Publications by Jeremiah Wang
Predicting Home Selling Prices
#https://datatables.net/reference/option/ options(DT.options = list(scrollX = TRUE, pagin=TRUE, fixedHeader = TRUE, searchHighlight = TRUE)) Intro Check this Kaggle Objective Predict Housing Prices using 2 supervised ml algos: elastic net random forest Get & Split Data a = read_csv('train.csv') %>% clean_names() %>% mutate(across(whe...
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Power Plant Part 2 - Predicting Power
Code Show All Hide All Power Plants - Predicting Power MISC Introduction Business Need Statistical Method Power Generation Inverter data set: Plant 1 Get and Clean Data EDA Forecasting at the Aggregate Level Power Generation Inverter data set: Plant 2 Get and Clean Data EDA Forecasting at the Aggregate Level for ‘good’ inverters For...
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Credit Card Customer Clustering
#https://datatables.net/reference/option/ options(DT.options = list(scrollX = TRUE, pagin=TRUE, fixedHeader = TRUE, searchHighlight = TRUE)) Introduction Please read this Kaggle Data Dictionary # CUSTID : Identification of Credit Card holder (Categorical) # # BALANCE : Balance amount left in their account to make purchases ( # # BALANCEF...
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Traveling around the Land of the Rising Sun
options(DT.options = list(fixedHeader = TRUE, paging = TRUE, scrollX = TRUE)) Filtering & Augmenting Datset Check out ds glimpse(j) ## Rows: 15 ## Columns: 12 ## $ city <chr> "Tokyo", "Yokohama", "Osaka", "Nagoya", "Sapporo"... ## $ lat <dbl> 35.68500, 35.43333, 34.68333, 35.18333, 43.06667,... ## $ lng ...
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Clustering Mall Customers
#https://datatables.net/reference/option/ options(DT.options = list(scrollX = TRUE, pagin=TRUE, fixedHeader = TRUE, searchHighlight = TRUE)) Intro Check out this Kaggle webpage In one piped statement: read in data convert char to factor vars rename all colnames lowercase order cols by name: alphabetically order cols by datatype: nominal, then ...
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Exploring Crime in Washington DC
Intro Check out this Kaggle Objective Understand crime in WDC by exploring data at both macro and micro levels, make recommendations for 2018, and then make future predictions. Goals Find total offenses by each factor group Historically Latest Year 2017 Find total offenses by each offense type/ward group Historically Latest Year 2017 Tr...
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Predicting Turbine Power
Objective Predict the capacity of wind turbines in Canada based on turbine features Algos Type: Supervised Machine Learning Random Forests XGBoost (Gradient Boosted Trees) Learning Goals Learn how to use the ‘xgboost’ package within the tidymodels framework Compare implementations / algo differences of rf vs xgb Get Data #a = read_csv("h...
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Learning Flexdashboard Part 1
flexdashboard Jeremiah W — 1/30/2021 Column Counts of Trips by Carrier/Origin Column Mean Arrival Delay by Carrier / Month Mean Departure Delay by Carrier / Month ...
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