tylerknutson

rfGrid =  expand.grid(mtry = c(2,16,30))
m_rf = train(x=train_repNA, y=labels, method="rf", weights=weight, verbose=TRUE,
trControl=ctrl, metric="AMS")
m_rf$finalModel
rfTestPred = predict(m_rf, newdata=test_repNA, type="prob")

predicted = rep("b",550000) 
predicted[rfTestPred[,2]>=threshold] = "s"
weightRank = rank(rfTestPred[,2], ties.method= "random")

tylerknutson

michelin_rest_scrape = []
michelin_rev_scrape = []

michelin_rest_scrape.append(url_cycle(m_url[0:]))
michelin_rev_scrape.append(url_cycle(m_url[0:]))

pd.DataFrame(michelin_rest_scrape[0]).to_csv('michelin_restaurant_data.csv', header = True)

flattened_list = []
for x in michelin_rev_scrape:

tylerknutson

t.test(t.gain.elite_ann_chg, t.none.elite_ann_chg, alternative = "greater")

Welch Two Sample t-test

        data:  t.gain.elite_ann_chg and t.none.elite_ann_chg
        t = 2.6138, df = 48.33, p-value = 0.005952
        alternative hypothesis: true difference in means is greater than 0
        95 percent confidence interval:
        0.3561711       Inf
        sample estimates:

tylerknutson

# create summary rows for all reds, all whites, and all districts, and by varietal

summary_1 = agro %>% group_by(year, type, reporting_district) %>% summarise(total_tons_crushed = sum(tons_crushed, na.rm = TRUE), total_tons_purchased = sum(tons_purchased, na.rm = TRUE))
summary_2 = agro %>% group_by(year, type) %>% summarise(total_tons_crushed = sum(tons_crushed, na.rm = TRUE), total_tons_purchased = sum(tons_purchased, na.rm = TRUE))
summary_3 = agro %>% group_by(year, type, varietal) %>% summarise(total_tons_crushed = sum(tons_crushed, na.rm = TRUE), total_tons_purchased = sum(tons_purchased, na.rm = TRUE))

tylerknutson

import requests
import json
import time
def getdata(offset_no, t = []):
    r1  = np.arange(1,96000,500)
    r2 = np.array([0])
    r = np.concatenate([r2,r1[1:len(r1)]])
    i = offset_no
    for i in r[r>=offset_no]:
        try: