This repository contains all of my works on machine learning, including a variety of projects, experiments, and research related to the field. The repository includes machine learning algorithms that I have implemented from scratch using Python and popular ML libraries like TensorFlow and PyTorch, as well as pre-trained machine learning models that I have created for tasks such as object recognition, natural language processing, and time series forecasting.
In addition to these projects, the repository also includes tutorials and guides on how to use these algorithms and models, including code examples and detailed explanations. I have also included research papers and articles that I have written on new developments and applications of ML techniques.
Overall, this repository provides a comprehensive collection of my works on machine learning, showcasing my skills and knowledge in the field. It is a valuable resource for anyone looking to learn more about machine learning or collaborate on ML projects. I am always looking for new opportunities to expand my knowledge and apply my skills in the field of machine learning.
To build the clickbait detection model, I first collected a large dataset of headlines labeled as either clickbait or non-clickbait. I used natural language processing techniques to extract relevant features from the headlines, such as the use of certain words or phrases that are commonly associated with clickbait. I then trained a machine learning model, such as a support vector machine or a random forest classifier, using these features and cross-validation to evaluate its performance and fine-tune the model. Finally, I tested the model on a held-out test set to evaluate its performance and make any final adjustments. Overall, the process involved collecting and preparing the data, extracting relevant features, training and evaluating the model, and testing its performance on unseen data.
In recent years, the issue of waste management has become increasingly important due to the growing amount of garbage being generated by human activity. Proper waste management is essential for protecting the environment and preventing pollution, as well as for reducing the amount of resources needed to dispose of waste.
One key aspect of waste management is the proper sorting of garbage into different categories, such as paper, plastic, metal, and glass. This allows for more efficient recycling and disposal of waste, and helps to reduce the amount of waste that ends up in landfills. However, manually sorting trash can be a time-consuming and error-prone process, particularly in large facilities with a high volume of waste.
This project aims to automate the process of sorting trash based on its recyclability status. The project uses a combination of computer vision and machine learning techniques to take images of individual pieces of trash and classify them into one of five categories: glass, leather, paper, plastic, and metal. The system achieves an accuracy of 88% on the test data.
The first step in the process is to extract useful features from the images of the trash using a technique called Scale-Invariant Feature Transform (SIFT). This allows the system to identify key points in the images that can be used to distinguish between the different types of trash.
Next, the system uses a machine learning algorithm called K-means clustering to group the images into clusters based on their visual characteristics. This allows the system to automatically identify common patterns in the images and group them accordingly.
Finally, the system uses a support vector machine (SVM) to classify the images into their respective categories. The SVM uses the output from the K-means clustering step to train itself, and is able to achieve an accuracy of 88% on the test data.
The project demonstrates the effectiveness of using computer vision and machine learning techniques to automatically classify trash based on its recyclability status. This could potentially be useful in a variety of settings, including waste management facilities and households, to help people sort their trash more efficiently and reduce the amount of waste that ends up in landfills.
Future work in this area could include exploring other machine learning algorithms and feature extraction techniques, as well as incorporating additional data sources such as the weight and composition of the trash to improve the system's accuracy.
In conclusion, the project shows the potential of using computer vision and machine learning to automate the process of sorting trash based on its recyclability status. The system is able to achieve a high level of accuracy, and has the potential to improve waste management and reduce the amount of garbage that ends up in landfills.