Working through all the exercises for An Introduction to Statistical Learning by Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani.

Solved problem of famous book in machine learning, deep learning for learners

Exploring Different types of Supervised Learning Classifications within the Scikit-Learn package.

Classification of an imbalanced dataset using SMOTE oversampling technique and ML Algorithms - KNN , XGBoost and Naive Bayes classifier

The goal of this project is to analyze data related to a marketing campaign and subsequently develop a machine learning model that can predict customers' response to the campaign. The overall benefit of this application is the efficient utilization of marketing budget.

Data science project including: 1) data anlysis of data science books and 2) price prediction using machine learning.

Predict diabetes using machine learning models. Experiment with logistic regression, decision trees, and random forests to achieve accurate predictions based on health indicators. Complete lifecycle of ML project included.

Benchmarking bank data to enhance marketing strategies. Models: Decision Tree and Random Forest. Libraries: Pandas, Matplotlib, Seaborn, Scikit-Learn, Numpy. Findings: Customer patterns and seasonal behaviors.

Revolutionize sales forecasting for Rossmann stores with our high-accuracy XGBoost model, leveraging data analysis, feature engineering, and machine learning to predict sales up to six weeks in advance.

This repository serves as a comprehensive showcase of my skills and expertise in data science, encompassing various projects and exercises completed throughout the bootcamp.

Energy consumption prediction of a building

This is a machine learning project where I utilized the LeNet-5 architecture to create a convolutional deep network that classifies 43 different kind of traffic signs. I've made sure to include a full step-by-step implementation of the project as well as detailed notes for every step.

An intelligent system based on neural networks to detect the occupancy of an office room from sensors data. Including implementation, testing, exploration of behaviour and explanation of the system, also optimisation.

Emotion detection from small images using CNN

This project details the various steps that I took to build my own spam email classifier that is able to classify an email as spam or non-spam(ham) via a set of its features.

In this project, the model is save and reused for prediction. Also, it is being containerize with docker to be ready for deployment.

A demonstration of MLflow working end-to-end on sample data sets.

Fine-tuning GPT-2 models with custom text corpora, utilizing Hugging Face's Transformers library and advanced training techniques for sophisticated text generation applications.