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README.md

@@ -23,8 +23,161 @@ The easiest way to install pycaret is using pip.
 ```python
 pip install pycaret
 ```
+
+## Quick Start
+As of beta 0.0.4 classification, regression and nlp modules are available. Future release will be include Anamoly Detection, Association Rules, Clustering, Recommender System and Time Series.
+
+### Classification
+
+Getting data from pycaret repository
+
+```python
+from pycaret.datasets import get_data
+juice = get_data('juice')
+```
+
+Initializing the pycaret environment setup
+
+```python
+exp1 = setup(juice, 'Purchase')
+```
+
+Creating a simple logistic regression (includes fitting, CV and metric evaluation)
+```python
+lr = create_model('lr')
+```
+
+List of available estimators:
+
+Estimator                   Abbreviated String     Original Implementation 
+---------                   ------------------     -----------------------
+Logistic Regression         'lr'                   linear_model.LogisticRegression
+K Nearest Neighbour         'knn'                  neighbors.KNeighborsClassifier
+Naives Bayes                'nb'                   naive_bayes.GaussianNB
+Decision Tree               'dt'                   tree.DecisionTreeClassifier
+SVM (Linear)                'svm'                  linear_model.SGDClassifier
+SVM (RBF)                   'rbfsvm'               svm.SVC
+Gaussian Process            'gpc'                  gaussian_process.GPC
+Multi Level Perceptron      'mlp'                  neural_network.MLPClassifier
+Ridge Classifier            'ridge'                linear_model.RidgeClassifier
+Random Forest               'rf'                   ensemble.RandomForestClassifier
+Quadratic Disc. Analysis    'qda'                  discriminant_analysis.QDA
+AdaBoost                    'ada'                  ensemble.AdaBoostClassifier
+Gradient Boosting           'gbc'                  ensemble.GradientBoostingClassifier
+Linear Disc. Analysis       'lda'                  discriminant_analysis.LDA
+Extra Trees Classifier      'et'                   ensemble.ExtraTreesClassifier
+Extreme Gradient Boosting   'xgboost'              xgboost.readthedocs.io
+Light Gradient Boosting     'lightgbm'             github.com/microsoft/LightGBM
+
+Tuning a model using GridSearchCV with pre-defined grids.
+```python
+tuned_lr = tune_model('lr')
+```
+Ensembling trained model
+```python
+dt = create_model('dt')
+dt_bagging = ensemble_model('dt', method='Bagging')
+dt_boosting = ensemble_model('dt', method='Boosting')
+```
+
+Creating a voting classifier
+```python
+voting_clf1 = blend_models() #creates voting classifier for entire library
+
+#create voting classifier for specific models
+lr = create_model('lr')
+svm = create_model('svm')
+mlp = create_model('mlp')
+xgboost = create_model('xgboost')
+
+voting_clf2 = blend_models( [lr, svm, mlp, xgboost] )
+```
+
+Creating a voting classifier
+```python
+voting_clf1 = blend_models() #creates voting classifier for entire library
+
+#create voting classifier for specific models
+lr = create_model('lr')
+svm = create_model('svm')
+mlp = create_model('mlp')
+xgboost = create_model('xgboost')
+
+voting_clf2 = blend_models( [lr, svm, mlp, xgboost] )
+```
+
+Stacking Models in one layer
+```python
+#create individual classifiers
+lr = create_model('lr')
+svm = create_model('svm')
+mlp = create_model('mlp')
+xgboost = create_model('xgboost')
+
+stacker = stack_models( [lr,svm,mlp], meta_model = xgboost )
+```
+
+Stacking Models in Multiple layers
+```python
+#create individual classifiers
+lr = create_model('lr')
+svm = create_model('svm')
+mlp = create_model('mlp')
+gbc = create_model('gbc')
+nb = create_model('nb')
+lightgbm = create_model('lightgbm')
+knn = create_model('knn')
+xgboost = create_model('xgboost')
+
+stacknet = create_stacknet( [ [lr,svm,mlp], [gbc, nb], [lightgbm, knn] ], meta_model = xgboost )
+#meta model by default is Logistic Regression
+```
+
+Plot Models
+```python
+lr = create_model('lr')
+plot_model(lr, plot='auc')
+```
+List of available plots:
+
+Name                        Abbreviated String     Original Implementation 
+---------                   ------------------     -----------------------
+Area Under the Curve         'auc'                 .. / rocauc.html
+Discrimination Threshold     'threshold'           .. / threshold.html
+Precision Recall Curve       'pr'                  .. / prcurve.html
+Confusion Matrix             'confusion_matrix'    .. / confusion_matrix.html
+Class Prediction Error       'error'               .. / class_prediction_error.html
+Classification Report        'class_report'        .. / classification_report.html
+Decision Boundary            'boundary'            .. / boundaries.html
+Recursive Feat. Selection    'rfe'                 .. / rfecv.html
+Learning Curve               'learning'            .. / learning_curve.html
+Manifold Learning            'manifold'            .. / manifold.html
+Calibration Curve            'calibration'         .. / calibration_curve.html
+Validation Curve             'vc'                  .. / validation_curve.html
+Dimension Learning           'dimension'           .. / radviz.html
+Feature Importance           'feature'                   N/A 
+Model Hyperparameter         'parameter'                 N/A 
+
+Saving Model for Deployment
+```python
+lr = create_model('lr')
+save_model(lr, 'lr_23122019')
+```
+Saving Entire Experiment Pipeline
+```python
+save_experiment('expname1')
+```
+Loading Model / Experiment
+```python
+m = load_model('lr_23122019')
+e = load_experiment('expname1')
+```
+AutoML
+```python
+aml1 = automl()
+```
 ## Documentation
-Documentation and detailed tutorials are underconstruction. They will be uploaded on our git as well as http://www.pycaret.org
+Documentation work is in progress. They will be uploaded on our website http://www.pycaret.org as soon as they are available. (Target Availability : 21/01/2020)
 
 ## Contributions
 Contributions are most welcome. To make contribution please reach out moez.ali@queensu.ca