Notebook image and notebook for feature reduction talk

A set of notebooks that leverage classical ML algorithms and DL neural nets using TF, Keras and Theano to address a series of issues in the field of conservation and biology.

Exploratory Data Analysis of Various datasets

Best for beginners | Well explained ML algorithms | organized Notebooks | Case Studies

This repository contains notebooks in which I have implemented ML Kaggle Exercises for academic and self-learning purposes. In my notebooks, I have implemented some basic processes involved in ML Data Processing like How to take care of Missing Values, Handling Categorical Variables, and operations like mapping, 'Grouping', 'Sorting', 'Renaming …

Extensive Collection of Jupyter Notebooks focused on Machine Learning covering different techniques includes Feature Engineering, Feature Selection, Feature Extraction, Model Training & Testing.

Data Science Python notebooks

This repository provides a collection of Jupyter Notebook examples demonstrating various feature selection techniques using Python.

Information gain of a car dataset was calculated in this notebook

Notebooks in this repository focus on code related to machine learning topics

This particular notebook consist of all the Feature Engineering technique and Feature Transformation technique

"A set of Jupyter Notebooks on feature selection methods in Python for machine learning. It covers techniques like constant feature removal, correlation analysis, information gain, chi-square testing, univariate selection, and feature importance, with datasets included for practical application.

This is the curated pile of notebooks/small projects which contains linear and non-linear regression models.

This notebook illustrates feature selection reverting many of the selection method results into pandas dataframes so that you get the appropriate column headings.

A collection of Jupyter notebooks for inference on Imbalance in the EU ETS: a non-parametric approach - C. Salvagnin, A. Glielmo, M.E. De Giuli, A. Mira

Jupyter notebook for IoT threat detection using ensemble machine learning. Features data preprocessing, model training (Logistic Regression, Decision Trees, Neural Networks, etc.), and ensemble techniques for enhanced accuracy.

This repository serves as a comprehensive resource for understanding and implementing various feature selection techniques, gaining familiarity with Jupyter Notebook, and mastering the process of model training and evaluation

Functions that implement the algorithms described in the preprint "Normalization and gene selection for single-cell RNA-seq UMI data using sampling-adjusted sums of squares of Pearson residuals with a Poisson model" and Jupyter notebooks that reproduce the results in the preprint and its Supplement 1

Determined which species of mosquito are most common, and which are most likely to carry WNV. Made dummies to reflect whether trap data corresponded to one of the chief carrier mosquitos, and whether trap data collected from a particular street. Obtained total pricipation data, and average temperature data for the 14 days prior to the date of ea…