Useful tools for constructing species distribution models
An Interactive Approach to Understanding Deep Learning with Keras
Awesome list of AutoML frameworks - curated by @oskar-j
Utilizing Kaggle Data and Real-World Data for Data Science and Prediction in Python, R, Excel, Power BI, and Tableau.
skrobot is a Python module for designing, running and tracking Machine Learning experiments / tasks. It is built on top of scikit-learn framework.
A Predictive Model for Marketing Campaigns
Project with examples of different recommender systems created with the Surprise framework. Different algorithms (with a collaborative filtering approach) are explored, such as KNN or SVD.
LSTM model for EURUSD price forecasting, exploring techniques from 'Machine Learning Aplicado al Trading'.
Evolutionary Neural Architecture Search framework that improves performance of your DL models
In this section, predicting the energy efficiency of buildings with machine learning algorithms.
TensorFlow and Keras are used for the construction and evaluation of Deep Learning models to predict success of companies that receive funding from a venture capital fund.
Recommender systems with collaborative filtering created with Apache Mahout framework. The system uses a Music Recommendation dataset for research purposes as input, but you can train it and predict recommendations with any other dataset.
Abundance-based species distribution models
The Disease Prediction Project uses AI/ML to predict diseases based on selected symptoms, designed for low-resource communities. It delivers fast, accurate predictions using a MLP model, offering tailored, efficient diagnostics for areas with limited healthcare access.
The data from this survey are used for a wide range of equipment design, sizing, and tariffing applications within the military and have many potential commercial, industrial, and academic applications.
18 Projects in AI & ML
🫀 Heart Disease Risk Prediction This project focuses on predicting the risk of heart disease using machine learning techniques. It includes thorough Exploratory Data Analysis (EDA), dimensionality reduction using Principal Component Analysis (PCA), model training, evaluation, and visualization of the results
Over-fitting and model tuning
The folliwing ML project involves EDA analysis of Election Dataset, Data preparation for modelling, and prediction using ML models. Also Text Analysis on the inaugral corpora from nltk to analyse the most frequently used words in Presidents' Speeches.
A complete ML project that explores feature engineering, model training (Decision Tree, Random Forest, Gradient Boosting), and model interpretation using SHAP and LIME.
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