An automated cross-validation framework for machine learning models, offering streamlined and efficient model evaluation.

Machine Learning Algorithms

Regression, Classification, Clustering, Dimension-reduction, Anomaly detection

Stata package to implement cross-validation methods for statistical models

Discover the power of Machine Learning through practical projects in our dynamic repository. Dive into linear models, classification techniques, and more, with projects ranging from spam detection to fruit classification. Perfect for learners at all levels, our repository grows with new insights and applications. Stay tuned for continuous updates!

Neural Network

Práctica de clasificación con Machine Learning en el dataset del Titanic, abordando exploración de datos, preprocesamiento, selección de métricas y modelos, con el objetivo de analizar detalladamente los resultados obtenidos.

In this comprehensive machine learning project, I executed the entire machine learning life cycle. Designed a streamlined and visually appealing interface using Streamlit. Ensuring a user-friendly experience for individuals to input their relevant information effortlessly. Handed off well-documented and easily modifiable code.

Bank Campaign Optimization: Targeting Term Deposit Customers

hgboost is a python package for hyper-parameter optimization for xgboost, catboost or lightboost using cross-validation, and evaluating the results on an independent validation set. hgboost can be applied for classification and regression tasks.

Forecasting pipeline for python

This is a Premiere Project done by Team Gitlab in Hamoye Data Science Program Dec'22. Out of 5 models used on the data, Random Forest Classifier was used to further improve the prediction of characters death. With parameter tuning and few cross validation, we were able to reduce the base error by 5.42% and increase accuracy by 2,42%.

This is a collection of personal datasets I analysed from Kaggle using a myriad of technical skills including Python, Scikit-Learn, Sklearn Pre-Processing, matplotlib, SciPy, ML, Statistical Inference, and Mathematics.

Web application that can be used to predict breast cancer, based on cell features. Model classifies result as benign or malignant.

Exploratory data analysis and predictive modeling using Tinder matching data. Model predicts whether you would find a relationship or not. The EDA was showcased with a web application, in collaboration with software engineer students. This project was part of Practicum Code Pudding 2.0 competition.

Machine Learning project on household median income prediction using census data.

In this project, we will predict the price for AMES House and learn Machine Learning Algorithms, different data preprocessing techniques such as Exploratory Data Analysis, Feature Engineering, Feature Selection, Feature Scaling and finally to build a machine learning model.

Using Machine Learning to predict Rossmann stores sales

What I taught myself about Machine learning

Group Project for Primo Academy