Featurizing time series data into a standard tabular format for classical ML models and improving accuracy using AutoML Source: Ahasanara AkterThis article delves into enhancing the process of forecasting daily energy consumption levels by transforming a time series dataset into a tabular format using open-source libraries. We explore the application of a popular multiclass classification…

Whether you’re applying to your first internship to running a multidisciplinary team of analysts and engineers, data science careers come with their own specific set of challenges. Some of these might be more exciting than others, and others can be downright tedious—that’s true in any job, of course—but we believe in framing all of these…

Imports We import modules from Hugging Face’s transforms, peft, and datasets libraries. from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline from peft import prepare_model_for_kbit_training from peft import LoraConfig, get_peft_model from datasets import load_dataset import transformers Additionally, we need the following dependencies installed for some of the previous modules to work. !pip install auto-gptq !pip install optimum !pip…