Projecte-Machine-Learning

ML exercices at IT ACADEMY

1. Project description (objectives, algorithms & metrics)
2. API & Scraping
3. Dataset documentation (sources, collection methods, format and structure, limitations & sensitive considerations)
4. EDA and filtering (basic descriptions, detection and management of missing values, detection and management of outliers, identification of the characteristics relevant to the resulting variable, detection and management of the imbalance of classes & insights)
5. Splitering the dataset into training and testing, code categorical variables, standardize features and reduce the dimensionality of the dataset.
6. Selection of models and metrics.
7. Training, evaluation and interpretation of the model.
8. Deployment of the model in Strimlit.