Basic Quantile Regression — R-bloggers

[This article was first published on R – insightR, and kindly contributed to R-bloggers]. (You can report issue about the content on this page here) Want to share your content on R-bloggers? click here if you have a blog, or here if you don’t. By Gabriel Vasconcelos Introduction Today we are going to talk about…

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Using Gradient Boosted Machine to Predict MPG for 2019 Vehicles — R-bloggers

Continuing on the below post, I am going to use a gradient boosted machine model to predict combined miles per gallon for all 2019 motor vehicles. Part 1: Using Decision Trees and Random Forest to Predict MPG for 2019 Vehicles The raw data is located on the EPA government site The variables/features I am using for the models are: Engine displacement (size), number of cylinders, transmission type, number of gears, air inspired method, regenerative braking type, battery capacity Ah, drivetrain, fuel type, cylinder deactivate, and variable valve. There are 1253 vehicles in the dataset (does not include pure electric vehicles) summarized below. fuel_economy_combined eng_disp num_cyl transmission Min. :11.00 Min. :1.000 Min. : 3.000 A :301 1st Qu.:19.00 1st Qu.:2.000 1st Qu.: 4.000 AM : 46 Median :23.00 Median :3.000 Median : 6.000 AMS: 87 Mean :23.32 Mean :3.063 Mean : 5.533 CVT: 50 3rd Qu.:26.00 3rd Qu.:3.600 3rd Qu.: 6.000 M :148 Max. :58.00 Max. :8.000 Max. :16.000 SA :555 SCV: 66 num_gears air_aspired_method Min. : 1.000 Naturally Aspirated :523 1st Qu.: 6.000 Other : 5 Median : 7.000 Supercharged : 55 Mean : 7.111 Turbocharged :663 3rd Qu.: 8.000 Turbocharged+Supercharged: 7 Max. :10.000 regen_brake batt_capacity_ah No :1194 Min. : 0.0000 Electrical Regen Brake: 57 1st Qu.: 0.0000 Hydraulic Regen Brake : 2 Median : 0.0000 Mean : 0.3618 3rd Qu.: 0.0000 Max. :20.0000 drive cyl_deactivate 2-Wheel Drive, Front :345 Y: 172 2-Wheel Drive, Rear :345 N:1081 4-Wheel Drive :174 All Wheel Drive :349 Part-time 4-Wheel Drive: 40 fuel_type Diesel, ultra low sulfur (15 ppm, maximum): 28 Gasoline (Mid Grade Unleaded Recommended) : 16 Gasoline (Premium Unleaded Recommended) :298 Gasoline (Premium Unleaded Required) :320 Gasoline (Regular Unleaded Recommended) :591 variable_valve N: 38 Y:1215 Starting with an untuned base model: trees boosted_stats_untuned RMSE Rsquared MAE 2.4262643 0.8350367 1.7513331 The untuned GBM model performs better than the multiple linear regression model, but worse than the random forest. I am going to tune the GBM by running a grid search: #create hyperparameter gridhyper_grid

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Using SVM to Predict MPG for 2019 Vehicles — R-bloggers

[This article was first published on Data Science, Data Mining and Predictive Analytics, and kindly contributed to R-bloggers]. (You can report issue about the content on this page here) Want to share your content on R-bloggers? click here if you have a blog, or here if you don’t. Continuing on the below post, I am…

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Forecasting tools in development — R-bloggers

(This article was first published on – R, and kindly contributed to R-bloggers) As I’ve been writing up a progress report for my NIGMS R35 MIRA award, I’ve been reminded at how much of the work that we’ve been doing is focused on forecasting infrastructure. A common theme in the Reich Lab is making operational…

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Dow Jones Stock Market Index (3/4): Log Returns GARCH Model — R-bloggers

(This article was first published on R Programming – DataScience+, and kindly contributed to R-bloggers) Categories Advanced Modeling Tags Data Visualisation Linear Regression R Programming… 2,448 more words

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Dow Jones Stock Market Index (2/4): Trade Volume Exploratory Analysis — R-bloggers

(This article was first published on R Programming – DataScience+, and kindly contributed to R-bloggers) Categories Basic Statistics Tags Data Visualisation Import Data R Programming This is the second part of the 4-series articles about Dow Jones Stock Market. To read the first part go to this link. In this part, I am going to…

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Dow Jones Stock Market Index (1/4): Log Returns Exploratory Analysis — R-bloggers

(This article was first published on R Programming – DataScience+, and kindly contributed to R-bloggers) Categories Basic Statistics Tags Data Visualisation Import Data R Programming In this four-post series, I am going to analyze the Dow Jones Industrial Average (DJIA) index on years 2007-2018. The Dow Jones Industrial Average (DIJA) is a stock market index…

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