Time-series Data Preprocessing Studio in Jupyter notebook.

A notebook on visualizing and preprocessing of the EEG signals

Scripts and notebooks for pre-processing jet data into different formats, primarily the jet-image and Lund plane formats. The focus is on datasets used in the Landscape of Top Taggers challenge.

Notebooks for preprocessing and analysis of Planetscope 4 band data/imagery, using rasterio and fiona.

My notes over the course of different experiences in Machine Learning with some useful snippets I learnt in Python3.

Several notebooks that contain different functions implemented in Python to understand the basic steps to carry out a machine learning problem.

This repository will explain a set of data mining labs to make you familiar with the machine learning process.

Scripts used to transform our data before importing to jupyter notebook and tensorflow. Mainly used to match our data with satellite imagery

Implements a genetic algorithm to select the most impactful features in a dataset to improve classifier performance. Written in Jupyter Notebook using pandas, numpy, scikit-learn. Results displayed with accuracy, precision, recall, F1 score comparison to using all features.

Identification of brain tumour at a premature stage offers a opportunity of effective medical treatment. For this purpose, the present notebook is an application of deep learning and transfer learning for brain tumor detection using keras from Tensorflow framework.

I used this notebook to discuss different supervised learning approaches. In the notebook you can find evaluations of a logistic regression, a K-Nearest-Neighboor, a Support Vector Machine, a Decision Tree and the ensemble methods Random Forest, AdaBoost and XGBoost Classifyer.

Welcome to the Machine Learning Repository! This repository houses a collection of notebooks focused on machine learning projects, feature engineering, and feature selection techniques. Whether you are a beginner or an experienced data scientist, this repository offers valuable insights and implementations to enhance your machine learning skills.

This notebook analyses the word frequency in the novel Moby Dick. It illustrates a general-purpose NLP pre-processing pipeline.

PSO feature selection improves classifier performance. Implemented in Jupyter Notebook with pandas, numpy, scikit-learn. PSO done from scratch. Results compared using accuracy, precision, recall, F1 score. Improves results compared to using all features. Can be applied to various classification problems.

This repository consists of a Jupiter notebook showing the experiments conducted to create an RNN LSTM Model. Also, it shows the prediction done on the collected data from Twitter.

Scripts and notebooks for pre-processing di-jet event data into different formats, primarily the jet-image and Lund plane formats. The focus is on the LHC Olympics 2020 datasets.

This notebook demonstrates data augmentation as showcased by Tensorflow: a technique to increase the diversity of your training set by applying random (but realistic) transformations, such as image rotation.

This repository contains a Google Colab notebook that provides tools and techniques to help identify and locate bad labels in datasets. Bad labels refer to incorrect, inconsistent, or misleading annotations assigned to data points.

Jupyter notebook using machine learning techniques to explore the complex drivers of modern slavery. Models from a research paper are replicated and evaluated . Actions also include filling missing data, training regression models, and analyzing feature importance.