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ekaanshkhosla / repository
This repository features a collection of small Data Science and Machine Learning projects across domains like NLP, Computer Vision, Time-Series Forecasting, and Transfer Learning. Each project includes code and Jupyter Notebooks for easy exploration.
This repository contains a curated collection of small-scale Data Science and Machine Learning projects. These projects span across domains like Natural Language Processing (NLP), Computer Vision, Time-Series Forecasting, and Transfer Learning. Each project is organized into its own folder with code, datasets (if applicable), and Jupyter Notebooks for ease of understanding and execution.
Amazon-webscrapping — Extracts data from Amazon product listings using web scraping techniques.CNN_using_Keras_Model — Image classification using Convolutional Neural Networks built with Keras.Chat-price-prediction — Predicting product prices based on chat or structured product data.Chat_Bot_with_PyTorch — A simple chatbot implemented using PyTorch and NLP methods.Churn_Risk_Rate_HackerEarthXML — Customer churn risk analysis with data from HackerEarth.Does_Employee_wants_to_stay_or_not_Data_Analytics — Employee retention prediction using HR analytics.Fake-news-classifier-using-LSTM — LSTM-based deep learning model to detect fake news articles.Fake-news-classifier — Basic machine learning approach to classify fake vs real news.Forecasting_fruits_and_vegetable_prices — Time series forecasting of produce prices using regression models.Hate-speech-detection-on-twitter-data — Classifies tweets as hate speech, offensive, or neutral.Low_Light_Image_Enhancement_with_Keras_Model — Enhances low-light images using deep learning techniques in Keras.Predict-Stock-Price-Movement-Based-On-News — NLP-based model to predict stock market movement from headlines.Predict_the_cost_to_ship_the_sculptures — Predicts shipping cost based on size/weight/art properties.Predicting_disease_on_tomato_plant_using_Pytorch — Uses CNN and PyTorch to detect diseases in tomato plant leaves.Spam-Detector-for-Short-Message-Service-SMS — Classifies SMS messages as spam or ham using NLP models.Spam-classifier — A simple spam detection model for emails or text using traditional ML.Speech_to_text_conversion — Converts speech audio into text using Python speech recognition libraries.Tomato_disease_prediction — CNN-based image classifier to detect tomato plant diseases.Using-Transfer-learning-for-predicting-disease-on-plant — Uses pretrained models (e.g., VGG16, ResNet) for plant disease detection.Using-Transfer-learning-for-predicting-hand-gesture — Gesture classification using transfer learning from pretrained models.Will-the-employee-stay-or-leave — Predicts employee attrition using classification techniques on HR data.hate-speech-and-offensive-language-detection — Classifies toxic, offensive, or hate content in textual data using NLP pipelines.Selected from shared topics, language and repository description—not editorial ratings.
sakha1370 /
This repository features my coursework from the IBM Generative AI Engineering Professional Certificate on Coursera. It includes Jupyter Notebooks with code, explanations, and visualizations, along with HTML/PDF summaries. The content highlights my skills in Generative AI, LLMs, NLP, and model optimization.
Project Overview Welcome to the Convolutional Neural Networks (CNN) project in the AI Nanodegree! In this project, you will learn how to build a pipeline that can be used within a web or mobile app to process real-world, user-supplied images. Given an image of a dog, your algorithm will identify an estimate of the canine’s breed. If supplied an image of a human, the code will identify the resembling dog breed. Sample Output Along with exploring state-of-the-art CNN models for classification, you will make important design decisions about the user experience for your app. Our goal is that by completing this lab, you understand the challenges involved in piecing together a series of models designed to perform various tasks in a data processing pipeline. Each model has its strengths and weaknesses, and engineering a real-world application often involves solving many problems without a perfect answer. Your imperfect solution will nonetheless create a fun user experience! Project Instructions Instructions Clone the repository and navigate to the downloaded folder. git clone https://github.com/udacity/dog-project.git cd dog-project Download the dog dataset. Unzip the folder and place it in the repo, at location path/to/dog-project/dogImages. Download the human dataset. Unzip the folder and place it in the repo, at location path/to/dog-project/lfw. If you are using a Windows machine, you are encouraged to use 7zip to extract the folder. Download the VGG-16 bottleneck features for the dog dataset. Place it in the repo, at location path/to/dog-project/bottleneck_features. (Optional) If you plan to install TensorFlow with GPU support on your local machine, follow the guide to install the necessary NVIDIA software on your system. If you are using an EC2 GPU instance, you can skip this step. (Optional) If you are running the project on your local machine (and not using AWS), create (and activate) a new environment. Linux (to install with GPU support, change requirements/dog-linux.yml to requirements/dog-linux-gpu.yml): conda env create -f requirements/dog-linux.yml source activate dog-project Mac (to install with GPU support, change requirements/dog-mac.yml to requirements/dog-mac-gpu.yml): conda env create -f requirements/dog-mac.yml source activate dog-project NOTE: Some Mac users may need to install a different version of OpenCV conda install --channel https://conda.anaconda.org/menpo opencv3 Windows (to install with GPU support, change requirements/dog-windows.yml to requirements/dog-windows-gpu.yml): conda env create -f requirements/dog-windows.yml activate dog-project (Optional) If you are running the project on your local machine (and not using AWS) and Step 6 throws errors, try this alternative step to create your environment. Linux or Mac (to install with GPU support, change requirements/requirements.txt to requirements/requirements-gpu.txt): conda create --name dog-project python=3.5 source activate dog-project pip install -r requirements/requirements.txt NOTE: Some Mac users may need to install a different version of OpenCV conda install --channel https://conda.anaconda.org/menpo opencv3 Windows (to install with GPU support, change requirements/requirements.txt to requirements/requirements-gpu.txt): conda create --name dog-project python=3.5 activate dog-project pip install -r requirements/requirements.txt (Optional) If you are using AWS, install Tensorflow. sudo python3 -m pip install -r requirements/requirements-gpu.txt Switch Keras backend to TensorFlow. Linux or Mac: KERAS_BACKEND=tensorflow python -c "from keras import backend" Windows: set KERAS_BACKEND=tensorflow python -c "from keras import backend" (Optional) If you are running the project on your local machine (and not using AWS), create an IPython kernel for the dog-project environment. python -m ipykernel install --user --name dog-project --display-name "dog-project" Open the notebook. jupyter notebook dog_app.ipynb (Optional) If you are running the project on your local machine (and not using AWS), before running code, change the kernel to match the dog-project environment by using the drop-down menu (Kernel > Change kernel > dog-project). Then, follow the instructions in the notebook. NOTE: While some code has already been implemented to get you started, you will need to implement additional functionality to successfully answer all of the questions included in the notebook. Unless requested, do not modify code that has already been included. Evaluation Your project will be reviewed by a Udacity reviewer against the CNN project rubric. Review this rubric thoroughly, and self-evaluate your project before submission. All criteria found in the rubric must meet specifications for you to pass. Project Submission When you are ready to submit your project, collect the following files and compress them into a single archive for upload: The dog_app.ipynb file with fully functional code, all code cells executed and displaying output, and all questions answered. An HTML or PDF export of the project notebook with the name report.html or report.pdf. Any additional images used for the project that were not supplied to you for the project. Please do not include the project data sets in the dogImages/ or lfw/ folders. Likewise, please do not include the bottleneck_features/ folder.
marcgarnica13 /
Understanding gender differences in professional European football through Machine Learning interpretability and match actions data. This repository contains the full data pipeline implemented for the study *Understanding gender differences in professional European football through Machine Learning interpretability and match actions data*. We evaluated European male, and female football players' main differential features in-match actions data under the assumption of finding significant differences and established patterns between genders. A methodology for unbiased feature extraction and objective analysis is presented based on data integration and machine learning explainability algorithms. Female (1511) and male (2700) data points were collected from event data categorized by game period and player position. Each data point included the main tactical variables supported by research and industry to evaluate and classify football styles and performance. We set up a supervised classification pipeline to predict the gender of each player by looking at their actions in the game. The comparison methodology did not include any qualitative enrichment or subjective analysis to prevent biased data enhancement or gender-related processing. The pipeline had three representative binary classification models; A logic-based Decision Trees, a probabilistic Logistic Regression and a multilevel perceptron Neural Network. Each model tried to draw the differences between male and female data points, and we extracted the results using machine learning explainability methods to understand the underlying mechanics of the models implemented. A good model predicting accuracy was consistent across the different models deployed. ## Installation Install the required python packages ``` pip install -r requirements.txt ``` To handle heterogeneity and performance efficiently, we use PySpark from [Apache Spark](https://spark.apache.org/). PySpark enables an end-user API for Spark jobs. You might want to check how to set up a local or remote Spark cluster in [their documentation](https://spark.apache.org/docs/latest/api/python/index.html). ## Repository structure This repository is organized as follows: - Preprocessed data from the two different data streams is collecting in [the data folder](data/). For the Opta files, it contains the event-based metrics computed from each match of the 2017 Women's Championship and a single file calculating the event-based metrics from the 2016 Men's Championship published [here](https://figshare.com/collections/Soccer_match_event_dataset/4415000/5). Even though we cannot publish the original data source, the two python scripts implemented to homogenize and integrate both data streams into event-based metrics are included in [the data gathering folder](data_gathering/) folder contains the graphical images and media used for the report. - The [data cleaning folder](data_cleaning/) contains descriptor scripts for both data streams and [the final integration](data_cleaning/merger.py) - [Classification](classification/) contains all the Jupyter notebooks for each model present in the experiment as well as some persistent models for testing.
renatocastro33 /
This repository contains the jupyter and mathematica notebooks of the Transfer Learning Model on VGG16, ResNet 50 architecture with ImageNet weights and FaceNet, and also our code proposed of the technique anti spoofing in the paper "Face Liveness Detection Based on Perceptual Image Quality Assessment Features with Multi-scale Analysis". Our project is about a Transfer Learning CNN on VGG16 architecture because it has the best results with an accuracy of 93,016 % in CASIA dataset, 97,321 % MDP dataset and 91,142 % NUAA dataset, we use a "real-time" landmark detection however the transfer learning model and landmark detection don't work together yet (our future work is that these two technologies can work together to reduce errors and improve the accuracy). This also contains the full paper of the "Anti Spoofing Face Detection Technique based on Transfer Learning Convolutional Neural Networks and Real-Time Facial Landmark Detection" to the Latinx in AI - ICML Workshop call.
tgrippa /
This repository contains Python script (Jupyter notebook) for computation of landscape units features (spatial metrics mainly)
mirsadra /
This GitHub repository features a Jupyter Notebook designed for web scraping Google Scholar search results. It automates the extraction of key details about scholarly papers and compiles them into a CSV file.