jieyima / cryptocurrency_investment_analysis_and_modeling Goto Github PK

accumulate_by <- function(dat, var) {
  var <- lazyeval::f_eval(var, dat)
  lvls <- plotly:::getLevels(var)
  dats <- lapply(seq_along(lvls), function(x) {
    cbind(dat[var %in% lvls[seq(1, x)], ], frame = lvls[[x]])
  })
  dplyr::bind_rows(dats)
}

d <- txhousing %>%
  filter(year > 2005, city %in% c("Abilene", "Bay Area")) %>%
  accumulate_by(~date)

def regression1(X_train, X_test, y_train, y_test):
    Regressor = {'Random Forest Regressor': RandomForestRegressor(n_estimators=200)}

    for name, clf in Regressor.items():
        print(name)
        clf.fit(X_train, y_train)
        
        st = time.time()
        print(f'R-Squared: {r2_score(y_test, clf.predict(X_test)):.9f}')
        et = time.time()
        print(f'Mean absolute error: {mean_absolute_error(y_test, clf.predict(X_test)):.9f}')
        print(f'Mean squared error: {mean_squared_error(y_test, clf.predict(X_test)):.9f}')
        print('Execution time:', et - st)
        print()
        
# define regression function
def regression2(X_train, X_test, y_train, y_test):
    Regressor = {'Extreme Gradient Boosting Regressor': GradientBoostingRegressor(n_estimators=500)}

    for name, clf in Regressor.items():
        print(name)
        clf.fit(X_train, y_train)
        
        st = time.time()
        print(f'R-Squared: {r2_score(y_test, clf.predict(X_test)):.9f}')
        et = time.time()
        print(f'Mean absolute error: {mean_absolute_error(y_test, clf.predict(X_test)):.9f}')
        print(f'Mean squared error: {mean_squared_error(y_test, clf.predict(X_test)):.9f}')
        print('Execution time:', et - st)
        print()


# Bitcoin (BTC)
print('Bitcoin (BTC):')
regression1(X_train_BTC, X_test_BTC, y_train_BTC, y_test_BTC)


plt.figure(figsize=(15,8))
(bitcoin[:0]['daily_avg']).plot(label='Historical Price')
(bitcoin[-31:]['daily_avg']).plot(label='Predicted Price')

plt.xlabel('Time')
plt.ylabel('Price in USD')
plt.title('Prediction with Random Forest Regressor')

# Bitcoin (BTC)
print('Bitcoin (BTC):')
regression2(X_train_BTC, X_test_BTC, y_train_BTC, y_test_BTC)


plt.figure(figsize=(15,8))
(bitcoin[:0]['daily_avg']).plot(label='Historical Price')
(bitcoin[-31:]['daily_avg']).plot(label='Predicted Price')

plt.xlabel('Time')
plt.ylabel('Price in USD')
plt.title('Prediction with Extreme Gradient Boosting Regressor')```