339 skills found · Page 12 of 12
vgno / Data ShaperUtility for building meaningful responses from normalized data using shapes
Shubhamghude808 / Sales PredictionThe project encompasses a multifaceted approach to analyze and predict purchase prices through rigorous data manipulation, visualization, normalization, and advanced modeling techniques.
lab-rasool / SeNMoA Self-Normalizing DL Model for Enhanced Multi-Omics Data Analysis in Oncology
FullStackCraft / FloeFast, type-safe options analytics library for TypeScript. Black-Scholes pricing, Greeks (delta, gamma, vanna, charm), and dealer exposure metrics with broker-agnostic data normalization.
dyxstat / HiCzinHiCzin: Normalizing metagenomic Hi-C data and detecting spurious contacts using zero-inflated negative binomial regression
danielgamage / Data Lathea set of utility functions for remapping and reshaping data (esp normalized), inspired by DSP and shader development
amandesai01 / DoubtGuessThis Project, on running, returns all the information of all teachers by scraping it from kjsce.somaiya.edu and applying various filtering algorithms to normalize chaotic data in JSON format in given manner.
augustsemrau / Voice Conversion Project StarGAN DanspeechUsing 'StarGAN-VC' voice conversion method as a means of normalizing input speech data for 'danspeech' speech-to-text system.
S-Sharvesh / Stock Price PredictionImplemented and trained XGBoost, LSTM, and WGAN-GP models for stock price forecasting, achieving robust predictive performance. Developed data preprocessing pipelines with normalization, splitting, and Fourier transforms, enhancing accuracy and efficiency. Optimized hyperparameters and visualized predictions using RMSE.
Aronno1920 / Digit Classifier MNISTBuild and evaluate a deep learning model that classifies handwritten digits (0–9) using the MNIST dataset. This project will reinforce core deep learning concepts such as data preprocessing, batch normalization, dropout regularization, and model evaluation through visual metrics.
debbrath / Digit Classifier MNISTBuild and evaluate a deep learning model that classifies handwritten digits (0–9) using the MNIST dataset. This project will reinforce core deep learning concepts such as data preprocessing, batch normalization, dropout regularization, and model evaluation through visual metrics.
mobileappsvn / EmojicounterCounting Emoji number, Java Counting Emoji number, Android Counting Emoji number, Extract all Emoji, SubString with data content contain Emoji, Android counting Emoji, Check String is Emoji character, Android Emoji counter, Emoji Utils, Java Emoji counter, SubString with fullsize (shift_JIS) Japanese font using the Normalize class, easy to cut string (subString) with shift-JIS,shift_JIS font type
hanegeyavuz / Extended Kalman FilterThis project processes IMU data to estimate the orientation of a device using an Extended Kalman Filter. The data is read from a text file, normalized, and then used to compute orientation in the form of Euler angles, which are subsequently visualized using MATLAB plots.
ilgwonPark2 / SystemAnalysisDesignTerm Project repository for System Analysis and Design course in ITM, Seoultech.
altunenes / Asap RsZero-dependency Rust implementation of ASAP (Automatic Smoothing for Attention Prioritization) for Time Series
SNEHAGOLATKAR / SAHAI Supervisory Aid For Health In Aged Using Integrated SystemsApproximately 28-35% of elderly people fall one time or more per year. They tend to leave the appliances unattended due to forgetfulness. Thus there is a need to provide help to elderly people as well as provide necessary information to their loved ones. The elderly people living alone needs an automatic integrated solution as a support which would provide homely assistance by taking care of them. The project is based on kinect device which does not invade their privacy while monitoring multiple elderly people's normal activities and detecting fall or catastrophic conditions by measuring their gait and analyzing change in posture when they change from sitting to walking or vice versa. Support vector machine is used to classify the gait and posture data obtained from the kinect device. Normalization technique is used to identify multiple users and activities. With the help of machine learning algorithm normal and abnormal activities are identified. The proposed system also informs concerned person in the case of any emergency. Monitoring the status of usage of electronic appliances is also considered
klimanyusuf / Combating Twitter Hate Speech Using ML And NLPUsing NLP and ML, make a model to identify hate speech (racist or sexist tweets) in Twitter. Problem Statement: Twitter is the biggest platform where anybody and everybody can have their views heard. Some of these voices spread hate and negativity. Twitter is wary of its platform being used as a medium to spread hate. You are a data scientist at Twitter, and you will help Twitter in identifying the tweets with hate speech and removing them from the platform. You will use NLP techniques, perform specific cleanup for tweets data, and make a robust model. Domain: Social Media Analysis to be done: Clean up tweets and build a classification model by using NLP techniques, cleanup specific for tweets data, regularization and hyperparameter tuning using stratified k-fold and cross validation to get the best model. Content: id: identifier number of the tweet Label: 0 (non-hate) /1 (hate) Tweet: the text in the tweet Tasks: Load the tweets file using read_csv function from Pandas package. Get the tweets into a list for easy text cleanup and manipulation. To cleanup: Normalize the casing. Using regular expressions, remove user handles. These begin with '@’. Using regular expressions, remove URLs. Using TweetTokenizer from NLTK, tokenize the tweets into individual terms. Remove stop words. Remove redundant terms like ‘amp’, ‘rt’, etc. Remove ‘#’ symbols from the tweet while retaining the term. Extra cleanup by removing terms with a length of 1. Check out the top terms in the tweets: First, get all the tokenized terms into one large list. Use the counter and find the 10 most common terms. Data formatting for predictive modeling: Join the tokens back to form strings. This will be required for the vectorizers. Assign x and y. Perform train_test_split using sklearn. We’ll use TF-IDF values for the terms as a feature to get into a vector space model. Import TF-IDF vectorizer from sklearn. Instantiate with a maximum of 5000 terms in your vocabulary. Fit and apply on the train set. Apply on the test set. Model building: Ordinary Logistic Regression Instantiate Logistic Regression from sklearn with default parameters. Fit into the train data. Make predictions for the train and the test set. Model evaluation: Accuracy, recall, and f_1 score. Report the accuracy on the train set. Report the recall on the train set: decent, high, or low. Get the f1 score on the train set. Looks like you need to adjust the class imbalance, as the model seems to focus on the 0s. Adjust the appropriate class in the LogisticRegression model. Train again with the adjustment and evaluate. Train the model on the train set. Evaluate the predictions on the train set: accuracy, recall, and f_1 score. Regularization and Hyperparameter tuning: Import GridSearch and StratifiedKFold because of class imbalance. Provide the parameter grid to choose for ‘C’ and ‘penalty’ parameters. Use a balanced class weight while instantiating the logistic regression. Find the parameters with the best recall in cross validation. Choose ‘recall’ as the metric for scoring. Choose stratified 4 fold cross validation scheme. Fit into the train set. What are the best parameters? Predict and evaluate using the best estimator. Use the best estimator from the grid search to make predictions on the test set. What is the recall on the test set for the toxic comments? What is the f_1 score?
DrSkippy / GnacsGnip normalized social data activities json to csv parser.
RenneLab / CnR FlowCUT&RUN-Flow, A Nextflow pipeline for QC, tag trimming, normalization, and peak calling for data from CUT&RUN experiments.
Alex-Gilbert / IVS3Application for viewing 4D Data sets using vector normalization and stereo-graphic projection.
