# -*- coding: utf-8 -*- """ Created on Sun Apr 15 21:20:26 2018 knn with CCS dataset from uci @author: shifuddin """ from sklearn.model_selection import train_test_split from sklearn import neighbors from sklearn.metrics import mean_squared_error from math import sqrt from load_data import load_excel ''' Load feature values as X and target as Y here we read day dataset ''' uri = 'https://archive.ics.uci.edu/ml/machine-learning-databases/concrete/compressive/Concrete_Data.xls' X,y = load_excel(uri, 'Sheet1', 0,8, 8,9) ''' Split into training and test set ''' X_train, X_test, y_train, y_test =train_test_split(X, y,test_size=0.2, random_state=1) knn_regressor = neighbors.KNeighborsRegressor(algorithm='auto', n_neighbors= 30, weights = 'uniform') knn_regressor.fit(X_train, y_train) y_pred = knn_regressor.predict(X_test) rmse = sqrt(mean_squared_error(y_test, y_pred))
decission tree with banknote
@author: shifuddin
"""
from load_data import load_excel
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.preprocessing import PolynomialFeatures
from sklearn.metrics import mean_squared_error
from math import sqrt
'''
Load feature values as X and target as Y
here we read day dataset
'''
uri = 'https://archive.ics.uci.edu/ml/machine-learning-databases/00242/ENB2012_data.xlsx'
X, y = load_excel(uri, 'Φύλλο1', 0, 8, 8, 10)
'''
Split into training and test set
'''
X_train, X_test, y_train, y_test = train_test_split(X,
y,
test_size=0.2,
random_state=1)
'''
Polynomial feature scaling
'''
ply_ft = PolynomialFeatures(degree=2)
X_train = ply_ft.fit_transform(X_train)
X_test = ply_ft.transform(X_test)
'''
Fit DecisionTreeRegressor with Bike Day data