311 skills found · Page 6 of 11
shreykshah / Google FoobarProblem statements and code from Google Foobar
sidhantagar / ConnectXThis is a project extending the solution to the kaggle-connectx problem statement. Here I have made the frontend UI for the same and added some new features.
calico-team / Calico Fa22All contest materials for CALICO Fall '22, including solutions, editorials, tests, templates, and problem statements.
calico-team / Calico Sp24All contest materials for CALICO Spring '24, including problem statements, templates, tests, solutions, scripts, and editorials.
prasoonsoni / ResearcHaven SIH FRONTENDProblem Statement By UGC - Development of system to avoid duplicity of Research Projects submitted to various funding agencies
shreyaslarer / Real World Project Ideas A curated list of real-world project ideas with clear problem statements, recommended tech stacks, and build guides. Organized by domains like AI, Web, IoT, Blockchain, and more—perfect for students, developers, and job seekers to create portfolio-ready, industry-relevant projects.
Shubh2-0 / Core JAVAThis repository contains three projects developed in Core Java to solve basic problem statements. The projects focus on different aspects of Core Java, including console printing, multiple-choice questions, and simple interactive problems.
allanjg89 / Taxi Trajectory SmoothingProblem Statement: This dataset contains the trajectories of thousands of taxis operating in China. Your task is to read through the following paper and produce the first graphs (distribution of distances and sampling time interval). Next, please pick a trajectory for a particular trip and determine its smoothed trajectory (using Kalman filter for example or splines)
newking9088 / MITx 6.431x Probability The Science Of Uncertainty And DataA guide on how to use the wealth of available material This class provides you with a great wealth of material, perhaps more than you can fully digest. This “guide" offers some tips about how to use this material. Start with the overview of a unit, when available. This will help you get an overview of what is to happen next. Similarly, at the end of a unit, watch the unit summary to consolidate your understanding of the “big picture" and of the relation between different concepts. Watch the lecture videos. You may want to download the slides (clean or annotated) at the beginning of each lecture, especially if you cannot receive high-quality streaming video. Some of the lecture clips proceed at a moderate speed. Whenever you feel comfortable, you may want to speed up the video and run it faster, at 1.5x. Do the exercises! The exercises that follow most of the lecture clips are a most critical part of this class. Some of the exercises are simple adaptations of you may have just heard. Other exercises will require more thought. Do your best to solve them right after each clip — do not defer this for later – so that you can consolidate your understanding. After your attempt, whether successful or not, do look at the solutions, which you will be able to see as soon as you submit your own answers. Solved problems and additional materials. In most of the units, we are providing you with many problems that are solved by members of our staff. We provide both video clips and written solutions. Depending on your learning style, you may pick and choose which format to focus on. But in either case, it is important that you get exposed to a large number of problems. The textbook. If you have access to the textbook, you can find more precise statements of what was discussed in lecture, additional facts, as well as several examples. While the textbook is recommended, the materials provided by this course are self-contained. See the “Textbook information" tab in Unit 0 for more details. Problem sets. One can really master the subject only by solving problems – a large number of them. Some of the problems will be straightforward applications of what you have learned. A few of them will be more challenging. Do not despair if you cannot solve a problem – no one is expected to do everything perfectly. However, once the problem set solutions are released (which will happen on the due date of the problem set), make sure to go over the solutions to those problems that you could not solve correctly. Exams. The midterm exams are designed so that in an on-campus version, learners would be given two hours. The final exam is designed so that in an on-campus version, learners would be given three hours. You should not expect to spend much more than this amount of time on them. In this respect, those weeks that have exams (and no problem sets!) will not have higher demands on your time. The level of difficulty of exam questions will be somewhere between the lecture exercises and homework problems. Time management. The corresponding on-campus class is designed so that students with appropriate prerequisites spend about 12 hours each week on lectures, recitations, readings, and homework. You should expect a comparable effort, or more if you need to catch up on background material. In a typical week, there will be 2 hours of lecture clips, but it might take you 4-5 hours when you add the time spent on exercises. Plan to spend another 3-4 hours watching solved problems and additional materials, and on textbook readings. Finally, expect about 4 hours spent on the weekly problem sets. Additional practice problems. For those of you who wish to dive even deeper into the subject, you can find a good collection of problems at the end of each chapter of the print edition of the book, whose solutions are available online.
Adedolapo-Oguntayo / Basic Needs Basic Rights Kenya Tech4Mental HealthAround 1 in 4 people will experience a mental health problem this year. Low-income countries have an estimated treatment gap of 85% (as compared with high-income countries with a gap of 35% to 50%). While Kenya has a mental illness prevalence rate that is comparable to that of high-income countries, there are still less than 500 healthcare professionals serving the country. In Kenya, there are growing concerns about mental health among young people, particularly university students that face a challenging and unique conflation of stressors that put them at risk of challenges like depression and substance abuse. From the use of app-based solutions for screening to electronically delivered therapies, the use of technologies including machine learning and AI will potentially transform the delivery of mental health services in the coming years. The objective of this challenge is to develop a machine learning model that classifies statements and questions expressed by university students in Kenya when speaking about the mental health challenges they struggle with. The four categories are depression, suicide, alchoholism, and drug abuse. This solution will be used for a prototype of a mental health chatbot designed specifically for university students. This initiative is a first step in leveraging technology to make mental health services more accessible and more user-friendly for young people in Kenya and around the world
jstjyoti / Smart India Hackathon Team DigIndia Smart India Hackathon 2018 Identification of meritorious students in primary education Problem Statement:- Gujarat government has nearly 90 lac students studying in primary education across state. They are in different cities and villages across state. There is no mechanism to identify bright students who are performing well in study, sports or other activities. Web portal can be designed to acquire date about such students and can be analyzed on different parameters. What Exact Problem is being solved? : Such identified students can be provided with extra resources or special attention can be given to their upbringing. Abstract To identify meritorious students firstly all the educational institutions need to upload the results of students as well as points of extra curriculum activity (activity name, score out of 10 for performance) to the database for a student according to the current class of study. Aadhar number for all the students will always be given (from there students details will be verified).A parent or any other nongovernment institute can also upload scanned copy of result or certificate of any student with his/her Aadhar number and their own details. Admin will Cross-check and verify it for the update in the database. One’s (schools and institutions) first login or registration, there will be a unique token, (user id and password) to the Portal. That login will be further verified. So every institution will have a unique user id and password and students' details will be uploaded yearly and updates will be done twice in a year. The second fold of the solution is to sort the data according to the merit of students. The designed application will perform the operation with the provided data and present a lesser (according to requirement) students' details. There should be some methods (a faster and optimal Algorithm to sort data by marks and activity score from database Base will be adopted i.e., any tree type-level representation) to sort the data (details) of meritorious students from provided records of all the students. The third and final part is providing the list of meritorious students to the education department and university. Each official and university will also have a login section. The list of meritorious students will be provided according to year, required field. The education department or university can also post the facilities provided to the selected and shortlisted student as a notice. Therefore, we are going to solve the stated problem by providing a Web-based application comprising of Web portal and secured database to identify meritorious students in primary education according to data (100%) uploaded and retrieved from several institutions and selected meritorious students list will be provided to (according to specification of different facilities 20-30%) to Education Department and Universities. Keywords: Aadhar Number as Primary key of Student Table. Online WEB-portal. Update Records every year to keep a check on the improvement, Standardization & Soring data based on Z – stat to filter out the meritorious students on the basis of acads and extra-curricular activities. Tree type-level representation of Database i.e. Admin – Institute – Student. Use Case :- Choice Based selection of meritorious student from data set. For instances if the requirement is only limited to academics, they can refer to the website to fetch a list of top scorers say top 100 or top 200 students. Again if the requirement is limited to selection of Extra-curricular activity like – singing, painting, dancing etc they can fetch the list of students having expertise in that particular field only. Identification of poor meritorious students and Funding based support from different NGO’s, organizations and donations if they want to provide. Supervising data based on entries done in every year (Region based) to keep a check on the individual growth of a student. For instances, a diligent student say X has been receiving scholarship every year now say that X student’s data has not been registered in Database in the next year. Thus there is a decay of GDP in the sample space. To highlight the social issues such as Child Labour, child trafficking, by year wise regulation Data. To prevent the girl child marriage on the basis of Dataset by the investigation Team. For instance if a girl found not registering in the consecutive Year, an investigation team can take action accordingly. Special Features: The school should submit their data to get a recognition as well as to be in sight of fund providing parties (governmental or non-governmental). Students will be benefited as direct communication is in between officials and student and no middle man in between. • Data analysis will be the key point to identification using assignment of z-marks by standard normal distribution. Technology Stack: We are to make a Web-based app, in a microlithic structure format, where the app structure is broken into different fragments, which does the different job. One part will be taking in the to the database from a web portal designed using CSS, JavaScript, PHP, and Servlet. Computation of the sorted data and the various mathematical calculations i.e. arranging the sorted data according to given criteria etc on a mathematical platform powered by JAVA. Another part will be integrated with the API's of various Education Department and Universities to provide them up with shortlisted meritorious students, integrating with their personal choices and cut-offs, and also where shortlisted students will be notified by notice posted. Keeping in mind the ease of obtaining marks and details which has increased throughout the years. In the web app, after one's first login or registration, each part of the education department, university and institution have a unique token, (user id and password) to the database. Coming to the part of its database, My SQL or Oracle or Mongo DB can be used with a firmed dashboard powered by python or JavaScript on a network frame. Since the app will be containing huge academic details of many students, so a strong encryption algorithm is to be used for data integrity and data security. AES-256 or MD5 would be best to use to protect the data in the database and for authentication Biometric data will also be preserved.
gdgcgbpant-dotcom / Problem Statements Praxis 2 0Official Problem Statement of Praxis-2.0
PranayKr / Smart Meter Data AnalyticsA Predictive Analytics Problem Statement to forecast future Electricity Consumption using Household Power Consumption Dataset
saif86 / UML CLASS DIAGRAMS EXAMPLESConverting a problem statement into class diagram.
antonio-ramadas / Aoc To Markdown🎅 Parses Advent Of Code problem statement to markdown with option to also download the input while keeping everything organised.
salma2vec / ML Beginner PortfolioKickstart ML through these 20+ foundational projects; Kaggle datasets, problem statements and comprehensive EDA (Exploratory Data Analysis) walkthroughs.
riju951 / AI Traffic SystemProblem Statement: Altering traffic light timings based on the congestion of traffic per lane using OpenCV and Neural Networks.
saikumargadde2807 / HDLBITS SolutionsThis is a repository containing solutions to the problem statements given in HDL Bits website.
RoyGilad / Dreamforce 2019 Do More Within Salesforce Governor Limits Using Platform EventsDreamforce Presentation: Mixed DMLS Operations, Too Many SOQL Queries, Too Many DML Statements, CPU Timeout: Salesforce's Governor limits are there for a reason but even when you employ best practices you may still exceed them. A good developer will look at all tools available on the platform and find the best approach to solving the problem they are facing. Join us to add the newest tool to your developer toolbelt. Use Platform Events to change the rules of the game, process more, and faster within governor limits.
rohitk140797k / E Commerce Machine Learning And NLP Techniques Used Problem Statement Amazon is an online shopping website that now caters to millions of people everywhere. Over 34,000 consumer reviews for Amazon brand products like Kindle, Fire TV Stick and more are provided. The dataset has attributes like brand, categories, primary categories, reviews.title, reviews.text, and the sentiment. Sentiment is a categorical variable with three levels "Positive", "Negative“, and "Neutral". For a given unseen data, the sentiment needs to be predicted. You are required to predict Sentiment or Satisfaction of a purchase based on multiple features and review text. picture Dataset Snapshot picture Project Task: Week 1 Class Imbalance Problem: Perform an EDA on the dataset. a) See what a positive, negative, and neutral review looks like. b) Check the class count for each class. It’s a class imbalance problem. Convert the reviews in Tf-Idf score. Run multinomial Naive Bayes classifier. Everything will be classified as positive because of the class imbalance. Project Task: Week 2 Tackling Class Imbalance Problem: Oversampling or undersampling can be used to tackle the class imbalance problem. In case of class imbalance criteria, use the following metrices for evaluating model performance: precision, recall, F1-score, AUC-ROC curve. Use F1-Score as the evaluation criteria for this project. Use Tree-based classifiers like Random Forest and XGBoost. Note: Tree-based classifiers work on two ideologies namely, Bagging or Boosting and have fine-tuning parameter which takes care of the imbalanced class. Project Task: Week 3 Model Selection: Apply multi-class SVM’s and neural nets. Use possible ensemble techniques like: XGboost + oversampled_multinomial_NB. Assign a score to the sentence sentiment (engineer a feature called sentiment score). Use this engineered feature in the model and check for improvements. Draw insights on the same. Project Task: Week 4 Applying LSTM: Use LSTM for the previous problem (use parameters of LSTM like top-word, embedding-length, Dropout, epochs, number of layers, etc.) Hint: Another variation of LSTM, GRU (Gated Recurrent Units) can be tried as well. Compare the accuracy of neural nets with traditional ML based algorithms. Find the best setting of LSTM (Neural Net) and GRU that can best classify the reviews as positive, negative, and neutral. Hint: Use techniques like Grid Search, Cross-Validation and Random Search Optional Tasks: Week 4 Topic Modelling: Cluster similar reviews. Note: Some reviews may talk about the device as a gift-option. Other reviews may be about product looks and some may highlight about its battery and performance. Try naming the clusters. Perform Topic Modelling Hint: Use scikit-learn provided Latent Dirchlette Allocation (LDA) and Non-Negative Matrix Factorization (NMF).
