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rahul2001 commited on Sep 14, 2023

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ba19e16

1 Parent(s): 0aa9c34

Model training

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Files changed (8) hide show

artifacts/model.pkl +0 -0
catboost_info/catboost_training.json +0 -0
catboost_info/learn/events.out.tfevents +0 -0
catboost_info/learn_error.tsv +1000 -1000
catboost_info/time_left.tsv +1000 -1000
src/Components/Data_ingestation.py +5 -2
src/Components/model_tranier.py +73 -0
src/utils.py +25 -0

artifacts/model.pkl ADDED Viewed

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catboost_info/catboost_training.json CHANGED Viewed

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catboost_info/learn/events.out.tfevents CHANGED Viewed

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catboost_info/learn_error.tsv CHANGED Viewed

@@ -1,1001 +1,1001 @@
 iter	RMSE
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-721	3.486869081
-722	3.486303638
-723	3.486035862
-724	3.482668951
-725	3.47820549
-726	3.474392283
-727	3.472268163
-728	3.471209164
-729	3.470049971
-730	3.469078654
-731	3.468643292
-732	3.468134848
-733	3.467820182
-734	3.465776156
-735	3.463037608
-736	3.45965637
-737	3.457275091
-738	3.455522955
-739	3.451270508
-740	3.450978947
-741	3.450608492
-742	3.448122004
-743	3.445646714
-744	3.44411263
-745	3.443264164
-746	3.439521363
-747	3.436809225
-748	3.435509465
-749	3.434450277
-750	3.431786753
-751	3.430150302
-752	3.42696281
-753	3.426870334
-754	3.425200309
-755	3.423383783
-756	3.422752979
-757	3.420845924
-758	3.418917862
-759	3.417095735
-760	3.414391848
-761	3.41426711
-762	3.411976013
-763	3.408614844
-764	3.407906721
-765	3.407494015
-766	3.406287128
-767	3.404600442
-768	3.404012662
-769	3.400398762
-770	3.399737714
-771	3.399185251
-772	3.397245411
-773	3.396229831
-774	3.394002836
-775	3.389617243
-776	3.385426488
-777	3.385072258
-778	3.383842366
-779	3.380110746
-780	3.378595218
-781	3.377371219
-782	3.375814782
-783	3.374195887
-784	3.373704309
-785	3.372971757
-786	3.371565136
-787	3.3700309
-788	3.368186663
-789	3.366710398
-790	3.364598042
-791	3.36348442
-792	3.362001759
-793	3.361580401
-794	3.359543675
-795	3.35766119
-796	3.354592969
-797	3.350985986
-798	3.349589624
-799	3.349110856
-800	3.345315441
-801	3.34397474
-802	3.343800902
-803	3.342329465
-804	3.34220347
-805	3.341279219
-806	3.337329521
-807	3.336708453
-808	3.333252703
-809	3.332186769
-810	3.330594851
-811	3.327867442
-812	3.327563111
-813	3.326308994
-814	3.326249879
-815	3.322336238
-816	3.320491154
-817	3.320434271
-818	3.319609638
-819	3.316182682
-820	3.313126612
-821	3.311222803
-822	3.309627335
-823	3.307974895
-824	3.307321505
-825	3.306759087
-826	3.30484037
-827	3.30201994
-828	3.299302297
-829	3.298132671
-830	3.296976183
-831	3.293144745
-832	3.290284404
-833	3.288285745
-834	3.286662667
-835	3.285692682
-836	3.284565611
-837	3.283897383
-838	3.280712855
-839	3.280468341
-840	3.277814895
-841	3.276316385
-842	3.27557063
-843	3.27499628
-844	3.273569958
-845	3.269665595
-846	3.267984685
-847	3.267702508
-848	3.266671924
-849	3.265359825
-850	3.2618983
-851	3.260456253
-852	3.258389147
-853	3.258108469
-854	3.256424936
-855	3.254817983
-856	3.253898449
-857	3.253023652
-858	3.252845542
-859	3.252316026
-860	3.24975301
-861	3.249151075
-862	3.246257135
-863	3.243826471
-864	3.243190653
-865	3.24008797
-866	3.240036811
-867	3.238676314
-868	3.237848782
-869	3.236405498
-870	3.233378068
-871	3.233169169
-872	3.232434885
-873	3.229978115
-874	3.229648848
-875	3.227208761
-876	3.226258869
-877	3.225331995
-878	3.221968268
-879	3.221166317
-880	3.220047503
-881	3.21982781
-882	3.216693405
-883	3.214798761
-884	3.214395085
-885	3.213470696
-886	3.211595017
-887	3.210965255
-888	3.209413583
-889	3.208331683
-890	3.207110711
-891	3.204519413
-892	3.201421296
-893	3.200691259
-894	3.200485067
-895	3.197467568
-896	3.195838544
-897	3.195671746
-898	3.194213094
-899	3.192884691
-900	3.188364239
-901	3.185219478
-902	3.184120387
-903	3.183725321
-904	3.181264554
-905	3.180741589
-906	3.179782639
-907	3.176377564
-908	3.174078182
-909	3.171242379
-910	3.170421012
-911	3.170087224
-912	3.167303059
-913	3.16705966
-914	3.165667683
-915	3.164689733
-916	3.163311455
-917	3.159425016
-918	3.159327058
-919	3.15819426
-920	3.157086603
-921	3.153354154
-922	3.153261317
-923	3.153168454
-924	3.152080399
-925	3.151635144
-926	3.147762655
-927	3.147172317
-928	3.146254125
-929	3.144869828
-930	3.143466218
-931	3.141919643
-932	3.141054135
-933	3.139390417
-934	3.139025283
-935	3.137824183
-936	3.135620453
-937	3.13443178
-938	3.132765713
-939	3.131434124
-940	3.128384262
-941	3.128120455
-942	3.126314615
-943	3.125409666
-944	3.123072589
-945	3.122921099
-946	3.122182662
-947	3.120040951
-948	3.118767799
-949	3.118203218
-950	3.116343362
-951	3.11480829
-952	3.112607903
-953	3.111661241
-954	3.110249676
-955	3.107333579
-956	3.107149851
-957	3.106867336
-958	3.10364681
-959	3.102703811
-960	3.101469264
-961	3.09863569
-962	3.09645939
-963	3.096240186
-964	3.094838828
-965	3.094176802
-966	3.093182279
-967	3.090150687
-968	3.088612829
-969	3.087153684
-970	3.085866843
-971	3.082669094
-972	3.081047548
-973	3.079684322
-974	3.07613431
-975	3.074005935
-976	3.072988853
-977	3.072344268
-978	3.070654438
-979	3.069724506
-980	3.067763166
-981	3.066737893
-982	3.065248174
-983	3.063186837
-984	3.062880371
-985	3.061288645
-986	3.059970575
-987	3.059576341
-988	3.058175919
-989	3.056830702
-990	3.056336579
-991	3.055810414
-992	3.055476518
-993	3.053331734
-994	3.050890374
-995	3.04972623
-996	3.047217327
-997	3.047155483
-998	3.04430671
-999	3.042664195

 iter	RMSE
+0	14.60668376
+1	14.22257463
+2	13.86007014
+3	13.48925673
+4	13.14139172
+5	12.83049051
+6	12.54910476
+7	12.30208677
+8	12.05365227
+9	11.76062999
+10	11.47503327
+11	11.22781413
+12	10.99600665
+13	10.75897715
+14	10.52407085
+15	10.28397662
+16	10.11579733
+17	9.91933306
+18	9.735798379
+19	9.549818053
+20	9.363353725
+21	9.182309467
+22	9.015291624
+23	8.856914943
+24	8.712403835
+25	8.574287791
+26	8.444249085
+27	8.317384933
+28	8.178590362
+29	8.073002609
+30	7.960563766
+31	7.849441712
+32	7.753838111
+33	7.65937175
+34	7.563791126
+35	7.465628415
+36	7.372242355
+37	7.279241408
+38	7.197765978
+39	7.111690502
+40	7.038043693
+41	6.980533113
+42	6.930878627
+43	6.861359427
+44	6.791709927
+45	6.722598419
+46	6.660883366
+47	6.590187959
+48	6.537772531
+49	6.482294921
+50	6.430416922
+51	6.385699129
+52	6.339986475
+53	6.294925183
+54	6.251214092
+55	6.214835702
+56	6.174090167
+57	6.135237075
+58	6.104992674
+59	6.069774311
+60	6.037442582
+61	6.011559165
+62	5.98236416
+63	5.961645238
+64	5.93462792
+65	5.908467573
+66	5.880785593
+67	5.854718337
+68	5.833208938
+69	5.809116724
+70	5.787466663
+71	5.764701325
+72	5.746845754
+73	5.726603353
+74	5.705827871
+75	5.685698161
+76	5.663933629
+77	5.645013263
+78	5.627844238
+79	5.612897399
+80	5.59678302
+81	5.580711097
+82	5.562808538
+83	5.54403869
+84	5.532915839
+85	5.520612705
+86	5.508675092
+87	5.495625878
+88	5.47850611
+89	5.464947477
+90	5.452391163
+91	5.442414252
+92	5.431117007
+93	5.423640202
+94	5.412886091
+95	5.405320379
+96	5.394580672
+97	5.383231997
+98	5.37177115
+99	5.358369826
+100	5.345397971
+101	5.334246249
+102	5.325113118
+103	5.312209731
+104	5.303579188
+105	5.295855292
+106	5.285745462
+107	5.275471332
+108	5.26878527
+109	5.259510609
+110	5.256859511
+111	5.248370697
+112	5.240869717
+113	5.233490314
+114	5.229393082
+115	5.227776592
+116	5.222816869
+117	5.212607193
+118	5.207183606
+119	5.201880074
+120	5.198722638
+121	5.19634166
+122	5.191236698
+123	5.182306897
+124	5.1776392
+125	5.171884304
+126	5.168034616
+127	5.163630008
+128	5.1605863
+129	5.155852928
+130	5.147862642
+131	5.141822608
+132	5.133729584
+133	5.12953466
+134	5.124071702
+135	5.121763255
+136	5.115227606
+137	5.110619641
+138	5.105826828
+139	5.101372092
+140	5.098689588
+141	5.095510592
+142	5.089056379
+143	5.080095448
+144	5.076682447
+145	5.073643
+146	5.068159407
+147	5.065231925
+148	5.061829803
+149	5.056818786
+150	5.053301142
+151	5.047496812
+152	5.042691251
+153	5.038156648
+154	5.035959636
+155	5.03106259
+156	5.022693054
+157	5.017937636
+158	5.015374112
+159	5.010812749
+160	5.005910011
+161	4.998796494
+162	4.994873605
+163	4.992358759
+164	4.988636915
+165	4.982985111
+166	4.980372049
+167	4.978007657
+168	4.97364116
+169	4.97001727
+170	4.964689283
+171	4.962205787
+172	4.958647148
+173	4.951792855
+174	4.946440773
+175	4.943645617
+176	4.936813978
+177	4.934049177
+178	4.929611317
+179	4.925111917
+180	4.92072123
+181	4.915982362
+182	4.910038783
+183	4.904876155
+184	4.901688829
+185	4.899636689
+186	4.894185021
+187	4.889992899
+188	4.885304372
+189	4.881837666
+190	4.879965664
+191	4.873233821
+192	4.870233156
+193	4.867083098
+194	4.863586698
+195	4.859222908
+196	4.855867957
+197	4.852631742
+198	4.849742015
+199	4.84647841
+200	4.843895383
+201	4.840054637
+202	4.838135564
+203	4.834995585
+204	4.831970066
+205	4.828406258
+206	4.825689172
+207	4.823281104
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+210	4.810881626
+211	4.805755734
+212	4.802803131
+213	4.799614236
+214	4.797702058
+215	4.796438123
+216	4.792670749
+217	4.791052772
+218	4.787559364
+219	4.784288171
+220	4.780515913
+221	4.776006872
+222	4.772224552
+223	4.768341669
+224	4.766859282
+225	4.76153681
+226	4.75378697
+227	4.749432696
+228	4.744716327
+229	4.741481093
+230	4.737236301
+231	4.732100637
+232	4.729576719
+233	4.727488318
+234	4.723514205
+235	4.720230407
+236	4.717091007
+237	4.714368087
+238	4.709590961
+239	4.70485378
+240	4.698174637
+241	4.695370953
+242	4.688882396
+243	4.685236812
+244	4.68191208
+245	4.679031934
+246	4.676890275
+247	4.674668467
+248	4.673009747
+249	4.667130367
+250	4.66472352
+251	4.661317009
+252	4.655537318
+253	4.653714732
+254	4.647921093
+255	4.645146133
+256	4.642140406
+257	4.639510149
+258	4.634359194
+259	4.632717324
+260	4.630582367
+261	4.627893222
+262	4.626362777
+263	4.623957557
+264	4.620250521
+265	4.614834116
+266	4.611290681
+267	4.606983096
+268	4.606053017
+269	4.599354622
+270	4.595838633
+271	4.593854125
+272	4.590469777
+273	4.585712896
+274	4.579478565
+275	4.577578503
+276	4.571076916
+277	4.567674521
+278	4.565812122
+279	4.561941151
+280	4.556788062
+281	4.552879294
+282	4.55205044
+283	4.550237573
+284	4.546069989
+285	4.544269197
+286	4.542675258
+287	4.538143571
+288	4.533674522
+289	4.530165467
+290	4.525695533
+291	4.521336599
+292	4.516061109
+293	4.514189672
+294	4.510206827
+295	4.505863178
+296	4.501121951
+297	4.494907772
+298	4.493204037
+299	4.488848604
+300	4.485441437
+301	4.48036418
+302	4.477277644
+303	4.473868021
+304	4.469766357
+305	4.465948295
+306	4.463944795
+307	4.462017176
+308	4.456581439
+309	4.455578207
+310	4.45471769
+311	4.450290225
+312	4.446692546
+313	4.441477019
+314	4.437763322
+315	4.435434454
+316	4.433594492
+317	4.428491418
+318	4.425420132
+319	4.423440189
+320	4.420663598
+321	4.418794434
+322	4.415436322
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+325	4.409485002
+326	4.407863782
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+328	4.398202067
+329	4.393533784
+330	4.390105052
+331	4.388838145
+332	4.385023164
+333	4.379684609
+334	4.375562042
+335	4.373750156
+336	4.369567164
+337	4.368235387
+338	4.364635401
+339	4.358704319
+340	4.353835877
+341	4.350396919
+342	4.348713878
+343	4.347152107
+344	4.342562983
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+346	4.338372738
+347	4.334195928
+348	4.333173018
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+352	4.328530889
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+355	4.321554995
+356	4.316332822
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+359	4.308315982
+360	4.305100085
+361	4.301820781
+362	4.297459199
+363	4.294459521
+364	4.292891424
+365	4.291682388
+366	4.289607142
+367	4.2844034
+368	4.279796532
+369	4.276571704
+370	4.275391327
+371	4.274661707
+372	4.269708809
+373	4.266733539
+374	4.262526778
+375	4.258310657
+376	4.257477665
+377	4.252704008
+378	4.248748677
+379	4.244094278
+380	4.240653404
+381	4.237311518
+382	4.233393404
+383	4.22876645
+384	4.227377182
+385	4.225423556
+386	4.222666613
+387	4.218805699
+388	4.217863448
+389	4.216178341
+390	4.213529032
+391	4.211841601
+392	4.210271888
+393	4.209199928
+394	4.207442252
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+396	4.200408322
+397	4.196242344
+398	4.193251998
+399	4.192653487
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+403	4.181068657
+404	4.177620506
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+409	4.170997258
+410	4.169763855
+411	4.168570209
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+414	4.15874983
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catboost_info/time_left.tsv CHANGED Viewed

@@ -1,1001 +1,1001 @@
 iter	Passed	Remaining
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src/Components/Data_ingestation.py CHANGED Viewed

@@ -7,6 +7,7 @@ import pandas as pd
 from sklearn.model_selection import train_test_split
 from dataclasses import dataclass
 from data_transformation import Data_transformation
 @dataclass
 class Data_ingestion_config:
     train_data_path: str =  os.path.join("artifact","train.csv")
@@ -48,7 +49,9 @@ if __name__ == "__main__":
     train_data,test_data = obj.intiate_data_ingestion()
     data_trans =  Data_transformation()
-    data_trans.initiate_data_transformation(train_data,test_data)

 from sklearn.model_selection import train_test_split
 from dataclasses import dataclass
 from data_transformation import Data_transformation
+from model_tranier import Model_training_config,Model_trainer
 @dataclass
 class Data_ingestion_config:
     train_data_path: str =  os.path.join("artifact","train.csv")
     train_data,test_data = obj.intiate_data_ingestion()
     data_trans =  Data_transformation()
+    train_arr,test_arr,_ =  data_trans.initiate_data_transformation(train_data,test_data)
+    modeltrainer =  Model_trainer()
+    print(modeltrainer.intiate_model_trainer(train_arr,test_arr))

src/Components/model_tranier.py ADDED Viewed

	@@ -0,0 +1,73 @@

+# Basic Import
+import numpy as np
+import pandas as pd
+import matplotlib.pyplot as plt
+import seaborn as sns
+import os
+# Modelling
+from sklearn.metrics import mean_squared_error, r2_score
+from sklearn.neighbors import KNeighborsRegressor
+from sklearn.tree import DecisionTreeRegressor
+from sklearn.ensemble import RandomForestRegressor,AdaBoostRegressor
+from sklearn.svm import SVR
+from sklearn.linear_model import LinearRegression, Ridge,Lasso
+from sklearn.metrics import r2_score, mean_absolute_error, mean_squared_error
+from sklearn.model_selection import RandomizedSearchCV
+from catboost import CatBoostRegressor
+from xgboost import XGBRegressor
+import warnings
+import sys
+from dataclasses import dataclass
+from src.utils import save_object,evaluate_model
+from src.logger import logging
+from src.exception import CustomException
+@dataclass
+class Model_training_config:
+    trained_model_path = os.path.join("artifacts","model.pkl")
+class Model_trainer:
+    def __init__(self) -> None:
+        self.model_trainer_config = Model_training_config()
+    def intiate_model_trainer(self,train_array,test_array):
+        try:
+            logging.info("Split training and testing data ")
+            x_train,y_train,x_test,y_test = (
+                train_array[:,:-1],
+                train_array[:,-1],
+                test_array[:,:-1],
+                test_array[:,-1]
+            )
+            models = {
+                "Random Forest":RandomForestRegressor(),
+                "Decision tree" : DecisionTreeRegressor(),
+                "Linear Regression" : LinearRegression(),
+                "K-neighbors Classifier": KNeighborsRegressor(),
+                "XGBClassifier": XGBRegressor(),
+                "Catboosting Classifier":CatBoostRegressor(),
+                "AdaBoost Classifer":AdaBoostRegressor()
+            }
+            model_report:dict = evaluate_model(X=x_train,Y = y_train,X_test = x_test,Y_test=y_test,Models = models)
+            best_model_score = max(sorted(model_report.values()))
+            best_model_nm = list(model_report.keys())[
+                list(model_report.values()).index(best_model_score)
+            ]
+            best_model =  models[best_model_nm]
+            if best_model_score < 0.6:
+                raise CustomException("No best model found")
+            logging.info("Best model Found")
+            save_object(file_path= Model_training_config.trained_model_path,
+                        obj = best_model )
+            predicted = best_model.predict(x_test)
+            r2score = r2_score(y_test,predicted)
+            return r2score
+        except Exception as e:
+            raise CustomException(e,sys)

src/utils.py CHANGED Viewed

@@ -10,6 +10,11 @@ from sklearn.model_selection import GridSearchCV
 from src.exception import CustomException
 def save_object(file_path , obj):
         try:
             dir_path = os.path.dirname(file_path)
@@ -20,4 +25,24 @@ def save_object(file_path , obj):
                   pickle.dump(obj,file_obj)
         except Exception as e:
               raise CustomException(e,sys)

 from src.exception import CustomException
+def eval_model(true, predicted):
+    r2_square = r2_score(true, predicted)
+    return r2_square
 def save_object(file_path , obj):
         try:
             dir_path = os.path.dirname(file_path)
                   pickle.dump(obj,file_obj)
         except Exception as e:
               raise CustomException(e,sys)
+def evaluate_model(X,Y,X_test,Y_test,Models):
+    try:
+        report = {}
+        for i in range(len(list(Models))):
+            model = list(Models.values())[i]
+            model.fit(X, Y) # Train model
+            # Make predictions
+            y_train_pred = model.predict(X)
+            y_test_pred = model.predict(X_test)
+            # Evaluate Train and Test dataset
+            model_test_r2 = eval_model(Y_test, y_test_pred)
+            report[(list(Models.keys())[i])] = model_test_r2
+        return report
+    except Exception as e:
+        raise CustomException(e,sys)