Linear Regressor

Author: sarincr

Diff for: 27.XGBRegressor.py

@@ -0,0 +1,13 @@
+import numpy as np
+import pandas as pd
+import xgboost as xg
+from sklearn.model_selection import train_test_split
+from sklearn.metrics import mean_squared_error as MSE
+import matplotlib.pyplot as plt
+dataset = pd.read_csv("LinearData.csv")
+X, y = dataset.iloc[:, :-1], dataset.iloc[:, -1]
+train_X, test_X, train_y, test_y = train_test_split(X, y,test_size = 0.25, random_state = 123)
+xgb_r = xg.XGBRegressor(objective ='reg:linear', n_estimators = 15, seed = 123)
+xgb_r.fit(train_X, train_y)
+pred = xgb_r.predict(test_X)
+print("RMSE : % f" ,np.sqrt(MSE(test_y, pred)))

Diff for: LinearData.csv

@@ -0,0 +1,31 @@
+X,Y
+1,953
+2,2068
+3,2871
+4,3972
+5,5240
+6,5610
+7,7196
+8,7936
+9,8577
+10,10950
+11,11077
+12,12468
+13,13780
+14,13888
+15,14325
+16,14496
+17,16116
+18,17064
+19,20387
+20,18620
+21,20517
+22,22264
+23,22770
+24,25344
+25,23450
+26,28522
+27,25920
+28,30072
+29,31813
+30,32370