ML algorithms in Python
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Updated
Jun 23, 2022 - Jupyter Notebook
ML algorithms in Python
2nd Year: 1st - 92. A brief project and report on using the OULAD data set to predict and return a CSV of students final grades, from a variety of features, using a Random Forest or an SVC.
Driver codes in C for RTOS development on STM32F411VETx
Performed Sentiment Analysis on the Twitter Us Airline dataset
ECE NTUA Neural Networks
Revolutionize customer feedback analysis with our NLP Insights Analyzer. Utilize cutting-edge text preprocessing to transform raw reviews into a machine-friendly format. Explore sentiment models, such as Logistic Regression and Naive Bayes, employing cross-validation for model robustness.
Machine Learning - Classification
Classification Models
Implement an algorithm that can classify handwritten digits, based on MNIST database.
analyzing data, performing visualization, and training five different machine learning models, including two ensemble models
Detecting Fake Job Postings - Data Visualization, TF-IDF, XGBoost, SVC
Predicts gender of a name based on name metadata
Crime Category Prediction by Description. This is sample of multiple class Text Classification with Scikit-Learn.
In this project I used different classification algorithms to find out fatal health. I used Kaggles free GPUs and Datasets in this competion. Those different algorithms include random forrest, decision tree, xgboost and so on. Initially I used feature engineering to get my data into the best shape
Various mathematical classifiers in Python
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