If you found these projects interesting and helpful, you can share a coffee with me, by accessing the below link.
In this project, we create a miniature version of a Large Language Model (LLM) using Python. The goal is to build a conversational AI that can answer user questions based on a predefined knowledge base stored in a JSON file.
Read MoreIn this project, we'll walk you through building a neural network model to predict the appropriate medication for patients based on their symptoms. We'll use a dataset containing patient symptoms and prescribed medications, preprocess the data, train a neural network, and make predictions based on new input symptoms.
Read MoreThis project aims to predict the total cost of road repairs using a Linear Regression model. The process involves data preprocessing, training a machine learning model, evaluating its performance, and visualizing the results.
Read MoreThis project aims to analyze and visualize crime data in Bucharest using geospatial mapping and machine learning techniques. The primary focus is on classifying crime severity and making predictions about crime severity using the K-Nearest Neighbors (KNN) algorithm. This comprehensive approach combines data processing, machine learning, and interactive visualization to provide valuable insights into crime patterns.
Read MoreThis project demonstrates a complete workflow for building and evaluating a machine learning model (Random Forest Classifier) to predict traffic congestion in Bucharest based on various features such as weather conditions, road type, and traffic volume. It also includes visualization using folium to map traffic observations.
Read MoreIn this project, we demonstrate a practical approach to forecasting interest rates using a Linear Regression model in Python. The goal is to analyze historical interest rate data, build a predictive model, and generate forecasts for future periods.
Read MoreThis Python script is designed for analyzing and predicting traffic accident severity using machine learning. It processes accident-related data, trains a predictive model, evaluates its performance, and provides insights into the key features influencing accidents.
Read MoreImage classification has numerous real-world applications, ranging from object detection in self-driving cars, to medical image analysis, and even identifying galaxies in astronomical images. With the help of neural networks, we can develop models that can automatically learn and improve their performance in identifying patterns and features within images, leading to highly accurate predictions.
Read MoreIn an era of information overload, distinguishing fact from fiction has become increasingly challenging. The rise of digital media has democratized information sharing, but it has also created unprecedented opportunities for the spread of misinformation.
Read MoreIn the rapidly evolving world of artificial intelligence, understanding how neural networks function is crucial for both developers and enthusiasts. Our app offers dynamic visualizations of neural networks, allowing users to see how data flows through different layers..
Read MoreThe "Random Forest Classifier App" is a web-based application designed for interactive data input and visualization of feature importance. It provides an intuitive interface for users to define, input, and...
Read MoreThe Linear Regression Sandbox App offers a hands-on way to understand how linear regression works, and how the gradient descent algorithm is used to optimize the model. The app includes...
Read MoreThis tool helps users in understanding how K-means clustering operates, the impact of different K values, the process of ...
Read MoreThe 'Reinforcement Learning Playground App' is a powerful tool for understanding reinforcement learning and the Q-learning algorithm ...
Read MoreIf you found these projects interesting and helpful, you can share a coffee with me, by accessing the below link.
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