Simulations for the paper "Deep Learning for the Gaussian Wiretap Channel by Rick Fritschek, Rafael F. Schaefer, Gerhard Wunder"
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Updated
Jun 20, 2024 - Jupyter Notebook
Simulations for the paper "Deep Learning for the Gaussian Wiretap Channel by Rick Fritschek, Rafael F. Schaefer, Gerhard Wunder"
🏠 Real Estate Price Prediction using Artificial Neural Networks (ANN) with TensorFlow/Keras. Analyzes 47+ property features to predict housing prices with 98% accuracy (R² Score). Includes data preprocessing, EDA, model training, and evaluation. 📊🤖
Electronic Music Classification ML
The project “HR Analytics – Employee Attrition Prediction” aims to predict employee attrition based on various work-related factors using the IBM HR Analytics Dataset.
Machine/Deep Learning Concepts in Time Series Context!
Healthcare data
The Telco Customer Churn dataset, the project involves collecting, cleaning, and analyzing customer data to uncover key factors influencing churn.
Machine Learning Drug Classification
Cybersecurity threat detection project that analyzes AWS CloudWatch web traffic logs to identify suspicious and anomalous interactions using machine learning models like Isolation Forest, Random Forest, and Neural Networks.
Prediction of creditworthiness for issuing a credit card
This data analysis finds out the trends and analysis of avocado data by year
Increase processing efficiency via principal components analysis
Classifies IMDb movie reviews as positive or negative using NLP.
This repository contains all the practical assignments and experiments performed during my second year in the Data Science course. It covers fundamental concepts such as data preprocessing, data visualization, statistical analysis, and basic machine learning techniques.
Predict whether customers will purchase a product by implementing KNN model
Using ride data, Uber guides drivers to areas with high demand to speed pick-ups and lower cancellation rates.
Process of converting a categorical features values into meaningful numerical values. OneHotEncoding is a method to convert categorical data into a binary (0 or 1) format so that it can be used in machine learning models. Each unique category becomes a new column, and a row has 1 in the column of its category and 0s elsewhere.
PROVA DATA SCIENCE HACKATÓ
Realizo una reducción de dimensionalidad a dos datasets AnsurMen.csv y AnsurWomen.csv
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