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.

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.

A Predictive Model for Marketing Campaigns

In this section, predicting the energy efficiency of buildings with machine learning algorithms.

Evolutionary Neural Architecture Search framework that improves performance of your DL models

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.

LSTM model for EURUSD price forecasting, exploring techniques from 'Machine Learning Aplicado al Trading'.

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.

Abundance-based species distribution models

Deep learning framework for multi-horizon financial time series forecasting using RNN, GRU, and LSTM. Incorporates hyperparameter optimization, visualization, and multivariate sequence prediction across Open, High, Low, Close, and Volume indicators.

Over-fitting and model tuning

A complete ML project that explores feature engineering, model training (Decision Tree, Random Forest, Gradient Boosting), and model interpretation using SHAP and LIME.

Applying AI to medical use cases: Diagnoses of lung and brain disorders, Building risk models and survival estimators for heart disease via RF, and Using NLP to extract information from radiology reports.

✨ Stock Price Prediction Using Tesla Dataset ✨ In this project, I analyzed Tesla’s historical stock data to forecast future closing prices using machine learning models like Random Forest Regressor. Through data cleaning, feature engineering, and rich visual analytics, I explored patterns in price trends, volatility, and trading volume.

This project builds a predictive model to estimate visa approval likelihood using candidate and job-related features. It showcases an end-to-end machine learning workflow with EDA, feature engineering, and model tuning to automate parts of the visa evaluation process.