diff --git a/README.md b/README.md
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--- a/README.md
+++ b/README.md
@@ -313,5 +313,176 @@ plt.show()
+Gradient Boosting - Classification
+
+
+````python
+ from sklearn.datasets import make_hastie_10_2
+ from sklearn.ensemble import GradientBoostingClassifier
+ import matplotlib.pyplot as plt
+
+ X, y = make_hastie_10_2(random_state=0)
+ X_train, X_test = X[:2000], X[2000:]
+ y_train, y_test = y[:2000], y[2000:]
+ print(X.shape, y.shape)
+ print(X[0:5,:])
+ print(y[0:5])
+
+ clf = GradientBoostingClassifier(n_estimators=100, learning_rate=0.1, max_depth=1, random_state=0)
+ clf.fit(X_train, y_train)
+ print('Accuracy score (training): {0:.3f}'.format(clf.score(X_train, y_train)))
+ print('Accuracy score (testing): {0:.3f}'.format(clf.score(X_test, y_test)))
+````
+
+
+
+Result
+
+
+````planetext
+ (12000, 10) (12000,)
+ [[ 1.76405235 0.40015721 0.97873798 2.2408932 1.86755799 -0.97727788
+ 0.95008842 -0.15135721 -0.10321885 0.4105985 ]
+ [ 0.14404357 1.45427351 0.76103773 0.12167502 0.44386323 0.33367433
+ 1.49407907 -0.20515826 0.3130677 -0.85409574]
+ [-2.55298982 0.6536186 0.8644362 -0.74216502 2.26975462 -1.45436567
+ 0.04575852 -0.18718385 1.53277921 1.46935877]
+ [ 0.15494743 0.37816252 -0.88778575 -1.98079647 -0.34791215 0.15634897
+ 1.23029068 1.20237985 -0.38732682 -0.30230275]
+ [-1.04855297 -1.42001794 -1.70627019 1.9507754 -0.50965218 -0.4380743
+ -1.25279536 0.77749036 -1.61389785 -0.21274028]]
+ [ 1. -1. 1. -1. 1.]
+ Accuracy score (training): 0.879
+ Accuracy score (testing): 0.819
+````
+
+
+
+XGBoosting - Regression
+
+
+````python
+ import numpy as np
+ import pandas as pd
+ from sklearn.datasets import load_boston
+ from sklearn.metrics import mean_squared_error
+ from sklearn.model_selection import train_test_split
+ import xgboost as xgb
+
+ boston = load_boston()
+ data = pd.DataFrame(boston.data)
+ data.columns = boston.feature_names
+ data['PRICE'] = boston.target
+ print(data.head())
+ X, y = data.iloc[:,:-1], data.iloc[:,-1]
+
+ X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=123)
+ xg_reg = xgb.XGBRegressor(objective='reg:squarederror', colsample_bytree=0.3, learning_rate=0.1, max_depth=5, alpha=10, n_estimators=10)
+ xg_reg.fit(X_train, y_train)
+ preds = xg_reg.predict(X_test)
+ rmse = np.sqrt(mean_squared_error(y_test, preds))
+ print('RMSE: %f' % (rmse))
+````
+
+
+
+Result
+
+
+````planetext
+ CRIM ZN INDUS CHAS NOX RM AGE DIS RAD TAX
+ 0 0.00632 18.0 2.31 0.0 0.538 6.575 65.2 4.0900 1.0 296.0
+ 1 0.02731 0.0 7.07 0.0 0.469 6.421 78.9 4.9671 2.0 242.0
+ 2 0.02729 0.0 7.07 0.0 0.469 7.185 61.1 4.9671 2.0 242.0
+ 3 0.03237 0.0 2.18 0.0 0.458 6.998 45.8 6.0622 3.0 222.0
+ 4 0.06905 0.0 2.18 0.0 0.458 7.147 54.2 6.0622 3.0 222.0
+
+ PTRATIO B LSTAT PRICE
+ 0 15.3 396.90 4.98 24.0
+ 1 17.8 396.90 9.14 21.6
+ 2 17.8 392.83 4.03 34.7
+ 3 18.7 394.63 2.94 33.4
+ 4 18.7 396.90 5.33 36.2
+ RMSE: 10.423243
+````
+
+
+
+
+LightGBM
+
+
+````python
+ from lightgbm import LGBMClassifier, LGBMRegressor
+ from lightgbm import plot_importance, plot_metric, plot_tree
+ from sklearn.datasets import load_iris
+ from sklearn.model_selection import train_test_split
+ from sklearn.model_selection import cross_validate
+
+ iris = load_iris()
+ X_train, X_test, y_train, y_test = train_test_split(iris.data, iris.target, test_size=0.2, random_state=123)
+ lgbmc = LGBMClassifier(n_estimators=400)
+ evals = [(X_test, y_test)]
+ lgbmc.fit(X_train, y_train, early_stopping_rounds=100, eval_metric='logloss', eval_set=evals, verbose=True)
+ preds = lgbmc.predict(X_test)
+
+ cross_val = cross_validate(
+ estimator=lgbmc,
+ X=iris.data, y=iris.target,
+ cv=5
+ )
+
+ print('avg fit time: {} (+/- {})'.format(cross_val['fit_time'].mean(), cross_val['fit_time'].std()))
+ print('avg fit time: {} (+/- {})'.format(cross_val['score_time'].mean(), cross_val['score_time'].std()))
+ print('avg fit time: {} (+/- {})'.format(cross_val['test_score'].mean(), cross_val['test_score'].std()))
+
+ plot_metric(lgbmc)
+ plot_importance(lgbmc, figsize=(10,12))
+ plot_tree(lgbmc, figsize=(28,14))
+````
+
+
+
+Result
+
+
+````planetext
+ [1] valid_0's multi_logloss: 0.95847
+ [2] valid_0's multi_logloss: 0.832184
+ [3] valid_0's multi_logloss: 0.731164
+ [4] valid_0's multi_logloss: 0.641056
+ [5] valid_0's multi_logloss: 0.571726
+ [6] valid_0's multi_logloss: 0.507286
+ [7] valid_0's multi_logloss: 0.454933
+ [8] valid_0's multi_logloss: 0.410205
+ [9] valid_0's multi_logloss: 0.372194
+ [10] valid_0's multi_logloss: 0.333919
+ [11] valid_0's multi_logloss: 0.310212
+ [12] valid_0's multi_logloss: 0.282326
+ [13] valid_0's multi_logloss: 0.257165
+ [14] valid_0's multi_logloss: 0.240836
+ [15] valid_0's multi_logloss: 0.225383
+ [16] valid_0's multi_logloss: 0.211583
+ [17] valid_0's multi_logloss: 0.199289
+ [18] valid_0's multi_logloss: 0.186269
+ [19] valid_0's multi_logloss: 0.171556
+ [20] valid_0's multi_logloss: 0.168245
+ [21] valid_0's multi_logloss: 0.161065
+ [22] valid_0's multi_logloss: 0.151371
+ [23] valid_0's multi_logloss: 0.148081
+ [24] valid_0's multi_logloss: 0.143843
+ [25] valid_0's multi_logloss: 0.140169
+ ...
+ [137] valid_0's multi_logloss: 0.376748
+ avg fit time: 0.5514350891113281 (+/- 0.3701610138582717)
+ avg fit time: 0.010002517700195312 (+/- 0.009552237668971902)
+ avg fit time: 0.9600000000000002 (+/- 0.04898979485566355)
+````
+
+
+
+
+
+
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