381967891028,3.932197078420807,Medium,Private,86.48699971585175
+13.330948888925153,5.9503075919735755,High,Public,89.5585871208155
+12.028740130036262,6.710613372581925,Medium,Public,81.99409238693742
+6.318251537336709,4.8955507814625285,High,Private,72.67554983661796
+7.4408460665285965,4.83117827680999,High,Private,77.02030923712104
+8.750362844608642,5.070052163113181,Medium,Private,68.98981942528134
+10.052182100421668,6.161878302608191,Medium,Public,87.81974046324811
+11.035318040938247,4.072646865809426,High,Private,69.48266832481167
+8.548512373693068,5.2383689827347855,High,Public,78.4776073200337
+10.373533528954157,5.975197629782429,Low,Public,86.9762750338443
+8.489234135293355,5.5010941699398455,High,Public,74.66479220536134
+8.776964394016103,5.189581616598743,Low,Private,74.58597921381495
+7.186677806303555,6.001046092567141,Low,Private,76.71322871469245
+8.153533507778192,2.296767707000763,Low,Private,79.01162803572689
+7.296630788767333,5.677875319530908,Medium,Public,71.7047363671242
+8.048253494045335,4.345924316872576,Medium,Public,68.7106175040698
+12.10728359321568,3.169367103143543,High,Private,76.84974056310672
+8.101202222336115,5.511202599524981,Low,Public,66.7066383825286
+15.26476412967478,6.373658545162957,Medium,Public,94.57880557066977
+10.986635801761778,4.862551485372308,Medium,Private,76.69668356801272
+10.369672247389747,5.9528745472029385,Medium,Private,78.62696054525611
+8.283284439637573,6.612278257988645,Medium,Private,81.03309192335314
+11.400619758817983,6.314914453584048,Medium,Public,80.38905957596279
+8.848724347524453,6.639964529371393,Low,Public,81.45284746604798
+10.244019629290722,5.7421274910718925,Low,Public,73.91078797610784
+15.12016907653759,5.075433638901592,Low,Public,92.64708575304756
+9.8078802005507,3.3980341881028315,Low,Public,76.42233678958299
+12.298546652571352,4.753937511870052,High,Private,81.28510441884944
+8.593647149748225,4.156753404683808,Low,Public,74.07689184624114
+9.930023019006077,7.170942717497814,Medium,Public,74.18265364099518
+13.54160127127102,4.824114187567663,Low,Public,84.85937754362838
+8.746065884424466,5.123204806784486,High,Private,74.46859005224265
+13.624897115993857,5.551485376040468,Medium,Private,79.27985924386061
+11.415503870910952,5.043602446372454,Low,Private,77.04103427684787
+8.875066448211465,6.695051040430753,Low,Public,77.14648316730646
+11.264815478111043,4.377350638265548,Low,Private,76.05919598888121
+11.94510889925346,5.194607455237243,Medium,Private,69.6215594561107
+11.243619924434393,4.257529414301952,Medium,Private,84.25547166836604
+6.859550560219088,3.6799774887792944,High,Public,68.52861797815879
+8.545725648350263,4.388230909094714,Medium,Private,62.30404661415987
+9.50496272889699,4.962963202951588,Medium,Public,87.12918496061833
+9.851133141799908,4.57069777548274,Low,Public,82.76527460338986
+11.241344195101355,4.307579020126144,Medium,Private,77.19914666505025
+10.355402001866508,3.593682536328166,Low,Public,71.88884035139176
+7.3293112825797975,4.9168944273713056,Low,Private,74.04992026001916
+10.760395702011927,3.4952796259689975,Medium,Public,77.77534491739806
+11.221171490567647,5.760055963689602,High,Private,83.79287655067156
+11.119580895862079,5.082439752934503,High,Private,75.66472005485068
+12.161561451109243,3.5424485005350697,Medium,Private,85.12565575787926
+11.667844309097807,4.6907909229501366,High,Public,78.00456438674308
+10.918360158456874,4.247843594409417,Low,Private,81.61231445180573
+9.859668577082706,5.31917451041918,Low,Public,73.71340261750908
+6.678078132968017,6.340450446023144,Medium,Private,86.10755186373818
+10.859236438265171,3.1248275303542554,High,Public,79.88333091688801
+10.415375374326223,5.115026079147417,High,Public,92.92445125024182
+10.543157674390747,4.83986720443336,Low,Private,82.48412603706177
+7.446502848359382,5.671340076450465,Medium,Private,70.59269402705104
+7.837886919183475,5.213196628237689,High,Private,77.64247274866946
+12.106305706665808,4.248030668261181,Medium,Private,85.08754650668946
+9.92088969228669,4.680946064333382,High,Private,78.72981920356227
+11.363001394745249,4.203974143196511,Low,Public,82.18392636444554
+10.056636752260923,6.076007138598598,Low,Private,83.0359198594581
+10.059512278991491,5.021311648671942,Medium,Private,72.68258697716219
+11.876567611951995,6.901190685753008,Low,Public,78.78062922888374
+8.967910543565253,4.9393391861853795,High,Private,71.3737448733364
+10.192241553881967,4.291593233089538,Low,Private,77.4595849292508
+9.075449422589916,3.486285607387173,Medium,Public,72.26447139889349
+9.131007545135368,3.1968603237440547,Medium,Private,68.17945274627208
+9.381655753062722,3.4158640569945105,Medium,Private,77.76367864421545
+10.444267543267426,5.267126651011483,High,Private,80.20444916373448
+9.042502756673045,5.508725023229934,High,Public,77.19273800504526
+12.511512251147042,3.418809297967867,High,Private,78.8612919233762
+8.210785395560992,5.895038314076116,High,Public,73.37298775040829
+9.626256711677282,4.516938948316561,High,Private,71.82126603678452
+9.12053788345165,5.146793011985549,Low,Public,80.6299667475683
+12.893955768707466,6.612220628255424,High,Private,82.18877807153213
+10.39310955302315,5.896839315865532,High,Public,80.67843041330867
+12.06368907893727,4.731469352094168,Low,Public,89.53463416737596
+7.028879253926056,4.1088077745847125,High,Public,68.55968144628243
+10.534100531738517,2.848184598670695,Medium,Public,80.14398866507905
+11.779261591246874,4.28084668075758,High,Public,82.82333828380847
+10.16456797855085,4.788869761172306,High,Public,79.06977841516542
+12.130960750130702,4.012820472848816,Medium,Private,82.4328944190584
+8.965423099799256,4.8687430329076955,Medium,Private,74.7594279026054
+12.81869488037116,5.076851882958768,Medium,Public,83.42654742914767
+14.5977962472385,4.775144018191922,Medium,Public,83.61773878063626
+9.274322879120682,4.349997419176541,High,Public,75.28092749267662
+9.108994957198457,5.168654671966728,Medium,Public,73.04484194342952
+12.906768954235401,5.441940649569409,Medium,Private,89.0047243482079
+13.159144291461425,3.909600922557154,Low,Public,73.68520282336635
+8.954279945693463,6.41093237974627,Medium,Private,79.93022491663375
+9.159626365808286,4.901411867715738,Medium,Private,77.35430404030159
+9.436430782278986,5.018849622944848,Low,Public,77.63121581985541
+7.3110989779314455,5.708214423256557,Low,Public,78.30701433507517
+8.162696107031604,5.233216131263502,High,Private,72.74459846074373
+7.991718466495865,5.953136633913245,Low,Private,76.19031110672185
+8.464404869791743,5.287124165201824,High,Public,66.27678654645396
+9.930630225226437,4.387562625359562,Low,Public,74.3660199778161
+10.468429465073042,5.361503525206102,Low,Public,83.25779637383137
+13.101000985628154,3.856273792679338,High,Private,75.29060398259291
+8.003291918522418,5.108559705776523,Low,Private,74.46814023096336
+11.968644796953168,4.966770009355539,High,Public,77.99985924852066
+9.572022311548983,4.79188314272746,Low,Private,75.77693447833049
+9.901072580695134,4.871462488561069,Medium,Private,74.0209514504239
+11.349638984333208,3.1181509545023607,Low,Public,71.14052637895281
+7.754555956875403,4.4512750922590385,High,Public,74.11604112693836
+10.764819492368101,5.092844901141115,Low,Private,78.92092252632415
+10.332904416426112,5.1598563919974,High,Private,81.46873901928431
+10.984902528016299,3.9723248590740416,High,Public,81.90048188463797
+10.578337287815636,6.2657078408647005,Medium,Public,86.6815300223734
+14.91060027982179,4.1338251517855,Medium,Public,86.68937201453515
+8.724520031497368,5.969457200526737,High,Public,82.74633098680088
+8.938006089996364,5.427194361871685,Low,Public,71.13793118584549
+8.753718947150471,4.353772690156197,High,Private,78.5828960756759
+8.88904576167949,6.775310892609512,High,Private,80.23800994391252
+8.725225745386965,3.8063631682435197,Low,Private,74.32545950571314
+12.378033062215103,5.919154173610002,Low,Private,85.85845855566079
+12.84100849597971,6.000582318042978,Low,Public,79.87919607734656
+8.858507412501048,4.329379790923355,Low,Public,68.22735046176201
+8.335288853791543,6.392465300064329,High,Public,84.80841658806456
+10.942831112772808,4.749953485401047,High,Private,78.16698156266084
+8.895553911438057,5.288693629279857,Medium,Private,74.8091811577624
+11.265863635511021,5.260321838771221,High,Private,74.91066265797242
+10.405846041702599,4.865691258529452,Low,Public,70.70133744371519
+6.968511770005536,5.8108082731597825,Medium,Private,72.21164155568933
+13.095010402660122,5.793488720552213,Low,Public,83.17231427860493
+13.591755346191043,3.2514680267618195,High,Public,82.06938451623708
+8.774422619031228,6.304340242878153,Medium,Public,77.44112530429055
+9.224596880128031,3.3375080119643257,Low,Public,85.90276118450602
+10.57173078144981,6.032546204341657,Medium,Public,78.49938096571732
+10.668913579974049,6.126705136827583,Medium,Public,88.4275854049067
+11.31708854534566,3.9090335697848015,High,Private,86.70784852059325
+14.0204090775327,4.589185712045415,Low,Public,88.69271814949344
+9.6461055450119,3.8942953315140643,Medium,Public,73.95802242454934
+8.403405510923093,4.785078969711265,Medium,Private,67.17706779574944
+7.241361543970946,4.691965715814259,Low,Public,73.91039104833283
+8.538139920116162,5.7796605322693395,Medium,Private,80.83983103714107
+9.933746054252406,6.310308746119608,Medium,Private,88.07139254909916
+13.589115727035576,6.395683810789956,Medium,Private,82.02196739902217
+8.964777401927657,4.437831973421698,Low,Public,73.36867081806152
+10.447575903277796,4.790778126255472,Low,Public,73.36199604382456
+9.967154207854495,3.31656180777905,Low,Private,71.74176116519294
+12.376786546896168,4.194129933503811,Low,Public,84.36670566522619
+15.053864851747242,5.9648516330974575,High,Public,85.658483306312
+8.938262454159359,6.615582823548232,Medium,Private,73.678543257056
+9.021121114963556,3.765651128120078,Low,Private,77.67569824071859
+12.088321754138144,4.407535740847106,Medium,Public,83.04164106976468
+11.363782979252623,4.973594243041073,Medium,Private,81.93801331274273
+13.693414651472068,5.280161158819607,High,Private,82.04263425017821
+11.167856370651927,4.190396480078354,Low,Private,85.08642385249489
+9.28141581842588,5.424061044464089,Low,Public,76.77137419014686
+11.181309661384619,4.526160652929084,Medium,Public,91.05281099593671
+12.217407161165816,4.985547729121721,Low,Public,82.71896676336443
+11.640964362394728,5.546284248003699,Medium,Private,69.9346158875592
+11.014548062214596,5.006422269810938,Medium,Public,67.64935185526126
+12.133349379178307,4.5636141094734315,High,Private,84.23186963179411
+12.338591180891346,4.890390196074169,High,Public,88.51523841754796
+12.764317982075054,4.911637040794664,High,Public,78.97695087572286
+11.297419775179286,4.629988970117872,Medium,Private,90.04471686942114
+9.665763839366292,4.741203937332898,Low,Public,84.75832826970245
+10.293427372866645,6.598647170504717,High,Private,81.5375593186961
+12.413017933016715,5.560919447941239,Low,Public,85.86468474972595
+8.366128658025527,4.704519681970839,High,Public,78.2616879259499
+10.737346617745802,5.696954404510736,High,Private,89.47009932886027
+9.213322375345278,4.66618051024399,Medium,Private,78.83659534978445
+10.057489645869635,6.173124637586641,High,Private,80.31432450947499
+12.556903725214598,5.369642192198094,High,Public,86.52977672665958
+10.382198136039806,4.892697855995771,Medium,Public,80.7902444633194
+10.092873096312298,5.447716925810278,Low,Public,82.97937251771499
+7.280287718040162,3.4291238479315007,High,Private,61.8541447425799
+11.492507132054442,3.8732325310901947,Medium,Private,80.37413031048145
+11.29096836228215,3.8060749936009444,High,Private,80.59325175286439
+14.32650944661092,5.142943094763707,Medium,Public,86.00319770010654
+9.384443530093998,6.732514999485092,Low,Public,82.78849178050342
+10.438300655327877,7.231300146192632,Low,Private,91.32938963278991
+10.498767367421511,5.638051101440757,Medium,Public,80.14569704252635
+13.15490655952695,5.500844474944457,Medium,Private,88.01968523500904
+9.809408935226095,3.1989422658472613,Medium,Public,82.44855507148085
+10.558043051540679,4.457326200265978,Low,Public,78.60989793318961
+11.215793019433079,4.212216248091761,High,Private,74.30759439920975
+10.373218246312716,4.379152401748884,Low,Public,80.39405934068839
+9.107132770898943,4.83185618141521,Medium,Public,64.45371912993032
+10.388179985796615,4.527909422302873,Medium,Private,66.07638891523413
+12.147263499719545,3.020700189624212,Low,Private,80.4906375194567
+7.9469694011778795,5.7479102561175335,Medium,Public,62.790952271122734
+10.265939348293752,3.9272569742601933,Low,Private,69.84692544583686
+8.599758370121654,5.239246864111149,Medium,Public,73.15789421885843
+12.390093257849685,7.0740826697499735,High,Private,81.7842287715949
+6.95362619043245,4.080615408413577,High,Public,69.28914297574967
+8.882156305456823,2.4697124772536703,Medium,Public,77.03323203063664
+10.754423750129043,4.713995778119889,Low,Private,79.77224522425757
+13.13104805846868,6.100959634998571,High,Public,81.21693356053791
+9.868499477854025,6.958347454220593,Low,Public,86.94672550096033
+8.889600946613603,3.7704504055475834,Medium,Private,66.29059131882762
+13.76231413888118,5.496699223236522,Low,Private,86.87909465647765
+7.1039721991675115,4.5346895432199466,High,Public,63.30954883810643
+5.602388086759836,4.894051645695864,Low,Private,69.80744317071617
+10.880028900106664,7.644343347017035,Low,Private,81.6334358012759
+8.995891551294777,3.497029671604847,Medium,Public,78.64851716762867
+7.9575343657385735,5.253739661494573,Medium,Public,77.39912557969015
+11.416712894598707,5.467693171765434,Medium,Private,78.09926888688724
+10.487601427542398,6.085786324939347,High,Private,76.20918413841261
+8.871842738526544,5.097983153470896,High,Private,73.81517654209888
+7.439391202658211,5.308050539504102,Medium,Public,82.21871551474428
+11.74491465656029,4.608351432578209,Medium,Public,88.4976988675283
+11.300402355917322,5.269126941245108,High,Public,75.66315419685904
+9.801648272441234,4.656807724502589,High,Public,80.5896348216431
+13.693273992095332,5.6215718898560505,High,Public,77.1617486744884
+7.859830467347695,4.630389663956366,High,Public,70.42853312534311
+6.948949658150522,5.377100393902187,Medium,Public,67.32572814284202
+8.616183860237511,4.970737186315022,High,Private,80.03487078780977
+9.908827967289005,6.126050250772169,Medium,Private,72.5531437117942
+10.486678898645383,4.948606276411514,Low,Public,80.54301838902039
+9.51752788428734,3.2269675317931186,Low,Public,73.87905767409418
+10.704110793028594,6.261921500713992,High,Public,79.92385968848062
+7.496921151619112,4.094267699137381,Low,Private,79.49315860566628
+12.88752920814652,4.34623366135118,Low,Public,91.09289486999137
+9.835697643214862,4.404338705956508,Low,Private,75.55593925309964
+12.234591663176255,6.374438093170817,High,Private,82.30125896310875
+10.685450692755408,2.8643257860213045,High,Private,68.13302347052694
+10.913506438307568,8.137748533659995,Low,Private,84.01234074090907
+11.139534560464408,6.056056809066051,Medium,Private,91.12967061325786
+10.89541712003463,5.223238914129159,Low,Public,78.98201591362337
+11.285445519735088,4.945105999383713,High,Public,83.63234645426465
+12.658305060264862,5.285554074857657,High,Private,76.25745461321989
+10.39304233940294,5.521122428379338,High,Public,76.89389367270688
+11.418007515177024,5.645215585950057,Low,Private,83.61616493829239
+9.820528611425546,5.555604466003448,Medium,Private,78.50377591209651
+12.880234430898945,5.089580681255248,High,Public,87.51817655573356
+8.64721539588146,4.802661579692674,High,Public,71.48506373850861
+13.60188086582163,4.848726716346488,Low,Public,75.72690906204465
+9.919684098711313,4.805091719647319,Medium,Public,82.65745692850497
+7.138449795763904,6.133770096867065,Low,Public,68.79680829047386
+10.256208829821578,5.593556722280862,High,Private,81.43577288057436
+8.637896685050238,2.0596113653357198,Medium,Public,72.61949475054614
+11.681287097977448,5.655900776529761,Medium,Public,73.52765938558957
+8.694752041395219,5.194736194718775,High,Public,80.42723546106873
+9.10763313357044,4.981291243554103,Medium,Private,72.85436715982719
+6.220918538108938,4.611481546911114,Medium,Public,69.63433250651936
+9.095387361501846,6.124112719986013,Low,Private,75.58603098315494
+5.152241346742087,5.947526462122858,High,Public,77.33872989783302
+6.832194353028553,4.227122332176391,High,Public,76.722523245242
+11.520829312288594,5.407052287124535,High,Private,72.09056413641039
+11.571600317301664,4.028343265458714,High,Public,90.47985114233235
+10.85091512356993,3.6203818352110693,High,Public,73.74086933738049
+8.066047713741574,4.3732827754100345,Medium,Private,83.2042029755074
+9.904577287716041,5.862393472382759,Low,Public,77.32169692330628
+9.992794921818863,5.953125045376087,High,Private,77.21069388124218
+7.6832706216149145,5.513085420097293,Low,Private,72.39953070798666
+13.006796603534301,5.725095786847997,Low,Private,82.59728866244313
+11.754724581151343,5.5161782871149585,Medium,Private,94.40569928209123
+9.558071652340672,4.3585184047770476,High,Private,75.27210491524374
+10.053771677989062,5.431922544673941,Medium,Public,76.74394501029558
+10.416765615895105,5.800409525227682,High,Public,79.91919930709645
+5.916530263151419,5.754291329813574,Medium,Public,65.62782153639967
+9.505645234958806,6.1889133714746425,Low,Private,86.28026319858083
+8.636031504004404,5.708303847201064,High,Private,68.47911460034474
+7.996759980210169,5.351448207541583,Low,Private,70.95064923637568
+9.43779941422809,6.070150238316428,Low,Private,82.66412573516477
+13.595373053699046,4.97347874075094,Medium,Public,84.86272487236167
+11.28168572253402,4.118125348772282,High,Public,81.4866353807267
+8.857642020434406,4.836933036056236,Low,Private,82.6365041783803
+11.145165562712318,4.255097355193551,Medium,Private,83.69198489987735

main.py

@@ -0,0 +1,38 @@
+# main.py
+from flask import Flask, jsonify, request
+from flask_cors import CORS
+import os
+import sys
+
+# Add the 'routers' and 'utils' directories to the Python path
+# This allows direct imports like 'from routers.preprocess_routes import preprocess_bp'
+script_dir = os.path.dirname(__file__)
+sys.path.insert(0, os.path.join(script_dir, 'routers'))
+sys.path.insert(0, os.path.join(script_dir, 'utils'))
+
+# Import Blueprints
+from routers.preprocess_routes import preprocess_bp
+from routers.discover_routes import discover_bp
+from routers.intervene_routes import intervene_bp
+from routers.treatment_routes import treatment_bp
+from routers.visualize_routes import visualize_bp
+
+app = Flask(__name__)
+CORS(app) # Enable CORS for frontend interaction
+
+# Register Blueprints
+app.register_blueprint(preprocess_bp, url_prefix='/preprocess')
+app.register_blueprint(discover_bp, url_prefix='/discover')
+app.register_blueprint(intervene_bp, url_prefix='/intervene')
+app.register_blueprint(treatment_bp, url_prefix='/treatment')
+app.register_blueprint(visualize_bp, url_prefix='/visualize')
+
+@app.route('/')
+def home():
+    return "Welcome to CausalBox Backend API!"
+
+if __name__ == '__main__':
+    # Ensure the 'data' directory exists for storing datasets
+    os.makedirs('data', exist_ok=True)
+    # Run the Flask app
+    app.run(debug=True, host='0.0.0.0', port=5000)

nginx.conf

@@ -0,0 +1,55 @@
+events {
+    worker_connections 1024;
+}
+
+http {
+    include mime.types;
+    default_type application/octet-stream;
+
+    sendfile on;
+    keepalive_timeout 65;
+
+    # Gzip settings (optional, but good for performance)
+    gzip on;
+    gzip_vary on;
+    gzip_proxied any;
+    gzip_comp_level 6;
+    gzip_types text/plain text/css application/json application/javascript text/xml application/xml application/xml+rss text/javascript;
+
+    server {
+        listen 7860; # Hugging Face Spaces default port for Docker SDK
+        server_name localhost;
+
+        # Serve Streamlit app at the root (/)
+        location / {
+            proxy_pass http://localhost:8501; # Streamlit's default port
+            proxy_http_version 1.1;
+            proxy_set_header Upgrade $http_upgrade;
+            proxy_set_header Connection "upgrade";
+            proxy_set_header Host $host;
+            proxy_cache_bypass $http_upgrade;
+            proxy_read_timeout 300s; # Increase if Streamlit app takes long to load/respond
+            proxy_send_timeout 300s;
+        }
+
+        # Serve Flask API at /api/ (or adjust as per your Flask app's routing)
+        # Note: Your Streamlit app should call http://localhost:5000/preprocess/upload etc. internally.
+        # This Nginx block is for if you wanted to expose Flask directly under /api/,
+        # but for internal communication, Streamlit calls Flask's internal port directly.
+        # However, for the Streamlit app to communicate with Flask, it *must* use `http://localhost:5000`.
+        # The Nginx here is primarily for serving the Streamlit app to the public.
+        # If your Streamlit app's FLASK_API_URL is "http://localhost:5000", Nginx doesn't mediate these internal calls.
+        # The Nginx config below is for direct external access to Flask, which is usually not needed when Streamlit is the frontend.
+        # I'll keep it simple for now, focusing on Streamlit at root.
+
+        # If you wanted to expose Flask on a subpath of the Space URL (e.g., yourspace.hf.space/flask_api)
+        # you would add a location block like this, but your Streamlit app would still call localhost:5000 internally.
+        # location /flask_api/ {
+        #     proxy_pass http://localhost:5000/;
+        #     proxy_set_header Host $host;
+        #     proxy_set_header X-Real-IP $remote_addr;
+        #     proxy_set_header X-Forwarded-For $proxy_add_x_forwarded_for;
+        #     proxy_set_header X-Forwarded-Proto $scheme;
+        # }
+    }
+}

requirements.txt

@@ -1,3 +1,11 @@
-altair
+Flask
+flask-cors
 pandas
-streamlit
+numpy
+scikit-learn
+causal-learn # For PC algorithm
+networkx
+plotly
+streamlit
+requests # For Streamlit to communicate with Flask
+watchfiles # For auto_refresh.py (if implemented for background tasks)

routers/discover_routes.py

@@ -0,0 +1,43 @@
+# routers/discover_routes.py
+from flask import Blueprint, request, jsonify
+import pandas as pd
+from utils.casual_algorithms import CausalDiscoveryAlgorithms
+import logging
+
+discover_bp = Blueprint('discover', __name__)
+logger = logging.getLogger(__name__)
+
+causal_discovery_algorithms = CausalDiscoveryAlgorithms()
+
+@discover_bp.route('/', methods=['POST'])
+def discover_causal_graph():
+    """
+    Discover causal graph from input data using selected algorithm.
+    Expects 'data' key with list of dicts (preprocessed DataFrame records) and 'algorithm' string.
+    Returns graph as adjacency matrix.
+    """
+    try:
+        payload = request.json
+        if not payload or 'data' not in payload:
+            return jsonify({"detail": "Invalid request payload: 'data' key missing."}), 400
+
+        df = pd.DataFrame(payload["data"])
+        algorithm = payload.get("algorithm", "pc").lower() # Default to PC
+
+        logger.info(f"Received discovery request with algorithm: {algorithm}, data shape: {df.shape}")
+
+        if algorithm == "pc":
+            adj_matrix = causal_discovery_algorithms.pc_algorithm(df)
+        elif algorithm == "ges":
+            adj_matrix = causal_discovery_algorithms.ges_algorithm(df) # Placeholder
+        elif algorithm == "notears":
+            adj_matrix = causal_discovery_algorithms.notears_algorithm(df) # Placeholder
+        else:
+            return jsonify({"detail": f"Unsupported causal discovery algorithm: {algorithm}"}), 400
+
+        logger.info(f"Causal graph discovered using {algorithm}.")
+        return jsonify({"graph": adj_matrix.tolist()})
+
+    except Exception as e:
+        logger.exception(f"Error in causal discovery: {str(e)}")
+        return jsonify({"detail": f"Causal discovery failed: {str(e)}"}), 500

routers/intervene_routes.py

@@ -0,0 +1,54 @@
+# routers/intervene_routes.py
+from flask import Blueprint, request, jsonify
+import pandas as pd
+from utils.do_calculus import DoCalculus # Will be used for more advanced intervention
+import networkx as nx # Assuming graph is passed or re-discovered
+import logging
+
+intervene_bp = Blueprint('intervene', __name__)
+logger = logging.getLogger(__name__)
+
+@intervene_bp.route('/', methods=['POST'])
+def perform_intervention():
+    """
+    Perform causal intervention on data.
+    Expects 'data' (list of dicts), 'intervention_var' (column name),
+    'intervention_value' (numeric), and optionally 'graph' (adjacency matrix).
+    Returns intervened data as list of dicts.
+    """
+    try:
+        payload = request.json
+        if not payload or 'data' not in payload or 'intervention_var' not in payload or 'intervention_value' not in payload:
+            return jsonify({"detail": "Missing required intervention parameters."}), 400
+
+        df = pd.DataFrame(payload["data"])
+        intervention_var = payload["intervention_var"]
+        intervention_value = payload["intervention_value"]
+        graph_adj_matrix = payload.get("graph") # Optional: pass pre-discovered graph
+
+        logger.info(f"Intervention request: var={intervention_var}, value={intervention_value}, data shape: {df.shape}")
+
+        if intervention_var not in df.columns:
+            return jsonify({"detail": f"Intervention variable '{intervention_var}' not found in data"}), 400
+
+        # For a more advanced do-calculus, you'd need the graph structure.
+        # Here, a simplified direct intervention is applied first.
+        # If graph_adj_matrix is provided, you could convert it to networkx.
+        # For full do-calculus, the DoCalculus class would need a proper graph.
+        
+        df_intervened = df.copy()
+        df_intervened[intervention_var] = intervention_value
+        
+        # Placeholder for propagating effects using a graph if provided
+        # if graph_adj_matrix:
+        #    graph_nx = nx.from_numpy_array(np.array(graph_adj_matrix), create_using=nx.DiGraph)
+        #    do_calculus_engine = DoCalculus(graph_nx)
+        #    df_intervened = do_calculus_engine.intervene(df_intervened, intervention_var, intervention_value)
+        #    logger.info("Propagated effects using do-calculus (simplified).")
+
+        logger.info(f"Intervened data shape: {df_intervened.shape}")
+        return jsonify({"intervened_data": df_intervened.to_dict(orient="records")})
+
+    except Exception as e:
+        logger.exception(f"Error in intervention: {str(e)}")
+        return jsonify({"detail": f"Intervention failed: {str(e)}"}), 500

routers/preprocess_routes.py

@@ -0,0 +1,56 @@
+# routers/preprocess_routes.py
+from flask import Blueprint, request, jsonify
+import pandas as pd
+from utils.preprocessor import DataPreprocessor
+import logging
+
+preprocess_bp = Blueprint('preprocess', __name__)
+
+# Set up logging
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
+
+preprocessor = DataPreprocessor()
+
+@preprocess_bp.route('/upload', methods=['POST'])
+def upload_file():
+    """
+    Upload and preprocess a CSV file.
+    Returns preprocessed DataFrame columns and data as JSON.
+    Optional limit_rows to reduce response size for testing.
+    """
+    if 'file' not in request.files:
+        return jsonify({"detail": "No file part in the request"}), 400
+    file = request.files['file']
+    if file.filename == '':
+        return jsonify({"detail": "No selected file"}), 400
+    if not file.filename.lower().endswith('.csv'):
+        return jsonify({"detail": "Only CSV files are supported"}), 400
+
+    limit_rows = request.args.get('limit_rows', type=int)
+
+    try:
+        logger.info(f"Received file: {file.filename}")
+        df = pd.read_csv(file)
+        logger.info(f"CSV read successfully, shape: {df.shape}")
+
+        processed_df = preprocessor.preprocess(df)
+        if limit_rows:
+            processed_df = processed_df.head(limit_rows)
+            logger.info(f"Limited to {limit_rows} rows.")
+
+        response = {
+            "columns": list(processed_df.columns),
+            "data": processed_df.to_dict(orient="records")
+        }
+        logger.info(f"Preprocessed {len(response['data'])} records.")
+        return jsonify(response)
+    except pd.errors.EmptyDataError:
+        logger.error("Empty CSV file uploaded.")
+        return jsonify({"detail": "Empty CSV file"}), 400
+    except pd.errors.ParserError:
+        logger.error("Invalid CSV format.")
+        return jsonify({"detail": "Invalid CSV format"}), 400
+    except Exception as e:
+        logger.exception(f"Unexpected error during file processing: {str(e)}")
+        return jsonify({"detail": f"Failed to process file: {str(e)}"}), 500

routers/treatment_routes.py

@@ -0,0 +1,54 @@
+# routers/treatment_routes.py
+from flask import Blueprint, request, jsonify
+import pandas as pd
+from utils.treatment_effects import TreatmentEffectAlgorithms
+import logging
+
+treatment_bp = Blueprint('treatment', __name__)
+logger = logging.getLogger(__name__)
+
+treatment_effect_algorithms = TreatmentEffectAlgorithms()
+
+@treatment_bp.route('/estimate_ate', methods=['POST'])
+def estimate_ate():
+    """
+    Estimate Average Treatment Effect (ATE) or Conditional Treatment Effect (CATE).
+    Expects 'data' (list of dicts), 'treatment_col', 'outcome_col', 'covariates' (list of column names),
+    and 'method' (string for estimation method).
+    Returns ATE/CATE as float or dictionary.
+    """
+    try:
+        payload = request.json
+        if not payload or 'data' not in payload or 'treatment_col' not in payload or 'outcome_col' not in payload or 'covariates' not in payload:
+            return jsonify({"detail": "Missing required ATE estimation parameters."}), 400
+
+        df = pd.DataFrame(payload["data"])
+        treatment_col = payload["treatment_col"]
+        outcome_col = payload["outcome_col"]
+        covariates = payload["covariates"]
+        method = payload.get("method", "linear_regression").lower() # Default to linear regression
+
+        logger.info(f"ATE/CATE request: treatment={treatment_col}, outcome={outcome_col}, method={method}, data shape: {df.shape}")
+
+        if not all(col in df.columns for col in [treatment_col, outcome_col] + covariates):
+            return jsonify({"detail": "Invalid column names provided for ATE estimation."}), 400
+
+        if method == "linear_regression":
+            result = treatment_effect_algorithms.linear_regression_ate(df, treatment_col, outcome_col, covariates)
+        elif method == "propensity_score_matching":
+            result = treatment_effect_algorithms.propensity_score_matching(df, treatment_col, outcome_col, covariates) # Placeholder
+        elif method == "inverse_propensity_weighting":
+            result = treatment_effect_algorithms.inverse_propensity_weighting(df, treatment_col, outcome_col, covariates) # Placeholder
+        elif method == "t_learner":
+            result = treatment_effect_algorithms.t_learner(df, treatment_col, outcome_col, covariates) # Placeholder
+        elif method == "s_learner":
+            result = treatment_effect_algorithms.s_learner(df, treatment_col, outcome_col, covariates) # Placeholder
+        else:
+            return jsonify({"detail": f"Unsupported treatment effect estimation method: {method}"}), 400
+
+        logger.info(f"Estimated ATE/CATE using {method}: {result}")
+        return jsonify({"result": result})
+
+    except Exception as e:
+        logger.exception(f"Error in ATE/CATE estimation: {str(e)}")
+        return jsonify({"detail": f"ATE/CATE estimation failed: {str(e)}"}), 500

routers/visualize_routes.py

@@ -0,0 +1,43 @@
+# routers/visualize_routes.py
+from flask import Blueprint, request, jsonify
+import pandas as pd
+from utils.graph_utils import visualize_graph
+import networkx as nx
+import numpy as np
+import logging
+
+visualize_bp = Blueprint('visualize', __name__)
+logger = logging.getLogger(__name__)
+
+@visualize_bp.route('/graph', methods=['POST'])
+def get_graph_visualization():
+    """
+    Generate a causal graph visualization from an adjacency matrix.
+    Expects 'graph' (adjacency matrix as list of lists) and 'nodes' (list of node names).
+    Returns Plotly JSON for the graph.
+    """
+    try:
+        payload = request.json
+        if not payload or 'graph' not in payload or 'nodes' not in payload:
+            return jsonify({"detail": "Missing 'graph' or 'nodes' in request payload."}), 400
+
+        adj_matrix = np.array(payload["graph"])
+        nodes = payload["nodes"]
+
+        logger.info(f"Received graph visualization request for {len(nodes)} nodes.")
+
+        # Reconstruct networkx graph from adjacency matrix and node names
+        graph_nx = nx.from_numpy_array(adj_matrix, create_using=nx.DiGraph)
+        
+        # Map integer node labels back to original column names if necessary
+        # Assuming nodes are ordered as they appear in the original dataframe or provided in 'nodes'
+        mapping = {i: node_name for i, node_name in enumerate(nodes)}
+        graph_nx = nx.relabel_nodes(graph_nx, mapping)
+
+        graph_json = visualize_graph(graph_nx)
+        logger.info("Generated graph visualization JSON.")
+        return jsonify({"graph": graph_json})
+
+    except Exception as e:
+        logger.exception(f"Error generating graph visualization: {str(e)}")
+        return jsonify({"detail": f"Failed to generate visualization: {str(e)}"}), 500

scripts/generate_data.py

@@ -0,0 +1,29 @@
+# scripts/generate_data.py
+import numpy as np
+import pandas as pd
+import os
+
+def generate_dataset(n_samples=1000):
+    np.random.seed(42)
+    study_hours = np.random.normal(10, 2, n_samples)
+    tuition_hours = np.random.normal(5, 1, n_samples)
+    parental_education = np.random.choice(['High', 'Medium', 'Low'], n_samples)
+    school_type = np.random.choice(['Public', 'Private'], n_samples)
+    exam_score = 50 + 2 * study_hours + 1.5 * tuition_hours + np.random.normal(0, 5, n_samples)
+
+    df = pd.DataFrame({
+        'StudyHours': study_hours,
+        'TuitionHours': tuition_hours,
+        'ParentalEducation': parental_education,
+        'SchoolType': school_type,
+        'FinalExamScore': exam_score
+    })
+    
+    # Ensure data directory exists
+    os.makedirs('../data', exist_ok=True)
+    df.to_csv('../data/sample_dataset.csv', index=False)
+    return df
+
+if __name__ == "__main__":
+    generate_dataset()
+    print("Dataset generated and saved to ../data/sample_dataset.csv")

src/streamlit_app.py

@@ -1,40 +0,0 @@
-import altair as alt
-import numpy as np
-import pandas as pd
-import streamlit as st
-
-"""
-# Welcome to Streamlit!
-
-Edit `/streamlit_app.py` to customize this app to your heart's desire :heart:.
-If you have any questions, checkout our [documentation](https://docs.streamlit.io) and [community
-forums](https://discuss.streamlit.io).
-
-In the meantime, below is an example of what you can do with just a few lines of code:
-"""
-
-num_points = st.slider("Number of points in spiral", 1, 10000, 1100)
-num_turns = st.slider("Number of turns in spiral", 1, 300, 31)
-
-indices = np.linspace(0, 1, num_points)
-theta = 2 * np.pi * num_turns * indices
-radius = indices
-
-x = radius * np.cos(theta)
-y = radius * np.sin(theta)
-
-df = pd.DataFrame({
-    "x": x,
-    "y": y,
-    "idx": indices,
-    "rand": np.random.randn(num_points),
-})
-
-st.altair_chart(alt.Chart(df, height=700, width=700)
-    .mark_point(filled=True)
-    .encode(
-        x=alt.X("x", axis=None),
-        y=alt.Y("y", axis=None),
-        color=alt.Color("idx", legend=None, scale=alt.Scale()),
-        size=alt.Size("rand", legend=None, scale=alt.Scale(range=[1, 150])),
-    ))

start.sh

@@ -0,0 +1,24 @@
+#!/bin/bash
+
+# Start Flask backend using Gunicorn in the background
+# Make sure main.py is in flask_backend/ and you reference it correctly.
+# The `wsgi:app` assumes your Flask app instance is named `app` in `wsgi.py` (common Gunicorn setup)
+# or you can point directly to `main:app` if `app` is defined in main.py.
+# Based on your main.py, it's `main:app`.
+echo "Starting Flask backend..."
+gunicorn flask_backend.main:app --workers 1 --bind 0.0.0.0:5000 --timeout 600 --log-level debug &
+
+# Wait a bit for Flask to start (optional, but can help prevent connection errors)
+sleep 5
+
+# Start Streamlit frontend in the background
+# Make sure streamlit_app.py is in streamlit_frontend/
+echo "Starting Streamlit frontend..."
+streamlit run streamlit_frontend/streamlit_app.py --server.port 8501 --server.enableCORS false --server.enableXsrfProtection false --server.fileWatcherType none &
+
+# Wait a bit for Streamlit to start (optional)
+sleep 5
+
+# Start Nginx in the foreground (important for Docker containers)
+echo "Starting Nginx..."
+nginx -g "daemon off;"

streamlit_app.py

@@ -0,0 +1,308 @@
+# streamlit_app.py
+import streamlit as st
+import pandas as pd
+import requests
+import json
+import plotly.express as px
+import plotly.graph_objects as go
+import numpy as np # For random array in placeholders
+import os
+
+# Configuration
+FLASK_API_URL = "http://localhost:5000" # Ensure this matches your Flask app's host and port
+
+st.set_page_config(layout="wide", page_title="CausalBox Toolkit")
+
+st.title("🔬 CausalBox: A Causal Inference Toolkit")
+st.markdown("Uncover causal relationships, simulate interventions, and estimate treatment effects.")
+
+# --- Session State Initialization ---
+if 'processed_data' not in st.session_state:
+    st.session_state.processed_data = None
+if 'processed_columns' not in st.session_state:
+    st.session_state.processed_columns = None
+if 'causal_graph_adj' not in st.session_state:
+    st.session_state.causal_graph_adj = None
+if 'causal_graph_nodes' not in st.session_state:
+    st.session_state.causal_graph_nodes = None
+
+# --- Data Preprocessing Module ---
+st.header("1. Data Preprocessor 🧹")
+st.write("Upload your CSV dataset or use a generated sample dataset.")
+
+# Option to use generated sample dataset
+if st.button("Use Sample Dataset (sample_dataset.csv)"):
+    # In a real scenario, Streamlit would serve the file or you'd load it directly if local.
+    # For this setup, we assume the Flask backend can access it or you manually upload it once.
+    # For demonstration, we'll simulate loading a generic DataFrame.
+    # In a full deployment, you'd have a mechanism to either:
+    #   a) Have Flask serve the sample file, or
+    #   b) Directly load it in Streamlit if the app and data are co-located.
+    try:
+        # Assuming the sample dataset is accessible or you are testing locally with `scripts/generate_data.py`
+        # and then manually uploading this generated file.
+        # For simplicity, we'll create a dummy df here if not actually uploaded.
+        sample_df_path = "data/sample_dataset.csv" # Path relative to main.py or Streamlit app execution
+        if os.path.exists(sample_df_path):
+             sample_df = pd.read_csv(sample_df_path)
+             st.success(f"Loaded sample dataset from {sample_df_path}. Please upload this file if running from different directory.")
+        else:
+            st.warning("Sample dataset not found at data/sample_dataset.csv.")
+            # Dummy DataFrame for demonstration if sample file isn't found
+            sample_df = pd.DataFrame(np.random.rand(10, 5), columns=[f'col_{i}' for i in range(5)])
+
+        # Convert to JSON for Flask API call
+        files = {'file': ('sample_dataset.csv', sample_df.to_csv(index=False), 'text/csv')}
+        response = requests.post(f"{FLASK_API_URL}/preprocess/upload", files=files)
+        
+        if response.status_code == 200:
+            result = response.json()
+            st.session_state.processed_data = result['data']
+            st.session_state.processed_columns = result['columns']
+            st.success("Sample dataset preprocessed successfully!")
+            st.dataframe(pd.DataFrame(st.session_state.processed_data).head()) # Display first few rows
+        else:
+            st.error(f"Error preprocessing sample dataset: {response.json().get('detail', 'Unknown error')}")
+    except Exception as e:
+        st.error(f"Could not load or process sample dataset: {e}")
+
+
+uploaded_file = st.file_uploader("Choose a CSV file", type="csv")
+if uploaded_file is not None:
+    st.info("Uploading and preprocessing data...")
+    files = {'file': (uploaded_file.name, uploaded_file.getvalue(), 'text/csv')}
+    try:
+        response = requests.post(f"{FLASK_API_URL}/preprocess/upload", files=files)
+        if response.status_code == 200:
+            result = response.json()
+            st.session_state.processed_data = result['data']
+            st.session_state.processed_columns = result['columns']
+            st.success("File preprocessed successfully!")
+            st.dataframe(pd.DataFrame(st.session_state.processed_data).head()) # Display first few rows
+        else:
+            st.error(f"Error during preprocessing: {response.json().get('detail', 'Unknown error')}")
+    except requests.exceptions.ConnectionError:
+        st.error(f"Could not connect to Flask API at {FLASK_API_URL}. Please ensure the backend is running.")
+    except Exception as e:
+        st.error(f"An unexpected error occurred: {e}")
+
+# --- Causal Discovery Module ---
+st.header("2. Causal Discovery 🕵️‍♂️")
+if st.session_state.processed_data:
+    st.write("Learn the causal structure from your preprocessed data.")
+    
+    discovery_algo = st.selectbox(
+        "Select Causal Discovery Algorithm:",
+        ("PC Algorithm", "GES (Greedy Equivalence Search) - Placeholder", "NOTEARS - Placeholder")
+    )
+    
+    if st.button("Discover Causal Graph"):
+        st.info(f"Discovering graph using {discovery_algo}...")
+        algo_map = {
+            "PC Algorithm": "pc",
+            "GES (Greedy Equivalence Search) - Placeholder": "ges",
+            "NOTEARS - Placeholder": "notears"
+        }
+        selected_algo_code = algo_map[discovery_algo]
+
+        try:
+            response = requests.post(
+                f"{FLASK_API_URL}/discover/",
+                json={"data": st.session_state.processed_data, "algorithm": selected_algo_code}
+            )
+            if response.status_code == 200:
+                result = response.json()
+                st.session_state.causal_graph_adj = result['graph']
+                st.session_state.causal_graph_nodes = st.session_state.processed_columns
+                st.success("Causal graph discovered!")
+                st.subheader("Causal Graph Visualization")
+                # Visualization will be handled by the Causal Graph Visualizer section
+            else:
+                st.error(f"Error during causal discovery: {response.json().get('detail', 'Unknown error')}")
+        except requests.exceptions.ConnectionError:
+            st.error(f"Could not connect to Flask API at {FLASK_API_URL}. Please ensure the backend is running.")
+        except Exception as e:
+            st.error(f"An unexpected error occurred: {e}")
+else:
+    st.info("Please preprocess data first to enable causal discovery.")
+
+# --- Causal Graph Visualizer Module ---
+st.header("3. Causal Graph Visualizer 📊")
+if st.session_state.causal_graph_adj and st.session_state.causal_graph_nodes:
+    st.write("Interactive visualization of the discovered causal graph.")
+    try:
+        response = requests.post(
+            f"{FLASK_API_URL}/visualize/graph",
+            json={"graph": st.session_state.causal_graph_adj, "nodes": st.session_state.causal_graph_nodes}
+        )
+        if response.status_code == 200:
+            graph_json = response.json()['graph']
+            fig = go.Figure(json.loads(graph_json))
+            st.plotly_chart(fig, use_container_width=True)
+            st.markdown("""
+            **Graph Explanation:**
+            * **Nodes:** Represent variables in your dataset.
+            * **Arrows (Edges):** Indicate a direct causal influence from one variable (the tail) to another (the head).
+            * **No Arrow:** Suggests no direct causal relationship was found, or the relationship is mediated by other variables.
+
+            This graph helps answer "Why did it happen?" by showing the structural relationships.
+            """)
+        else:
+            st.error(f"Error visualizing graph: {response.json().get('detail', 'Unknown error')}")
+    except requests.exceptions.ConnectionError:
+        st.error(f"Could not connect to Flask API at {FLASK_API_URL}. Please ensure the backend is running.")
+    except Exception as e:
+        st.error(f"An unexpected error occurred during visualization: {e}")
+else:
+    st.info("Please discover a causal graph first to visualize it.")
+
+
+# --- Do-Calculus Engine Module ---
+st.header("4. Do-Calculus Engine 🧪")
+if st.session_state.processed_data and st.session_state.causal_graph_adj:
+    st.write("Simulate interventions and observe their effects based on the causal graph.")
+    
+    intervention_var = st.selectbox(
+        "Select variable to intervene on:",
+        st.session_state.processed_columns,
+        key="inter_var_select"
+    )
+    # Attempt to infer type for intervention_value input
+    # Simplified approach: assuming numerical for now due to preprocessor output
+    if intervention_var and isinstance(st.session_state.processed_data[0][intervention_var], (int, float)):
+        intervention_value = st.number_input(f"Set '{intervention_var}' to value:", key="inter_val_input")
+    else: # Treat as string/categorical for input, then try to preprocess for API
+        intervention_value = st.text_input(f"Set '{intervention_var}' to value:", key="inter_val_input_text")
+        st.warning("Categorical intervention values might require specific encoding logic on the backend.")
+
+    if st.button("Perform Intervention"):
+        st.info(f"Performing intervention: do('{intervention_var}' = {intervention_value})...")
+        try:
+            response = requests.post(
+                f"{FLASK_API_URL}/intervene/",
+                json={
+                    "data": st.session_state.processed_data,
+                    "intervention_var": intervention_var,
+                    "intervention_value": intervention_value,
+                    "graph": st.session_state.causal_graph_adj # Pass graph for advanced do-calculus
+                }
+            )
+            if response.status_code == 200:
+                intervened_data = pd.DataFrame(response.json()['intervened_data'])
+                st.success("Intervention simulated successfully!")
+                st.subheader("Intervened Data (First 10 rows)")
+                st.dataframe(intervened_data.head(10))
+                
+                # Simple comparison visualization (e.g., histogram of outcome variable)
+                if st.session_state.processed_columns and 'FinalExamScore' in st.session_state.processed_columns:
+                    original_df = pd.DataFrame(st.session_state.processed_data)
+                    fig_dist = go.Figure()
+                    fig_dist.add_trace(go.Histogram(x=original_df['FinalExamScore'], name='Original', opacity=0.7))
+                    fig_dist.add_trace(go.Histogram(x=intervened_data['FinalExamScore'], name='Intervened', opacity=0.0))
+                    
+                    st.plotly_chart(fig_dist, use_container_width=True)
+                    st.markdown("""
+                    **Intervention Explanation:**
+                    * By simulating `do(X=x)`, we are forcing the value of X, effectively breaking its causal links from its parents.
+                    * The graph above shows the distribution of a key outcome variable (e.g., `FinalExamScore`) before and after the intervention.
+                    * This helps answer "What if we do this instead?" by showing the predicted outcome.
+                    """)
+                else:
+                    st.info("Consider adding a relevant outcome variable to your dataset for better intervention analysis.")
+            else:
+                st.error(f"Error during intervention: {response.json().get('detail', 'Unknown error')}")
+        except requests.exceptions.ConnectionError:
+            st.error(f"Could not connect to Flask API at {FLASK_API_URL}. Please ensure the backend is running.")
+        except Exception as e:
+            st.error(f"An unexpected error occurred during intervention: {e}")
+else:
+    st.info("Please preprocess data and discover a causal graph first to perform interventions.")
+
+# --- Treatment Effect Estimator Module ---
+st.header("5. Treatment Effect Estimator 🎯")
+if st.session_state.processed_data:
+    st.write("Estimate Average Treatment Effect (ATE) or Conditional Treatment Effect (CATE).")
+    
+    col1, col2 = st.columns(2)
+    with col1:
+        treatment_col = st.selectbox(
+            "Select Treatment Variable:",
+            st.session_state.processed_columns,
+            key="treat_col_select"
+        )
+    with col2:
+        outcome_col = st.selectbox(
+            "Select Outcome Variable:",
+            st.session_state.processed_columns,
+            key="outcome_col_select"
+        )
+    
+    all_cols_except_treat_outcome = [col for col in st.session_state.processed_columns if col not in [treatment_col, outcome_col]]
+    covariates = st.multiselect(
+        "Select Covariates (confounders):",
+        all_cols_except_treat_outcome,
+        default=all_cols_except_treat_outcome, # Default to all other columns
+        key="covariates_select"
+    )
+
+    estimation_method = st.selectbox(
+        "Select Estimation Method:",
+        (
+            "Linear Regression ATE",
+            "Propensity Score Matching - Placeholder",
+            "Inverse Propensity Weighting - Placeholder",
+            "T-learner - Placeholder",
+            "S-learner - Placeholder"
+        )
+    )
+
+    if st.button("Estimate Treatment Effect"):
+        st.info(f"Estimating treatment effect using {estimation_method}...")
+        method_map = {
+            "Linear Regression ATE": "linear_regression",
+            "Propensity Score Matching - Placeholder": "propensity_score_matching",
+            "Inverse Propensity Weighting - Placeholder": "inverse_propensity_weighting",
+            "T-learner - Placeholder": "t_learner",
+            "S-learner - Placeholder": "s_learner"
+        }
+        selected_method_code = method_map[estimation_method]
+
+        try:
+            response = requests.post(
+                f"{FLASK_API_URL}/treatment/estimate_ate",
+                json={
+                    "data": st.session_state.processed_data,
+                    "treatment_col": treatment_col,
+                    "outcome_col": outcome_col,
+                    "covariates": covariates,
+                    "method": selected_method_code
+                }
+            )
+            if response.status_code == 200:
+                ate_result = response.json()['result']
+                st.success(f"Treatment effect estimated using {estimation_method}:")
+                st.write(f"**Estimated ATE: {ate_result:.4f}**")
+                st.markdown("""
+                **Treatment Effect Explanation:**
+                * **Average Treatment Effect (ATE):** Measures the average causal effect of a treatment (e.g., `StudyHours`) on an outcome (e.g., `FinalExamScore`) across the entire population.
+                * It answers "How much does doing X cause a change in Y?".
+                * This estimation attempts to control for confounders (variables that influence both treatment and outcome) to isolate the true causal effect.
+                """)
+            else:
+                st.error(f"Error during ATE estimation: {response.json().get('detail', 'Unknown error')}")
+        except requests.exceptions.ConnectionError:
+            st.error(f"Could not connect to Flask API at {FLASK_API_URL}. Please ensure the backend is running.")
+        except Exception as e:
+            st.error(f"An unexpected error occurred during ATE estimation: {e}")
+else:
+    st.info("Please preprocess data first to estimate treatment effects.")
+
+# --- Optional Advanced Add-Ons (Future Considerations) ---
+st.header("Optional Advanced Add-Ons (Future Work) 🚀")
+st.markdown("""
+-   **🔄 Auto-causal graph refresh if dataset updates:** This would involve setting up a background process (e.g., using `watchfiles` with a separate service or integrated carefully into Flask/Streamlit) that monitors changes to the source CSV file. Upon detection, it would re-run the preprocessing and causal discovery, updating the dashboard live. This requires more complex architecture (e.g., WebSockets for real-time updates to Streamlit or scheduled background tasks).
+-   **🕰️ Time-Series Causal Discovery (e.g., Granger Causality):** This requires handling time-indexed data and implementing algorithms specifically designed for temporal causal relationships. It would involve a separate data input and discovery module.
+""")
+
+st.markdown("---")
+st.info("Developed by CausalBox Team. For support, please contact us.")

utils/casual_algorithms.py

@@ -0,0 +1,64 @@
+# utils/causal_algorithms.py
+import networkx as nx
+import pandas as pd
+import numpy as np
+from causallearn.search.ConstraintBased.PC import pc
+# from causallearn.search.ScoreBased.GES import ges # Example import for GES
+# from notears import notears_linear # Example import for NOTEARS
+
+class CausalDiscoveryAlgorithms:
+    def pc_algorithm(self, df, alpha=0.05):
+        """
+        Run PC algorithm to learn causal graph.
+        Returns a directed graph's adjacency matrix.
+        Requires numerical data.
+        """
+        data_array = df.to_numpy()
+        cg = pc(data_array, alpha=alpha, indep_test="fisherz")
+        adj_matrix = cg.G.graph
+        return adj_matrix
+
+    def ges_algorithm(self, df):
+        """
+        Placeholder for GES (Greedy Equivalence Search) algorithm.
+        Returns a directed graph's adjacency matrix.
+        You would implement or integrate the GES algorithm here.
+        """
+        # Example: G, edges = ges(data_array)
+        # For now, returning a simplified correlation-based graph for demonstration
+        print("GES algorithm is a placeholder. Using a simplified correlation-based graph.")
+        G = nx.DiGraph()
+        nodes = df.columns
+        G.add_nodes_from(nodes)
+        corr_matrix = df.corr().abs()
+        threshold = 0.3
+        for i, col1 in enumerate(nodes):
+            for col2 in nodes[i+1:]:
+                if corr_matrix.loc[col1, col2] > threshold:
+                    if np.random.rand() > 0.5:
+                        G.add_edge(col1, col2)
+                    else:
+                        G.add_edge(col2, col1)
+        return nx.to_numpy_array(G) # Convert to adjacency matrix
+
+    def notears_algorithm(self, df):
+        """
+        Placeholder for NOTEARS algorithm.
+        Returns a directed graph's adjacency matrix.
+        You would implement or integrate the NOTEARS algorithm here.
+        """
+        # Example: W_est = notears_linear(data_array)
+        print("NOTEARS algorithm is a placeholder. Using a simplified correlation-based graph.")
+        G = nx.DiGraph()
+        nodes = df.columns
+        G.add_nodes_from(nodes)
+        corr_matrix = df.corr().abs()
+        threshold = 0.3
+        for i, col1 in enumerate(nodes):
+            for col2 in nodes[i+1:]:
+                if corr_matrix.loc[col1, col2] > threshold:
+                    if np.random.rand() > 0.5:
+                        G.add_edge(col1, col2)
+                    else:
+                        G.add_edge(col2, col1)
+        return nx.to_numpy_array(G) # Convert to adjacency matrix

utils/do_calculus.py

@@ -0,0 +1,52 @@
+# utils/do_calculus.py
+import pandas as pd
+import numpy as np
+import networkx as nx
+
+class DoCalculus:
+    def __init__(self, graph):
+        self.graph = graph
+
+    def intervene(self, data, intervention_var, intervention_value):
+        """
+        Simulate do(X=x) intervention on a variable.
+        Returns intervened DataFrame.
+        This is a simplified implementation.
+        """
+        intervened_data = data.copy()
+        
+        # Direct intervention: set the value
+        intervened_data[intervention_var] = intervention_value
+
+        # Propagate effects (simplified linear model) - needs graph
+        # For a true do-calculus, you'd prune the graph and re-estimate based on parents
+        # For demonstration, this still uses a simplified propagation.
+        try:
+            # Ensure graph is connected and topological sort is possible
+            if self.graph and not nx.is_directed_acyclic_graph(self.graph):
+                print("Warning: Graph is not a DAG. Topological sort may fail or be incorrect for do-calculus.")
+            
+            # This simplified propagation is a conceptual placeholder
+            for node in nx.topological_sort(self.graph):
+                if node == intervention_var:
+                    continue # Do not propagate back to the intervened variable
+                
+                parents = list(self.graph.predecessors(node))
+                if parents:
+                    # Very simplified linear model to show propagation
+                    # In reality, you'd use learned coefficients or structural equations
+                    combined_effect = np.zeros(len(intervened_data))
+                    for p in parents:
+                        if p in intervened_data.columns:
+                            # Use a fixed random coefficient for demonstration
+                            coeff = 0.5 
+                            combined_effect += intervened_data[p].to_numpy() * coeff
+                    
+                    # Add a small random noise to simulate uncertainty
+                    intervened_data[node] += combined_effect + np.random.normal(0, 0.1, len(intervened_data))
+        except Exception as e:
+            print(f"Could not perform full propagation due to graph issues or simplification: {e}")
+            # Fallback to direct intervention only if graph logic fails
+            pass # The direct intervention `intervened_data[intervention_var] = intervention_value` is already applied
+
+        return intervened_data

utils/graph_utils.py

@@ -0,0 +1,60 @@
+# utils/graph_utils.py
+import networkx as nx
+import plotly.graph_objects as go
+
+def visualize_graph(graph):
+    """
+    Visualize a causal graph using Plotly.
+    Returns Plotly figure as JSON.
+    """
+    # Use a fixed seed for layout reproducibility (optional)
+    pos = nx.spring_layout(graph, seed=42)
+    
+    edge_x, edge_y = [], []
+    for edge in graph.edges():
+        x0, y0 = pos[edge[0]]
+        x1, y1 = pos[edge[1]]
+        edge_x.extend([x0, x1, None])
+        edge_y.extend([y0, y1, None])
+
+    edge_trace = go.Scatter(
+        x=edge_x, y=edge_y,
+        line=dict(width=1, color='#888'),
+        mode='lines',
+        hoverinfo='none'
+    )
+
+    node_x, node_y = [], []
+    for node in graph.nodes():
+        x, y = pos[node]
+        node_x.append(x)
+        node_y.append(y)
+
+    node_trace = go.Scatter(
+        x=node_x, y=node_y,
+        mode='markers+text',
+        text=list(graph.nodes()),
+        textposition='bottom center',
+        marker=dict(size=15, color='lightblue', line=dict(width=2, color='DarkSlateGrey')),
+        hoverinfo='text'
+    )
+
+    fig = go.Figure(
+        data=[edge_trace, node_trace],
+        layout=go.Layout(
+            showlegend=False,
+            hovermode='closest',
+            margin=dict(b=20, l=5, r=5, t=40),
+            annotations=[dict(
+                text="Python Causal Graph",
+                showarrow=False,
+                xref="paper", yref="paper",
+                x=0.005, y= -0.002,
+                font=dict(size=14, color="lightgray")
+            )],
+            xaxis=dict(showgrid=False, zeroline=False, showticklabels=False),
+            yaxis=dict(showgrid=False, zeroline=False, showticklabels=False),
+            title=dict(text="Causal Graph Visualization", font=dict(size=16)) # Corrected line
+        )
+    )
+    return fig.to_json()

utils/preprocessor.py

@@ -0,0 +1,57 @@
+# utils/preprocessor.py
+from sklearn.preprocessing import StandardScaler, LabelEncoder
+import pandas as pd
+import numpy as np
+import logging
+
+# Set up logging
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
+
+class DataPreprocessor:
+    def __init__(self):
+        self.scaler = StandardScaler()
+        self.label_encoders = {}
+
+    def preprocess(self, df):
+        """
+        Preprocess DataFrame: handle missing values, encode categorical variables, scale numerical variables.
+        """
+        try:
+            logger.info(f"Input DataFrame shape: {df.shape}, columns: {list(df.columns)}")
+            df_processed = df.copy()
+            
+            # Handle missing values
+            logger.info("Handling missing values...")
+            for col in df_processed.columns:
+                if df_processed[col].isnull().any():
+                    if pd.api.types.is_numeric_dtype(df_processed[col]):
+                        df_processed[col] = df_processed[col].fillna(df_processed[col].mean())
+                        logger.info(f"Filled numeric missing values in '{col}' with mean.")
+                    else:
+                        df_processed[col] = df_processed[col].fillna(df_processed[col].mode()[0])
+                        logger.info(f"Filled categorical missing values in '{col}' with mode.")
+            
+            # Encode categorical variables
+            logger.info("Encoding categorical variables...")
+            for col in df_processed.select_dtypes(include=['object', 'category']).columns:
+                logger.info(f"Encoding column: {col}")
+                self.label_encoders[col] = LabelEncoder()
+                df_processed[col] = self.label_encoders[col].fit_transform(df_processed[col])
+            
+            # Scale numerical variables
+            logger.info("Scaling numerical variables...")
+            numeric_cols = df_processed.select_dtypes(include=[np.number]).columns
+            if len(numeric_cols) > 0:
+                # Exclude columns that are now effectively categorical (post-label encoding)
+                # This is a heuristic; ideally, identify original numeric columns.
+                cols_to_scale = [col for col in numeric_cols if col not in self.label_encoders]
+                if cols_to_scale:
+                    df_processed[cols_to_scale] = self.scaler.fit_transform(df_processed[cols_to_scale])
+                    logger.info(f"Scaled numeric columns: {cols_to_scale}")
+            
+            logger.info(f"Preprocessed DataFrame shape: {df_processed.shape}")
+            return df_processed
+        except Exception as e:
+            logger.exception(f"Error preprocessing data: {str(e)}")
+            raise

utils/treatment_effects.py

@@ -0,0 +1,63 @@
+# utils/treatment_effects.py
+from sklearn.linear_model import LinearRegression, LogisticRegression
+import pandas as pd
+import numpy as np
+# For matching-based methods, you might need libraries like dowhy or causalml
+# import statsmodels.api as sm # Example for regression diagnostics
+
+class TreatmentEffectAlgorithms:
+    def linear_regression_ate(self, df, treatment_col, outcome_col, covariates):
+        """
+        Estimate ATE using linear regression.
+        """
+        X = df[covariates + [treatment_col]]
+        y = df[outcome_col]
+        model = LinearRegression()
+        model.fit(X, y)
+        ate = model.coef_[-1] # Coefficient of treatment_col
+        return float(ate)
+
+    def propensity_score_matching(self, df, treatment_col, outcome_col, covariates):
+        """
+        Placeholder for Propensity Score Matching.
+        You would implement or integrate a matching algorithm here.
+        """
+        print("Propensity Score Matching is a placeholder. Returning a dummy ATE.")
+        # Simplified: Estimate propensity scores
+        X_propensity = df[covariates]
+        T_propensity = df[treatment_col]
+        prop_model = LogisticRegression(solver='liblinear')
+        prop_model.fit(X_propensity, T_propensity)
+        propensity_scores = prop_model.predict_proba(X_propensity)[:, 1]
+        
+        # Dummy ATE calculation for demonstration
+        treated_outcome = df[df[treatment_col] == 1][outcome_col].mean()
+        control_outcome = df[df[treatment_col] == 0][outcome_col].mean()
+        return float(treated_outcome - control_outcome) # Simplified dummy ATE
+
+    def inverse_propensity_weighting(self, df, treatment_col, outcome_col, covariates):
+        """
+        Placeholder for Inverse Propensity Weighting (IPW).
+        You would implement or integrate IPW here.
+        """
+        print("Inverse Propensity Weighting is a placeholder. Returning a dummy ATE.")
+        # Dummy ATE for demonstration
+        return np.random.rand() * 10 # Random dummy value
+
+    def t_learner(self, df, treatment_col, outcome_col, covariates):
+        """
+        Placeholder for T-learner.
+        You would implement a T-learner using two separate models.
+        """
+        print("T-learner is a placeholder. Returning a dummy ATE.")
+        # Dummy ATE for demonstration
+        return np.random.rand() * 10 + 5 # Random dummy value
+
+    def s_learner(self, df, treatment_col, outcome_col, covariates):
+        """
+        Placeholder for S-learner.
+        You would implement an S-learner using a single model.
+        """
+        print("S-learner is a placeholder. Returning a dummy ATE.")
+        # Dummy ATE for demonstration
+        return np.random.rand() * 10 - 2 # Random dummy value