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Library Installation & Purpose

📦 Library Setup (for First Model Training)

These are the core packages you’ll need to train and evaluate your first neural model:

✅ 1. Install Everything

Run this once to install all required packages:

install.packages(c("dplyr", "lubridate", "jsonlite", "httr", "keras", "keras3", "tensorflow", "reticulate"))

Optional (recommended for modeling structure later)

install.packages("tidymodels") # Includes yardstick, recipes, parsnip, etc.

✅ 2. Load Libraries in Your Script

Put this at the top of your R file:

library(dplyr) # data wrangling (filter, mutate, join)

library(lubridate) # handle datetime fields like created_at or cancelled_at

library(jsonlite) # parse JSON responses from Recharge/Klaviyo APIs

library(httr) # make authenticated API requests

library(keras3) # modern neural network building (Keras v3)

library(tensorflow) # TensorFlow backend for keras3

library(reticulate) # Python bridge

❓ What Each Package Does

Purpose of Library

Purpose of Library

Package

Purpose

dplyr

Filter, mutate, join, and organize tabular data

lubridate

Makes it easy to convert and compare timestamps

jsonlite

Converts JSON → data.frame when pulling API data

httr

Sends API requests with headers, tokens, etc.

keras3

Build and train neural networks with clear syntax

tensorflow

Backend engine that runs keras3 models

reticulate

Allows R to use Python for heavy lifting

tidymodels

(Optional) A structured framework for modeling & evaluation

There are no rows in this table

⁠

✅ 3. Validate That Everything Works

Make sure the backend is wired correctly:

library(keras3)

library(tensorflow)

# Should output TensorFlow version

tensorflow::tf_config()

If you see an error, run:

tensorflow::install_tensorflow()

And restart your R session.

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