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Select a supervised algorithm that can predict stock prices of historical data based on the predictors (statistical indicators). Accordingly formulate a trading strategy based on predicted values to generate orders on same historical training set to backtest how much portfolio would have increased. Select the combination of Machine learning algorithm and Trading strategy to maximize gain for future orders placed automatically via the program.

Front-end demonstration of Formulate, a dynamic data collection and manipulation system.

Global Climate Statistical Analysis Library (GCSAL) allows viewing of climate statistics formulated from over 60 years of data acquisition at 3000 locations around the world.

lustering in Machine Learning Introduction to Clustering It is basically a type of unsupervised learning method . An unsupervised learning method is a method in which we draw references from datasets consisting of input data without labelled responses. Generally, it is used as a process to find meaningful structure, explanatory underlying processes, generative features, and groupings inherent in a set of examples. Clustering is the task of dividing the population or data points into a number of groups such that data points in the same groups are more similar to other data points in the same group and dissimilar to the data points in other groups. It is basically a collection of objects on the basis of similarity and dissimilarity between them. For ex– The data points in the graph below clustered together can be classified into one single group. We can distinguish the clusters, and we can identify that there are 3 clusters in the below picture. It is not necessary for clusters to be a spherical. Such as : DBSCAN: Density-based Spatial Clustering of Applications with Noise These data points are clustered by using the basic concept that the data point lies within the given constraint from the cluster centre. Various distance methods and techniques are used for calculation of the outliers. Why Clustering ? Clustering is very much important as it determines the intrinsic grouping among the unlabeled data present. There are no criteria for a good clustering. It depends on the user, what is the criteria they may use which satisfy their need. For instance, we could be interested in finding representatives for homogeneous groups (data reduction), in finding “natural clusters” and describe their unknown properties (“natural” data types), in finding useful and suitable groupings (“useful” data classes) or in finding unusual data objects (outlier detection). This algorithm must make some assumptions which constitute the similarity of points and each assumption make different and equally valid clusters. Clustering Methods : Density-Based Methods : These methods consider the clusters as the dense region having some similarity and different from the lower dense region of the space. These methods have good accuracy and ability to merge two clusters.Example DBSCAN (Density-Based Spatial Clustering of Applications with Noise) , OPTICS (Ordering Points to Identify Clustering Structure) etc. Hierarchical Based Methods : The clusters formed in this method forms a tree-type structure based on the hierarchy. New clusters are formed using the previously formed one. It is divided into two category Agglomerative (bottom up approach) Divisive (top down approach) examples CURE (Clustering Using Representatives), BIRCH (Balanced Iterative Reducing Clustering and using Hierarchies) etc. Partitioning Methods : These methods partition the objects into k clusters and each partition forms one cluster. This method is used to optimize an objective criterion similarity function such as when the distance is a major parameter example K-means, CLARANS (Clustering Large Applications based upon Randomized Search) etc. Grid-based Methods : In this method the data space is formulated into a finite number of cells that form a grid-like structure. All the clustering operation done on these grids are fast and independent of the number of data objects example STING (Statistical Information Grid), wave cluster, CLIQUE (CLustering In Quest) etc. Clustering Algorithms : K-means clustering algorithm – It is the simplest unsupervised learning algorithm that solves clustering problem.K-means algorithm partition n observations into k clusters where each observation belongs to the cluster with the nearest mean serving as a prototype of the cluster . Applications of Clustering in different fields Marketing : It can be used to characterize & discover customer segments for marketing purposes. Biology : It can be used for classification among different species of plants and animals. Libraries : It is used in clustering different books on the basis of topics and information. Insurance : It is used to acknowledge the customers, their policies and identifying the frauds. City Planning: It is used to make groups of houses and to study their values based on their geographical locations and other factors present. Earthquake studies: By learning the earthquake-affected areas we can determine the dangerous zones. References : Wiki Hierarchical clustering Ijarcs matteucc analyticsvidhya knowm

Privacy Policy introduction We understand the importance of personal information to you and will do our utmost to protect your personal information. We are committed to maintaining your trust in us and to abide by the following principles to protect your personal information: the principle of consistency of rights and responsibilities, the principle of purpose , choose the principle of consent, at least the principle of sufficient use, ensure the principle of security, the principle of subject participation, the principle of openness and transparency, and so on. At the same time, we promise that we will take appropriate security measures to protect your personal information according to the industry's mature security solutions. In view of this, we have formulated this "Private Privacy Policy" (hereinafter referred to as "this policy" /This Privacy Policy") and remind you: This policy applies to products or services on this platform. If the products or services provided by the platform are used in the products or services of our affiliates (for example, using the platform account directly) but there is no independent privacy policy, this policy also applies to the products or services. It is important to note that this policy does not apply to other third-party services provided by you, nor to products or services on this platform that have been independently set up with a privacy policy. Before using the products or services on this platform, please read and understand this policy carefully, and use the related products or services after confirming that you fully understand and agree. By using the products or services on this platform, you understand and agree to this policy. If you have any questions, comments or suggestions about the content of this policy, you can contact us through various contact methods provided by this platform. This privacy policy section will help you understand the following: How we collect and use your personal information How do we use cookies and similar technologies? How do we share, transfer, and publicly disclose your personal information? How we protect your personal information How do you manage your personal information? How do we deal with the personal information of minors? How your personal information is transferred globally How to update this privacy policy How to contact us 一、How we collect and use your personal information Personal information refers to various information recorded electronically or otherwise that can identify a specific natural person or reflect the activities of a particular natural person, either alone or in combination with other information. We collect and use your information for the purposes described in this policy. 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To improve the security of your services provided by us and our affiliates and partners, protect the personal and property of you or other users or the public from being compromised, and better prevent phishing websites, fraud, network vulnerabilities, computer viruses, cyber attacks , security risks such as network intrusion, more accurately identify violations of laws and regulations or the relevant rules of the platform, we may use or integrate your user information, transaction information, equipment information, related web logs and our affiliates, partners to obtain You authorize or rely on the information shared by law to comprehensively judge your account and transaction risks, conduct identity verification, detect and prevent security incidents, and take necessary records, audits, analysis, and disposal measures in accordance with the law. （五）Other uses When we use the information for other purposes not covered by this policy, or if the information collected for a specific purpose is used for other purposes, you will be asked for your prior consent. （六）Exception for authorization of consent According to relevant laws and regulations, collecting your personal information in the following situations does not require your authorized consent: 1、Related to national security and national defense security； 2、Related to public safety, public health, and major public interests； 3、Related to criminal investigation, prosecution, trial and execution of judgments, etc.； 4、It is difficult to obtain your own consent for the maintenance of the important legal rights of the personal information or other individuals’ lives and property； 5、The personal information collected is disclosed to the public by yourself; 二、How do we use cookies and similar technologies? （一）Cookies To ensure that your site is up and running, to give you an easier access experience, and to recommend content that may be of interest to you, we store a small data file called a cookie on your computer or mobile device. 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(2) Transfer We do not transfer your personal information to any company, organization or individual, except: Transfer with the express consent: After obtaining your explicit consent, we will transfer your personal information to other parties; 2, in the case of mergers, acquisitions or bankruptcy liquidation, or other circumstances involving mergers, acquisitions or bankruptcy liquidation, if it involves the transfer of personal information, we will require new companies and organizations that hold your personal information to continue to receive This policy is bound, otherwise we will ask the company, organization and individual to re-seek your consent. (3) Public disclosure We will only publicly disclose your personal information in the following circumstances: We may publicly disclose your personal information by obtaining your explicit consent or based on your active choice; 2, if we determine that you have violated laws and regulations or serious violations of the relevant rules of the platform, or to protect the personal safety of the platform and its affiliates users or the public from infringement, we may be based on laws and regulations or The relevant agreement rules of this platform disclose your personal information, including related violations, and the measures that the platform has taken against you, with your consent. (4) Exceptions for prior authorization of consent when sharing, transferring, and publicly disclosing personal information In the following situations, sharing, transferring, and publicly disclosing your personal information does not require prior authorization from you: Related to national security and national defense security; Related to public safety, public health, and major public interests; 3, related to criminal investigation, prosecution, trial and judgment execution; 4, in order to protect your or other individuals' life, property and other important legal rights but it is difficult to get my consent; Personal information that you disclose to the public on your own; Collect personal information from legally publicly disclosed information, such as legal news reports and government information disclosure. According to the law, sharing, transferring and de-identifying personal information, and ensuring that the data recipient cannot recover and re-identify the personal information subject, does not belong to the external sharing, transfer and public disclosure of personal information. The preservation and processing of the class data will not require additional notice and your consent. How do we protect your personal information? (1) We have taken reasonable and feasible security measures in accordance with the industry's general solutions to protect the security of personal information provided by you, and to prevent unauthorized access, public disclosure, use, modification, damage or loss of personal information. For example, SSL (Secure Socket) when exchanging data (such as credit card information) between your browser and the server Layer) protocol encryption protection; we use encryption technology to improve the security of personal information; we use a trusted protection mechanism to prevent personal information from being maliciously attacked; we will deploy access control mechanisms to ensure that only authorized personnel can access individuals Information; and we will conduct security and privacy protection training courses to enhance employees' awareness of the importance of protecting personal information. (2) We have advanced data security management system around the data life cycle, which enhances the security of the whole system from organizational construction, system design, personnel management, product technology and other aspects. (3) We will take reasonable and feasible measures and try our best to avoid collecting irrelevant personal information. We will only retain your personal information for the period of time required to achieve the purposes stated in this policy, unless the retention period is extended or permitted by law. (4) The Internet is not an absolutely secure environment. We strongly recommend that you do not use personal communication methods that are not recommended by this platform. You can connect and share with each other through our services. When you create communications, transactions, or sharing through our services, you can choose who you want to communicate, trade, or share as a third party who can see your trading content, contact information, exchange information, or share content. If you find that your personal information, especially your account or password, has been leaked, please contact our customer service immediately so that we can take appropriate measures according to your application. Please note that the information you voluntarily share or even share publicly when using our services may involve personal information of you or others or even sensitive personal information, such as when you post a news or choose to upload in public in group chats, circles, etc. A picture containing personal information. Please consider more carefully whether you share or even share information publicly when using our services. Please use complex passwords to help us keep your account secure. We will do our best to protect the security of any information you send us. At the same time, we will report the handling of personal information security incidents in accordance with the requirements of the regulatory authorities. V. How your personal information is transferred globally Personal information collected and generated by us during our operations in the People's Republic of China is stored in China, with the following exceptions: Laws and regulations have clear provisions; 2, get your explicit authorization; 3, you through the Internet for cross-border live broadcast / release dynamics and other personal initiatives. In response to the above, we will ensure that your personal information is adequately protected in accordance with this Privacy Policy.

Deep-Learning-Based Approach to Anomaly Detection Techniques for Large Acoustic Data in Machine Operation.Developed a deep leaning algorithm which detects anomaly in acoustic sensor data with approx. 90% accuracy.  Implemented the different machine/deep learning algorithms like SVM, KNN, K-means, CNN, Delayed LSTM, Conv LSTM and different Beamforming algorithms such as delay and sum beamforming, linear constrained minimum variance beamformer etc. and analyzed their limitations  Formulated the Sound source localization algorithms like MUSIC algorithm (Multiple Signal Classification), TDOA and Steered response and currently working on the optimization of it using GAN-LSTM

Enable stakeholders with an engaging Power BI dashboard illustrating key e-commerce performance indicators and patterns. Evaluate sales data, predict forthcoming trends, and formulate knowledgeable approaches to foster business expansion.

Formulate human-readable queries and retrieve data from ENTSO-E into pandas.DataFrame format.

Given the Live on board data of various drivers, a score corresponding to each driver is to be formulated, which will help insurance companies to rate a Driver.

Demand Forecasting is the process in which historical sales data is used to develop an estimate of an expected forecast of customer demand. To businesses, Demand Forecasting provides an estimate of the amount of goods and services that its customers will purchase in the foreseeable future. Critical business assumptions like turnover, profit margins, cash flow, capital expenditure, risk assessment and mitigation plans, capacity planning, etc. are dependent on Demand Forecasting. Demand Forecasting is the pivotal business process around which strategic and operational plans of a company are devised. Based on the Demand Forecast, strategic and long-range plans of a business like budgeting, financial planning, sales and marketing plans, capacity planning, risk assessment and mitigation plans are formulated. Short to medium term tactical plans like pre-building, make-to-stock, make-to-order, contract manufacturing, supply planning, network balancing, etc. are execution based. Demand Forecasting also facilitates important management activities like decision making, performance evaluation, judicious allocation of resources in a constrained environment and business expansion planning.

Objective: Students can formulate a research question, conduct preliminary data analysis, and prepare data, and select features, build, evaluate, optimize a model using regression

Mobile devices contain highly sensitive data, making them an attractive target to attackers. As an Android malware classifier, LiM aims to tackle security issues while respecting the privacy of users by leveraging the power of federated learning. Compared to centralized ways of learning, the unique properties of federated learning open up new attack surfaces for adversaries. For instance, an adversary can attempt to let a targeted malicious app be misclassified as clean by sending poisoned model updates in the federation. This work builds on LiM with the aim of improving its resistance against these poisoning attacks. First, I formulate and test several targeted model update poisoning attacks. Depending on assumptions regarding the adversary's knowledge, the attacks are able to successfully compromise around 10 to 25\% of the honest client devices in the federation. Second, while most defenses result in a trade-off between improving resistance and maintaining performance, I propose a simple defense strategy that can never decrease the performance of the federation. Against a strong adversary, who has knowledge of the algorithm used to aggregate the model updates, the defense was mostly insufficient to prevent poisoning. In the presence of a more realistic adversary, the defense caused LiM to regain best-case performance, comparable to the performance in a scenario without adversary.

This is a demonstration on how to produce speech in a particular emotion from text, this is achieved by fine tuning a TTS model on emotion labelled speech data, formulating it as a multi-modal problem.

Data Science Project Governance Framework is a framework that can be followed by any new Data Science business or team. It will help in formulating strategies around how to leverage Data Science as a business, how to architect Data Science based solutions and team formation strategy, ROI calculation approaches, typical Data Science project lifecycle components, commonly available Deep Learning toolsets and frameworks and best practices used by Data Scientists. A lot of research is happening all around the world in various domains to leverage Deep Learning, Machine Learning and Data Science based solutions to solve problems that would otherwise be impossible to solve using simple rule based systems. All the major players in the market and businesses are also getting started and setting up new Data Science teams to take advantages of modern State-of-the-Art ML/DL techniques. Even though most of the Data Scientists are great at knowledge of mathematical modeling techniques, they lack the business acumen and management knowledge to drive Data Science based solutions in a corporate/MNC setup. On the other hand, management executives in most of the corporates/MNCs do not have first hand knowledge of setting up new Data Science team and approach to solving business problems using Data Science. This framework intends to help bridge the above mentioned gap and provide Executives and Data Scientists with a common ground around which they can easily build any Data Science business/team from ground zero.

Open Domain Conversational Engine using Azure, Hadoop, SQL, C#, F#. Its designed to be a cloud based api in which you provide a sentence and it formulates a response back about anything. Driven by big data.

I formulated and supervised “Understanding Climate and Weather (UCW)” and “Motion in the Atmosphere (MiA)” student projects (~70 students in ~15 groups) that use long-term climate and sounding data from different stations across the province of British Columbia, Canada to quantify the rate of change of climate at local level with respect to the global change.

To boost the income through ad clicks, it is imperative to understand the significance of the factors affecting ad clicks. After mining through data logs provided by Outbrain,we formulated new learning problem in content ranking based on past clickthrough data to predict which pieces of content (ads) likely to be clicked by global users automatically.We used classification models like Naive Bayes, SVM, Random Forest and Stochastic Gradient Descent algorithms for learning parameterized orderings.

NetDecoder was developed in the Hu Li lab (http://www.hulilab.org) at Mayo Clinic. NetDecoder is a network biology-based computational platform designed to integrate transcriptomes, interactomes and gene ontologies to identify phenotype-specific subnetworks. NetDecoder is based on network flow algorithm and formulated as a minimum-cost flow optimization problem to identify and prioritize paths and key regulators within disease specific subnetworks. NetDecoder is designed to capture molecular switches and infer disease-specific networks to better understand pathways and identify key regulators that contribute to a disease phenotype. NetDecoder has extensive documentation and tutorial with free software package downloadable for the research communities. You can use NetDecoder on-line by uploading your data here, or you can download and run NetDecoder locally on your computer. Please, go to our website http://netdecoder.org to obtain more information about NetDecoder.

Assignment formulated for the students of the Computer Systems for Data Science course at Columbia University.

This package aims to give technical earnings data for stocks to help traders formulate strategies

Downloading real time Bitcoin option data from Deribit API and formulate a quadratic equation to figure out the relationship between Strikes and mark IV

The objective of this study is to formulate and implement a linear Mixed Binary Programming (MBP) model to identify the minimum-cost expansion plan for BestChip to the western US based on the data presented. Besides, the model is used to answer the questions following the problem description.

It is a Business Case Problem Used in Data Science Consulting and Engineering. Data Science always helps us to take important Business Decisions which leads to development of the Organization and also helps to avoid any Disaster by taking any gut-feeling decisions. Here In This Dataset I have Predicted the Behaviours of the Customers through their App Usage to Predict and Formulate different Policies and Rules for Different Set of Customers.

"Today many big organizations are sitting on large chunks of data, not knowing what to do with it. They invite consultants & business analysts to have a look at data and come up with insights that could help the organization run their business better. There is no clear set of instructions in such open-ended problems and it is expected of the consultant to do a lot of exploration first and formulate the problems themselves. These DVT projects fall into the bucket of such open-ended problems and a specific problem statement has not been given intentionally. It is expected of students to explore the data and come up with good insights. There is no right and wrong answer here. There should a clear logical story which should come out of their submission."

Seismic events such as Earthquakes are very under-researched in global scientific domains but, with advancements in Machine Learning, classification and analysis of seismic data is becoming more feasible. This project focuses on developing techniques to effectively classify Seismic data into earthquake events and seismic noise (mining blasts, man-made noise etc.). The project not only focuses on developing deep learning models but, is also focused on collecting seismic data from credible repositories with proper labelling. An extensive research in seismology ensued proper knowledge of Earthquake waves, their propagation and behavior. Main motivation behind this project is to defy already accepted triggering (STA/LTA, Z Transform etc.) as well as Picking (AR picker, baer picker etc.) algorithms and formulate new techniques to extract P and S wave features. Problem with these algorithms is that different threshold values can produce entirely different triggers and accurate thresholds are hard to generalize for large number of events.

Sentiment analysis also known as opinion mining is a subfield within Natural Language Processing (NLP) that builds machine learning algorithms to classify a text according to the sentimental polarities of opinions it contains, e.g., positive or negative. In recent year, sentiment analysis has become a topic of great interest and development in both academics and industry. Analysing the sentiment of texts could benefit, for example, customer services, product analytics, market research etc. Take Ebay as an example. Customers on Ebay choose their preferred products based on the reviews from other users. an automatic sentiment classification system can not only help companies grasp the satisfaction level of the products, but also significantly assist new customers to locate their online shopping shelves. In this data analysis challenge, we are interested in developing such an automatic sentiment classification system that relies on machine learning techniques to learn from a large set of product reviews provided by Yelp. The levels of polarity of opinion we consider include strong negative, weak negative, neutral, weak positive, and strong positive. For example, “Website says open, Google says open, Yelp says open on Sundays. Our delivery was cancelled suddenly and no one is answering the phone. Shame” gives us a strong negative sentiment, whereas the sentiment of “They have great food & definitely excellent service. Tried their mochi mango flavored and it s definitely delis” is likely to be strong positive. The sentiment analysis task is often formulated as a classification problem, where a classifier is fed with a text and returns the corresponding sentiment label, e.g., positive, negative, or neutral. In other words, the problem of learning the sentimental polarities of opinions is reduced to a classi- fication problem. There are many machine learning methods that can be used in the classification task. They can be categorised into supervised method (like SVM) and unsupervised method (like clustering).

Work Integrated Learning Programmes Division M.Tech (Data Science and Engineering) (S1-19_DSECLZG519) (Data Structures and Algorithms Design) Academic Year 2019-2020 Assignment 2 – PS7 - [ Cricket Batting Order ] - [Weightage 13%] 1. Problem Statement For the upcoming world-cup, the Indian Cricket Selection Committee has to come up with a possible batting order for their players. Instead of using the traditional approach they have decided to use computer algorithms to come up with all the possible batting orders and then decide from that. The algorithm however requires the possible batting positions for each player. The algorithm takes a list of 11 players. Each player can have more than one position they can bat at. Your job for now is to help the selection committee calculate the total number of unique batting charts such that every player gets exactly one batting position from their list of positions and no two players are given the same batting position in one batting chart. Requirements: 1. Formulate an efficient algorithm using dynamic programming to perform the above task. 2. Analyse the time complexity of your algorithm. 3. Implement the above problem statement using Python 3.7 Input: Input should be taken in through a file called “inputPS7.txt” which has the fixed format mentioned below using the “/” as a field separator: Player <num> / < position 1> / < position 2> / < position 3>.... Ex: P1 / 1 / 2 / 3 / 4 P2 / 1 / 5 / 9 / 2 / 6 / 7 / 8 P3 / 1 / 2 / 7 / 10 / 3 P4 / 1 / 9 / 2 / 6 / 7 / 10 / 3 / 4P5 / 5 / 9 / 2 / 8 / 3 / 4 P6 / 1 / 5 / 3 / 6 P7 / 6 / 7 / 4 P8 / 1 / 9 / 2 / 4 P9 / 9 / 6 / 11 / 3 / 4 P10 / 1 / 5 / 9 / 7 / 8 / 4 P11 / 6 / 11 / 7 / 10 Note that the input data shown here is only for understanding and testing, the actual file used for evaluation will be different. Output: Syntax of the output should be: The total number of allocations possible is: <number of possible unique combinations> Ex: The total number of allocations possible is: 4646. Display the output in outputPS7.txt. 2. Deliverables • Word document designPS7_<group id>.docx detailing your algorithm design and time complexity of the algorithm. • Zipped AS2_PS7_CBO_[Group id].py package folder containing all the modules classes and functions for the employee node, binary tree and the main body of the program. • inputPS7.txt file used for testing • outputPS7.txt file generated while testing 1. Instructions a. It is compulsory to make use of the data structure/s mentioned in the problem statement. b. It is compulsory to use Python 3.7 for implementation. c. Ensure that all data structures and functions throw appropriate messages when their capacity is empty or full.d. For the purposes of testing, you may implement some functions to print the data structures or other test data. But all such functions must be commented before submission. e. Make sure that your read, understand, and follow all the instructions f. Ensure that the input, prompt and output file guidelines are adhered to. Deviations from the mentioned formats will not be entertained. g. The input, prompt and output samples shown here are only a representation of the syntax to be used. Actual files used to test the submissions will be different. Hence, do not hard code any values into the code. h. Run time analysis is provided in asymptotic notations and not timestamp based runtimes in sec or milliseconds. 2. Deadline a. The strict deadline for submission of the assignment is 16 th Feb, 2020. b. The deadline is set for a month from the date of rollout to accommodate for the semester exams. No further extension of the deadline will be entertained. c. Late submissions will not be evaluated. 3. How to submit a. This is a group assignment. b. Each group has to make one submission (only one, no resubmission) of solutions. c. Each group should zip the deliverables and name the zipped file as below “ASSIGNMENT1_[BLR/HYD/DLH/PUN/CHE]_[G1/G2/...].zip” and upload in CANVAS in respective location under ASSIGNMENT Tab. d. Assignment submitted via means other than through CANVAS will not be graded. 4. Evaluation a. The assignment carries 13 Marks. b. Grading will depend on a. Fully executable code with all functionality b. Well-structured and commented code c. Accuracy of the run time analysis and design document c. Every bug in the functionality will have negative marking.d. Source code files which contain compilation errors will get at most 25% of the value of that question. 5. Readings Text book: Algorithms Design: Foundations, Analysis and Internet Examples Michael T. Goodrich, Roberto Tamassia, 2006, Wiley (Students Edition)

This model is formulated to Classification problem. It uses the concepts of Graph data structure

Formulate the network optimization problem as a discrete model, identifying mathematically the variables and constraints associated with the network. Formulate (mathematically) and solve a non-linear optimization problem based on real (or realistic) world data.