#!/usr/bin/env python
# coding: utf-8

# In[1]:


import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import scipy
import sklearn

#feature engr'ing
from sklearn.preprocessing import OneHotEncoder

#models
from sklearn import model_selection
from sklearn.linear_model import LinearRegression
from sklearn.ensemble import RandomForestRegressor
from sklearn.linear_model import ElasticNet
from sklearn.linear_model import Lasso
from sklearn.linear_model import Ridge
from sklearn.svm import SVR

#metrics
from sklearn.metrics import classification_report
from sklearn.metrics import confusion_matrix
from sklearn.metrics import accuracy_score

#ignore some warnings we dont care about
import warnings
warnings.filterwarnings("ignore", category=FutureWarning)


# In[7]:


names = ['instant','date','season','year','month','hour','holiday','weekday','workingday','weathersit','temp','atemp','humidity','windspeed','casual','registered','total']
dataset = pd.read_csv('hour.csv',names=names,header=0,usecols=[*range(2,17)])

print(dataset.head(5))


# In[8]:


weatherSits = dataset['weathersit'].values
total = dataset['total'].values

#One-Hot Encoding
OHEr = OneHotEncoder(sparse=False)
weatherSits = weatherSits.reshape(len(weatherSits), 1)
OHEd = OHEr.fit_transform(weatherSits)

#remove unwanted columns
dataset = dataset.drop(columns=['weathersit','casual','registered','total'])

#add new OHE columns and put total back at the end
dataset['weather1'] = np.transpose(OHEd)[0]
dataset['weather2'] = np.transpose(OHEd)[1]
dataset['weather3'] = np.transpose(OHEd)[2]
dataset['weather4'] = np.transpose(OHEd)[3]
dataset['total'] = total

print(dataset.head(5))


# In[9]:


array = dataset.values
X = array[:,0:15]
Y = array[:,15]
validation_size = 0.20
seed = 11

X_train, X_validation, Y_train, Y_validation = model_selection.train_test_split(X,Y,test_size=validation_size,random_state=seed)


# In[14]:


reg_model = LinearRegression()
reg_model.fit(X_train,Y_train)
reg_model.score(X_validation,Y_validation)


# In[33]:


reg_model = RandomForestRegressor(max_depth=60,random_state=0,n_estimators=500)
reg_model.fit(X_train,Y_train)
reg_model.score(X_validation,Y_validation)


# In[17]:


reg_model = ElasticNet(random_state=0)
reg_model.fit(X_train,Y_trcorner&2
              ain)
reg_model.score(X_validation,Y_validation)


# In[19]:


reg_model = Lasso(random_state=0)
reg_model.fit(X_train,Y_train)
reg_model.score(X_validation,Y_validation)


# In[21]:


reg_model = Ridge(random_state=0)
reg_model.fit(X_train,Y_train)
reg_model.score(X_validation,Y_validation)


# In[32]:


reg_model = SVR(gamma='scale', C=400.0, epsilon=0.2)
reg_model.fit(X_train,Y_train)
reg_model.score(X_validation,Y_validation)


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