Pickl AI

JANUARY 3, 2024

EDA, imputation, encoding, scaling, extraction, outlier handling, and cross-validation ensure robust models. Example: Using techniques like TF-IDF (Term Frequency-Inverse Document Frequency) to convert text data into features suitable for Machine Learning models. Steps of Feature Engineering 1.

Machine Learning 52

Machine Learning Machine Learning Exploratory Data Analysis Cross Validation 52

Pickl AI

JULY 12, 2024

Jupyter notebooks allow you to create and share live code, equations, visualisations, and narrative text documents. Exploratory Data Analysis (EDA) EDA is a crucial preliminary step in understanding the characteristics of the dataset. Feature Engineering : Creating or transforming new features to enhance model performance.

Artificial Intelligence 52

Artificial Intelligence Artificial Intelligence Python Natural Language Processing 52

Pickl AI

DECEMBER 16, 2024

Documenting Objectives: Create a comprehensive document outlining the project scope, goals, and success criteria to ensure all parties are aligned. Exploratory Data Analysis (EDA): Conduct EDA to identify trends, seasonal patterns, and correlations within the dataset. accuracy, precision).

AI 52

AI AI Machine Learning Machine Learning 52

Mlearning.ai

MARCH 15, 2023

Data Extraction, Preprocessing & EDA & Machine Learning Model development Data collection : Automatically download the stock historical prices data in CSV format and save it to the AWS S3 bucket. Data Extraction, Preprocessing & EDA : Extract & Pre-process the data using Python and perform basic Exploratory Data Analysis.

Python 52

Python AWS Exploratory Data Analysis Machine Learning 52

Pickl AI

SEPTEMBER 12, 2024

Cross-Validation: A model evaluation technique that assesses how well a model will generalise to an independent dataset. Exploratory Data Analysis (EDA): Analysing and visualising data to discover patterns, identify anomalies, and test hypotheses.

Data Analyst 52

Data Analyst Data Science Machine Learning Machine Learning 52

Heartbeat

MAY 29, 2023

It is also essential to evaluate the quality of the dataset by conducting exploratory data analysis (EDA), which involves analyzing the dataset’s distribution, frequency, and diversity of text. Use a representative and diverse validation dataset to ensure that the model is not overfitting to the training data.

Machine Learning 78

Machine Learning Machine Learning Natural Language Processing Data Preparation 78