👉 What is PyCaret?¶

PyCaret is an open-source, low-code machine learning library in Python that automates machine learning workflows. It is an end-to-end machine learning and model management tool that speeds up the experiment cycle exponentially and makes you more productive.

In comparison with the other open-source machine learning libraries, PyCaret is an alternate low-code library that can be used to replace hundreds of lines of code with few words only. This makes experiments exponentially fast and efficient. PyCaret is essentially a Python wrapper around several machine learning libraries and frameworks such as scikit-learn, XGBoost, LightGBM, CatBoost, spaCy, Optuna, Hyperopt, Ray, and many more.

The design and simplicity of PyCaret is inspired by the emerging role of citizen data scientists, a term first used by Gartner. Citizen Data Scientists are power users who can perform both simple and moderately sophisticated analytical tasks that would previously have required more expertise. Seasoned data scientists are often difficult to find and expensive to hire but citizen data scientists can be an effective way to mitigate this gap and address data-related challenges in the business setting.

Official Website: https://www.pycaret.org Documentation: https://pycaret.readthedocs.io/en/latest/

👉 Install PyCaret¶

Installing PyCaret is very easy and takes only a few minutes. We strongly recommend using a virtual environment to avoid potential conflicts with other libraries. PyCaret's default installation is a slim version of pycaret that only installs hard dependencies that are listed in requirements.txt. To install the default version:

pip install pycaret

When you install the full version of pycaret, all the optional dependencies as listed here are also installed.To install version:

pip install pycaret[full]

👉Dataset¶

In [1]:

from pycaret.datasets import get_data
data = get_data('insurance')

	age	sex	bmi	children	smoker	region	charges
0	19	female	27.900	0	yes	southwest	16884.92400
1	18	male	33.770	1	no	southeast	1725.55230
2	28	male	33.000	3	no	southeast	4449.46200
3	33	male	22.705	0	no	northwest	21984.47061
4	32	male	28.880	0	no	northwest	3866.85520

👉 Data Preparation¶

In [2]:

from pycaret.regression import *
s = setup(data, target = 'charges', session_id = 123)

	Description	Value
0	session_id	123
1	Target	charges
2	Original Data	(1338, 7)
3	Missing Values	False
4	Numeric Features	2
5	Categorical Features	4
6	Ordinal Features	False
7	High Cardinality Features	False
8	High Cardinality Method	None
9	Transformed Train Set	(936, 14)
10	Transformed Test Set	(402, 14)
11	Shuffle Train-Test	True
12	Stratify Train-Test	False
13	Fold Generator	KFold
14	Fold Number	10
15	CPU Jobs	-1
16	Use GPU	False
17	Log Experiment	False
18	Experiment Name	reg-default-name
19	USI	13da
20	Imputation Type	simple
21	Iterative Imputation Iteration	None
22	Numeric Imputer	mean
23	Iterative Imputation Numeric Model	None
24	Categorical Imputer	constant
25	Iterative Imputation Categorical Model	None
26	Unknown Categoricals Handling	least_frequent
27	Normalize	False
28	Normalize Method	None
29	Transformation	False
30	Transformation Method	None
31	PCA	False
32	PCA Method	None
33	PCA Components	None
34	Ignore Low Variance	False
35	Combine Rare Levels	False
36	Rare Level Threshold	None
37	Numeric Binning	False
38	Remove Outliers	False
39	Outliers Threshold	None
40	Remove Multicollinearity	False
41	Multicollinearity Threshold	None
42	Clustering	False
43	Clustering Iteration	None
44	Polynomial Features	False
45	Polynomial Degree	None
46	Trignometry Features	False
47	Polynomial Threshold	None
48	Group Features	False
49	Feature Selection	False
50	Feature Selection Method	classic
51	Features Selection Threshold	None
52	Feature Interaction	False
53	Feature Ratio	False
54	Interaction Threshold	None
55	Transform Target	False
56	Transform Target Method	box-cox

In [3]:

# check transformed X_train
get_config('X_train')

Out[3]:

	age	bmi	sex_female	children_0	children_1	children_2	children_3	children_4	children_5	smoker_yes	region_northeast	region_northwest	region_southeast	region_southwest
300	36.0	27.549999	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0
904	60.0	35.099998	1.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0
670	30.0	31.570000	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0
617	49.0	25.600000	0.0	0.0	0.0	1.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	1.0
373	26.0	32.900002	0.0	0.0	0.0	1.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	1.0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
1238	37.0	22.705000	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0
1147	20.0	31.920000	1.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0
106	19.0	28.400000	1.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0
1041	18.0	23.084999	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0
1122	53.0	36.860001	1.0	0.0	0.0	0.0	1.0	0.0	0.0	1.0	0.0	1.0	0.0	0.0

936 rows × 14 columns

In [4]:

# list columns of transformed X_train 
get_config('X_train').columns

Out[4]:

Index(['age', 'bmi', 'sex_female', 'children_0', 'children_1', 'children_2',
       'children_3', 'children_4', 'children_5', 'smoker_yes',
       'region_northeast', 'region_northwest', 'region_southeast',
       'region_southwest'],
      dtype='object')

👉Model Training & Selection¶

Compare Models¶

In [5]:

# train all models using default hyperparameters
best = compare_models()

	Model	MAE	MSE	RMSE	R2	RMSLE	MAPE	TT (Sec)
gbr	Gradient Boosting Regressor	2702.7680	23242056.4409	4801.5704	0.8348	0.4397	0.3113	0.0480
catboost	CatBoost Regressor	2844.4446	24943135.5224	4977.1926	0.8228	0.4707	0.3364	2.0210
rf	Random Forest Regressor	2736.7455	24862762.2305	4970.6959	0.8213	0.4674	0.3294	0.2620
lightgbm	Light Gradient Boosting Machine	2959.5584	25236477.0456	5013.0892	0.8171	0.5427	0.3685	0.1770
ada	AdaBoost Regressor	4162.2323	28328260.0955	5316.6146	0.7985	0.6349	0.7263	0.0180
et	Extra Trees Regressor	2814.2964	28815493.0260	5339.0879	0.7964	0.4889	0.3350	0.2560
xgboost	Extreme Gradient Boosting	3302.3215	31739266.6000	5615.5941	0.7701	0.5661	0.4218	0.7540
llar	Lasso Least Angle Regression	4315.7895	38355976.5126	6173.8740	0.7311	0.6105	0.4415	0.0150
ridge	Ridge Regression	4336.2309	38381496.8000	6175.9541	0.7309	0.6193	0.4454	0.0160
br	Bayesian Ridge	4333.6881	38381669.3629	6175.9476	0.7308	0.6151	0.4450	0.0140
lr	Linear Regression	4323.6136	38380061.2000	6175.7164	0.7308	0.6175	0.4432	1.2800
lasso	Lasso Regression	4323.0688	38375137.8000	6175.3801	0.7308	0.6140	0.4431	0.0150
lar	Least Angle Regression	4447.7872	39653123.4167	6265.9028	0.7234	0.6468	0.4687	0.0170
dt	Decision Tree Regressor	3148.3402	43766011.6491	6584.7198	0.6855	0.5331	0.3455	0.0170
huber	Huber Regressor	3455.4030	48903724.4845	6970.8198	0.6545	0.4790	0.2174	0.0350
omp	Orthogonal Matching Pursuit	5754.7768	57503216.4290	7566.7093	0.5997	0.7418	0.8990	0.0140
par	Passive Aggressive Regressor	4164.7843	61324373.4835	7747.8332	0.5840	0.4724	0.2586	0.0190
en	Elastic Net	7369.0573	90443346.8000	9468.6782	0.3791	0.7377	0.9256	0.0150
knn	K Neighbors Regressor	7805.8425	126951808.0000	11221.6535	0.1218	0.8398	0.9147	0.0330

In [6]:

print(best)

GradientBoostingRegressor(alpha=0.9, ccp_alpha=0.0, criterion='friedman_mse',
                          init=None, learning_rate=0.1, loss='ls', max_depth=3,
                          max_features=None, max_leaf_nodes=None,
                          min_impurity_decrease=0.0, min_impurity_split=None,
                          min_samples_leaf=1, min_samples_split=2,
                          min_weight_fraction_leaf=0.0, n_estimators=100,
                          n_iter_no_change=None, presort='deprecated',
                          random_state=123, subsample=1.0, tol=0.0001,
                          validation_fraction=0.1, verbose=0, warm_start=False)

In [7]:

type(best)

Out[7]:

sklearn.ensemble._gb.GradientBoostingRegressor

Create Model¶

In [8]:

# train individual model
dt = create_model('dt')

	MAE	MSE	RMSE	R2	RMSLE	MAPE
0	3001.2294	37001480.2590	6082.8842	0.7790	0.4984	0.3140
1	3389.8885	49305179.5732	7021.7647	0.7133	0.5574	0.3361
2	2926.0191	42025684.6666	6482.7220	0.4679	0.6215	0.4025
3	2744.7144	34078761.4507	5837.7017	0.7154	0.5412	0.3740
4	3924.4816	59489464.3207	7712.9414	0.5575	0.6455	0.4796
5	3322.5435	42747575.4453	6538.1630	0.7250	0.4869	0.2928
6	3158.7047	49369669.1652	7026.3553	0.6641	0.4511	0.3089
7	2405.2970	31318616.6440	5596.3038	0.8278	0.4497	0.1434
8	3021.5461	39091793.3775	6252.3430	0.7475	0.5117	0.4381
9	3588.9772	53231891.5889	7296.0189	0.6571	0.5679	0.3653
Mean	3148.3402	43766011.6491	6584.7198	0.6855	0.5331	0.3455
SD	410.7953	8481549.4829	638.3390	0.1005	0.0631	0.0878

In [9]:

print(dt)

DecisionTreeRegressor(ccp_alpha=0.0, criterion='mse', max_depth=None,
                      max_features=None, max_leaf_nodes=None,
                      min_impurity_decrease=0.0, min_impurity_split=None,
                      min_samples_leaf=1, min_samples_split=2,
                      min_weight_fraction_leaf=0.0, presort='deprecated',
                      random_state=123, splitter='best')

Tune Hyperparameters¶

In [12]:

# tune hyperparameters of model
tuned_dt = tune_model(dt, search_library = 'optuna')

	MAE	MSE	RMSE	R2	RMSLE	MAPE
0	2701.1273	21028661.9672	4585.7019	0.8744	0.4025	0.2937
1	3091.8449	30933374.9177	5561.7780	0.8201	0.4644	0.3233
2	2617.2545	22345453.1888	4727.0978	0.7171	0.4627	0.3155
3	2830.8796	22588574.3822	4752.7439	0.8114	0.4632	0.3918
4	2960.2007	25929500.5455	5092.1018	0.8071	0.4537	0.3175
5	2653.9498	18985846.9396	4357.2752	0.8779	0.3584	0.2672
6	2556.3558	18410040.3286	4290.6923	0.8747	0.3800	0.3059
7	2614.9736	22942326.3754	4789.8149	0.8738	0.4678	0.3049
8	2671.8309	19576326.3629	4424.5143	0.8736	0.3970	0.3326
9	2602.0013	23910249.5350	4889.8108	0.8460	0.4363	0.2869
Mean	2730.0418	22665035.4543	4747.1531	0.8376	0.4286	0.3139
SD	166.3070	3530111.2020	359.9627	0.0485	0.0385	0.0316

In [13]:

print(tuned_dt)

DecisionTreeRegressor(ccp_alpha=0.0, criterion='friedman_mse', max_depth=5,
                      max_features=0.9723036176467299, max_leaf_nodes=None,
                      min_impurity_decrease=7.642419608769017e-06,
                      min_impurity_split=None, min_samples_leaf=6,
                      min_samples_split=6, min_weight_fraction_leaf=0.0,
                      presort='deprecated', random_state=123, splitter='best')

Ensemble Model¶

In [14]:

bagged_tunned_dt = ensemble_model(tuned_dt)

	MAE	MSE	RMSE	R2	RMSLE	MAPE
0	2584.1702	20381846.7339	4514.6259	0.8783	0.4112	0.3067
1	2957.1900	30309405.6671	5505.3979	0.8238	0.4556	0.2961
2	2481.1893	19964450.5458	4468.1596	0.7472	0.4411	0.2926
3	2715.0783	20812332.5053	4562.0535	0.8262	0.4392	0.3649
4	2763.6315	25039494.3653	5003.9479	0.8138	0.4619	0.2862
5	2537.7935	18224009.0741	4268.9588	0.8828	0.3294	0.2451
6	2319.5499	16909113.0122	4112.0692	0.8849	0.3442	0.2728
7	2451.7525	22210287.0372	4712.7791	0.8779	0.4352	0.2625
8	2452.7959	19002500.6278	4359.1858	0.8773	0.4294	0.3517
9	2490.9265	23750364.4102	4873.4346	0.8470	0.4422	0.2904
Mean	2575.4078	21660380.3979	4638.0612	0.8459	0.4189	0.2969
SD	177.1968	3709070.0443	385.7052	0.0419	0.0432	0.0351

In [15]:

print(bagged_tunned_dt)

BaggingRegressor(base_estimator=DecisionTreeRegressor(ccp_alpha=0.0,
                                                      criterion='friedman_mse',
                                                      max_depth=5,
                                                      max_features=0.9723036176467299,
                                                      max_leaf_nodes=None,
                                                      min_impurity_decrease=7.642419608769017e-06,
                                                      min_impurity_split=None,
                                                      min_samples_leaf=6,
                                                      min_samples_split=6,
                                                      min_weight_fraction_leaf=0.0,
                                                      presort='deprecated',
                                                      random_state=123,
                                                      splitter='best'),
                 bootstrap=True, bootstrap_features=False, max_features=1.0,
                 max_samples=1.0, n_estimators=10, n_jobs=None, oob_score=False,
                 random_state=123, verbose=0, warm_start=False)

Voting Ensemble¶

In [16]:

dt = create_model('dt', verbose=False)
lasso = create_model('lasso', verbose=False)
knn = create_model('knn', verbose=False)
blender = blend_models([dt,lasso,knn])

	MAE	MSE	RMSE	R2	RMSLE	MAPE
0	4317.6330	40225874.7120	6342.3871	0.7597	0.5377	0.5095
1	4603.8770	47671358.8264	6904.4449	0.7228	0.5528	0.4544
2	3526.5657	30025112.7222	5479.5176	0.6198	0.6098	0.5355
3	4059.9577	33028436.9777	5747.0372	0.7242	0.6159	0.6499
4	4719.1236	47134827.5838	6865.4809	0.6494	0.5783	0.5456
5	4036.9877	39416780.6753	6278.2785	0.7464	0.4745	0.3684
6	4098.2195	43644758.3056	6606.4180	0.7030	0.5116	0.4656
7	3938.6892	37270337.0487	6104.9437	0.7951	0.4289	0.3185
8	4493.1798	43777521.8925	6616.4584	0.7172	0.5800	0.6167
9	4452.8107	44338906.9474	6658.7466	0.7144	0.5613	0.4606
Mean	4224.7044	40653391.5692	6360.3713	0.7152	0.5451	0.4925
SD	341.8405	5548059.5247	446.1712	0.0480	0.0561	0.0970

In [17]:

print(blender)

VotingRegressor(estimators=[('dt',
                             DecisionTreeRegressor(ccp_alpha=0.0,
                                                   criterion='mse',
                                                   max_depth=None,
                                                   max_features=None,
                                                   max_leaf_nodes=None,
                                                   min_impurity_decrease=0.0,
                                                   min_impurity_split=None,
                                                   min_samples_leaf=1,
                                                   min_samples_split=2,
                                                   min_weight_fraction_leaf=0.0,
                                                   presort='deprecated',
                                                   random_state=123,
                                                   splitter='best')),
                            ('lasso',
                             Lasso(alpha=1.0, copy_X=True, fit_intercept=True,
                                   max_iter=1000, normalize=False,
                                   positive=False, precompute=False,
                                   random_state=123, selection='cyclic',
                                   tol=0.0001, warm_start=False)),
                            ('knn',
                             KNeighborsRegressor(algorithm='auto', leaf_size=30,
                                                 metric='minkowski',
                                                 metric_params=None, n_jobs=-1,
                                                 n_neighbors=5, p=2,
                                                 weights='uniform'))],
                n_jobs=-1, verbose=False, weights=None)

In [18]:

type(blender)

Out[18]:

sklearn.ensemble._voting.VotingRegressor

In [ ]:

Stacking Ensemble¶

In [19]:

stacker = stack_models([dt,lasso,knn])

	MAE	MSE	RMSE	R2	RMSLE	MAPE
0	3330.1372	26118092.6166	5110.5863	0.8440	0.4528	0.3698
1	3823.1056	36671240.6179	6055.6784	0.7868	0.5307	0.3557
2	3330.5109	29504475.5264	5431.8022	0.6264	0.5701	0.3860
3	3029.4249	21804106.6611	4669.4868	0.8179	0.4804	0.4295
4	3931.7435	36775712.8059	6064.2982	0.7265	0.5132	0.3751
5	3461.5432	29482846.8211	5429.8109	0.8103	0.4937	0.3541
6	3479.7848	31955531.2624	5652.9224	0.7826	0.4684	0.3600
7	3610.5786	30708816.3945	5541.5536	0.8311	0.5030	0.2904
8	3761.7961	27523649.5059	5246.2986	0.8222	0.5596	0.4627
9	3913.4878	35744866.0161	5978.7010	0.7698	0.6368	0.4228
Mean	3567.2113	30628933.8228	5518.1139	0.7818	0.5209	0.3806
SD	278.9752	4611695.3808	423.5013	0.0612	0.0525	0.0458

In [20]:

print(stacker)

StackingRegressor(cv=KFold(n_splits=10, random_state=RandomState(MT19937) at 0x1B967A57468,
   shuffle=False),
                  estimators=[('dt',
                               DecisionTreeRegressor(ccp_alpha=0.0,
                                                     criterion='mse',
                                                     max_depth=None,
                                                     max_features=None,
                                                     max_leaf_nodes=None,
                                                     min_impurity_decrease=0.0,
                                                     min_impurity_split=None,
                                                     min_samples_leaf=1,
                                                     min_samples_split=2,
                                                     min_weight_fraction_leaf=0.0,
                                                     presor...
                                     positive=False, precompute=False,
                                     random_state=123, selection='cyclic',
                                     tol=0.0001, warm_start=False)),
                              ('knn',
                               KNeighborsRegressor(algorithm='auto',
                                                   leaf_size=30,
                                                   metric='minkowski',
                                                   metric_params=None,
                                                   n_jobs=-1, n_neighbors=5,
                                                   p=2, weights='uniform'))],
                  final_estimator=LinearRegression(copy_X=True,
                                                   fit_intercept=True,
                                                   n_jobs=-1, normalize=False),
                  n_jobs=-1, passthrough=True, verbose=0)

Analyze Model¶

In [21]:

evaluate_model(best)

interactive(children=(ToggleButtons(description='Plot Type:', icons=('',), options=(('Hyperparameters', 'param…

In [22]:

interpret_model(dt)

In [23]:

interpret_model(dt, plot = 'reason', observation=1)

Out[23]:

Visualization omitted, Javascript library not loaded!
Have you run `initjs()` in this notebook? If this notebook was from another user you must also trust this notebook (File -> Trust notebook). If you are viewing this notebook on github the Javascript has been stripped for security. If you are using JupyterLab this error is because a JupyterLab extension has not yet been written.

Model Predictions¶

In [24]:

# predict on holdout / test set
pred_holdout = predict_model(best);

	Model	MAE	MSE	RMSE	R2	RMSLE	MAPE
0	Gradient Boosting Regressor	2386.2018	17296249.1379	4158.8759	0.8789	0.3985	0.2922

In [25]:

pred_holdout.head()

Out[25]:

	age	bmi	sex_female	children_0	children_1	children_2	smoker_yes	region_northeast	region_northwest	region_southeast	region_southwest	charges	Label
0	49.0	42.680000	1.0	0.0	0.0	1.0	0.0	0.0	0.0	1.0	0.0	9800.888672	10621.483595
1	32.0	37.334999	0.0	0.0	1.0	0.0	0.0	1.0	0.0	0.0	0.0	4667.607422	7290.151941
2	27.0	31.400000	1.0	1.0	0.0	0.0	1.0	0.0	0.0	0.0	1.0	34838.871094	36012.959871
3	35.0	24.129999	0.0	0.0	1.0	0.0	0.0	0.0	1.0	0.0	0.0	5125.215820	7553.788882
4	60.0	25.740000	0.0	1.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	12142.578125	14904.032497

In [26]:

# predict on new data
data2 = data.copy()
data2.drop('charges', axis=1, inplace=True)
data2.head()

Out[26]:

	age	sex	bmi	children	smoker	region
0	19	female	27.900	0	yes	southwest
1	18	male	33.770	1	no	southeast
2	28	male	33.000	3	no	southeast
3	33	male	22.705	0	no	northwest
4	32	male	28.880	0	no	northwest

In [27]:

# finalize model
best_final = finalize_model(best)

In [28]:

# predict on data2
predictions = predict_model(best_final, data=data2)
predictions.head()

Out[28]:

	age	sex	bmi	children	smoker	region	Label
0	19	female	27.900	0	yes	southwest	18894.260073
1	18	male	33.770	1	no	southeast	3698.287534
2	28	male	33.000	3	no	southeast	6029.271578
3	33	male	22.705	0	no	northwest	8958.189116
4	32	male	28.880	0	no	northwest	3900.039002

👉 Save / Load / Deploy Model¶

In [29]:

save_model(best_final, 'insurance-pipeline')

Transformation Pipeline and Model Succesfully Saved

Out[29]:

(Pipeline(memory=None,
          steps=[('dtypes',
                  DataTypes_Auto_infer(categorical_features=[],
                                       display_types=True, features_todrop=[],
                                       id_columns=[], ml_usecase='regression',
                                       numerical_features=[], target='charges',
                                       time_features=[])),
                 ('imputer',
                  Simple_Imputer(categorical_strategy='not_available',
                                 fill_value_categorical=None,
                                 fill_value_numerical=None,
                                 numeric_strategy...
                                            learning_rate=0.1, loss='ls',
                                            max_depth=3, max_features=None,
                                            max_leaf_nodes=None,
                                            min_impurity_decrease=0.0,
                                            min_impurity_split=None,
                                            min_samples_leaf=1,
                                            min_samples_split=2,
                                            min_weight_fraction_leaf=0.0,
                                            n_estimators=100,
                                            n_iter_no_change=None,
                                            presort='deprecated',
                                            random_state=123, subsample=1.0,
                                            tol=0.0001, validation_fraction=0.1,
                                            verbose=0, warm_start=False)]],
          verbose=False),
 'insurance-pipeline.pkl')

In [30]:

loaded_pipeline = load_model('insurance-pipeline')

Transformation Pipeline and Model Successfully Loaded

In [31]:

print(loaded_pipeline)

Pipeline(memory=None,
         steps=[('dtypes',
                 DataTypes_Auto_infer(categorical_features=[],
                                      display_types=True, features_todrop=[],
                                      id_columns=[], ml_usecase='regression',
                                      numerical_features=[], target='charges',
                                      time_features=[])),
                ('imputer',
                 Simple_Imputer(categorical_strategy='not_available',
                                fill_value_categorical=None,
                                fill_value_numerical=None,
                                numeric_strategy...
                                           learning_rate=0.1, loss='ls',
                                           max_depth=3, max_features=None,
                                           max_leaf_nodes=None,
                                           min_impurity_decrease=0.0,
                                           min_impurity_split=None,
                                           min_samples_leaf=1,
                                           min_samples_split=2,
                                           min_weight_fraction_leaf=0.0,
                                           n_estimators=100,
                                           n_iter_no_change=None,
                                           presort='deprecated',
                                           random_state=123, subsample=1.0,
                                           tol=0.0001, validation_fraction=0.1,
                                           verbose=0, warm_start=False)]],
         verbose=False)

In [32]:

# deploy model on AWS S3
deploy_model(best_final, 'insurance-pipeline-aws', platform = 'aws',
             authentication = {'bucket' : 'pycaret-test'})

Model Succesfully Deployed on AWS S3

THE END¶

In [ ]: