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sayantann11 / All Classification Templetes For MLClassification - Machine Learning This is ‘Classification’ tutorial which is a part of the Machine Learning course offered by Simplilearn. We will learn Classification algorithms, types of classification algorithms, support vector machines(SVM), Naive Bayes, Decision Tree and Random Forest Classifier in this tutorial. Objectives Let us look at some of the objectives covered under this section of Machine Learning tutorial. Define Classification and list its algorithms Describe Logistic Regression and Sigmoid Probability Explain K-Nearest Neighbors and KNN classification Understand Support Vector Machines, Polynomial Kernel, and Kernel Trick Analyze Kernel Support Vector Machines with an example Implement the Naïve Bayes Classifier Demonstrate Decision Tree Classifier Describe Random Forest Classifier Classification: Meaning Classification is a type of supervised learning. It specifies the class to which data elements belong to and is best used when the output has finite and discrete values. It predicts a class for an input variable as well. There are 2 types of Classification: Binomial Multi-Class Classification: Use Cases Some of the key areas where classification cases are being used: To find whether an email received is a spam or ham To identify customer segments To find if a bank loan is granted To identify if a kid will pass or fail in an examination Classification: Example Social media sentiment analysis has two potential outcomes, positive or negative, as displayed by the chart given below. https://www.simplilearn.com/ice9/free_resources_article_thumb/classification-example-machine-learning.JPG This chart shows the classification of the Iris flower dataset into its three sub-species indicated by codes 0, 1, and 2. https://www.simplilearn.com/ice9/free_resources_article_thumb/iris-flower-dataset-graph.JPG The test set dots represent the assignment of new test data points to one class or the other based on the trained classifier model. Types of Classification Algorithms Let’s have a quick look into the types of Classification Algorithm below. Linear Models Logistic Regression Support Vector Machines Nonlinear models K-nearest Neighbors (KNN) Kernel Support Vector Machines (SVM) Naïve Bayes Decision Tree Classification Random Forest Classification Logistic Regression: Meaning Let us understand the Logistic Regression model below. This refers to a regression model that is used for classification. This method is widely used for binary classification problems. It can also be extended to multi-class classification problems. Here, the dependent variable is categorical: y ϵ {0, 1} A binary dependent variable can have only two values, like 0 or 1, win or lose, pass or fail, healthy or sick, etc In this case, you model the probability distribution of output y as 1 or 0. This is called the sigmoid probability (σ). If σ(θ Tx) > 0.5, set y = 1, else set y = 0 Unlike Linear Regression (and its Normal Equation solution), there is no closed form solution for finding optimal weights of Logistic Regression. Instead, you must solve this with maximum likelihood estimation (a probability model to detect the maximum likelihood of something happening). It can be used to calculate the probability of a given outcome in a binary model, like the probability of being classified as sick or passing an exam. https://www.simplilearn.com/ice9/free_resources_article_thumb/logistic-regression-example-graph.JPG Sigmoid Probability The probability in the logistic regression is often represented by the Sigmoid function (also called the logistic function or the S-curve): https://www.simplilearn.com/ice9/free_resources_article_thumb/sigmoid-function-machine-learning.JPG In this equation, t represents data values * the number of hours studied and S(t) represents the probability of passing the exam. Assume sigmoid function: https://www.simplilearn.com/ice9/free_resources_article_thumb/sigmoid-probability-machine-learning.JPG g(z) tends toward 1 as z -> infinity , and g(z) tends toward 0 as z -> infinity K-nearest Neighbors (KNN) K-nearest Neighbors algorithm is used to assign a data point to clusters based on similarity measurement. It uses a supervised method for classification. The steps to writing a k-means algorithm are as given below: https://www.simplilearn.com/ice9/free_resources_article_thumb/knn-distribution-graph-machine-learning.JPG Choose the number of k and a distance metric. (k = 5 is common) Find k-nearest neighbors of the sample that you want to classify Assign the class label by majority vote. KNN Classification A new input point is classified in the category such that it has the most number of neighbors from that category. For example: https://www.simplilearn.com/ice9/free_resources_article_thumb/knn-classification-machine-learning.JPG Classify a patient as high risk or low risk. Mark email as spam or ham. Keen on learning about Classification Algorithms in Machine Learning? Click here! Support Vector Machine (SVM) Let us understand Support Vector Machine (SVM) in detail below. SVMs are classification algorithms used to assign data to various classes. They involve detecting hyperplanes which segregate data into classes. SVMs are very versatile and are also capable of performing linear or nonlinear classification, regression, and outlier detection. Once ideal hyperplanes are discovered, new data points can be easily classified. https://www.simplilearn.com/ice9/free_resources_article_thumb/support-vector-machines-graph-machine-learning.JPG The optimization objective is to find “maximum margin hyperplane” that is farthest from the closest points in the two classes (these points are called support vectors). In the given figure, the middle line represents the hyperplane. SVM Example Let’s look at this image below and have an idea about SVM in general. Hyperplanes with larger margins have lower generalization error. The positive and negative hyperplanes are represented by: https://www.simplilearn.com/ice9/free_resources_article_thumb/positive-negative-hyperplanes-machine-learning.JPG Classification of any new input sample xtest : If w0 + wTxtest > 1, the sample xtest is said to be in the class toward the right of the positive hyperplane. If w0 + wTxtest < -1, the sample xtest is said to be in the class toward the left of the negative hyperplane. When you subtract the two equations, you get: https://www.simplilearn.com/ice9/free_resources_article_thumb/equation-subtraction-machine-learning.JPG Length of vector w is (L2 norm length): https://www.simplilearn.com/ice9/free_resources_article_thumb/length-of-vector-machine-learning.JPG You normalize with the length of w to arrive at: https://www.simplilearn.com/ice9/free_resources_article_thumb/normalize-equation-machine-learning.JPG SVM: Hard Margin Classification Given below are some points to understand Hard Margin Classification. The left side of equation SVM-1 given above can be interpreted as the distance between the positive (+ve) and negative (-ve) hyperplanes; in other words, it is the margin that can be maximized. Hence the objective of the function is to maximize with the constraint that the samples are classified correctly, which is represented as : https://www.simplilearn.com/ice9/free_resources_article_thumb/hard-margin-classification-machine-learning.JPG This means that you are minimizing ‖w‖. This also means that all positive samples are on one side of the positive hyperplane and all negative samples are on the other side of the negative hyperplane. This can be written concisely as : https://www.simplilearn.com/ice9/free_resources_article_thumb/hard-margin-classification-formula.JPG Minimizing ‖w‖ is the same as minimizing. This figure is better as it is differentiable even at w = 0. The approach listed above is called “hard margin linear SVM classifier.” SVM: Soft Margin Classification Given below are some points to understand Soft Margin Classification. To allow for linear constraints to be relaxed for nonlinearly separable data, a slack variable is introduced. (i) measures how much ith instance is allowed to violate the margin. The slack variable is simply added to the linear constraints. https://www.simplilearn.com/ice9/free_resources_article_thumb/soft-margin-calculation-machine-learning.JPG Subject to the above constraints, the new objective to be minimized becomes: https://www.simplilearn.com/ice9/free_resources_article_thumb/soft-margin-calculation-formula.JPG You have two conflicting objectives now—minimizing slack variable to reduce margin violations and minimizing to increase the margin. The hyperparameter C allows us to define this trade-off. Large values of C correspond to larger error penalties (so smaller margins), whereas smaller values of C allow for higher misclassification errors and larger margins. https://www.simplilearn.com/ice9/free_resources_article_thumb/machine-learning-certification-video-preview.jpg SVM: Regularization The concept of C is the reverse of regularization. Higher C means lower regularization, which increases bias and lowers the variance (causing overfitting). https://www.simplilearn.com/ice9/free_resources_article_thumb/concept-of-c-graph-machine-learning.JPG IRIS Data Set The Iris dataset contains measurements of 150 IRIS flowers from three different species: Setosa Versicolor Viriginica Each row represents one sample. Flower measurements in centimeters are stored as columns. These are called features. IRIS Data Set: SVM Let’s train an SVM model using sci-kit-learn for the Iris dataset: https://www.simplilearn.com/ice9/free_resources_article_thumb/svm-model-graph-machine-learning.JPG Nonlinear SVM Classification There are two ways to solve nonlinear SVMs: by adding polynomial features by adding similarity features Polynomial features can be added to datasets; in some cases, this can create a linearly separable dataset. https://www.simplilearn.com/ice9/free_resources_article_thumb/nonlinear-classification-svm-machine-learning.JPG In the figure on the left, there is only 1 feature x1. This dataset is not linearly separable. If you add x2 = (x1)2 (figure on the right), the data becomes linearly separable. Polynomial Kernel In sci-kit-learn, one can use a Pipeline class for creating polynomial features. Classification results for the Moons dataset are shown in the figure. https://www.simplilearn.com/ice9/free_resources_article_thumb/polynomial-kernel-machine-learning.JPG Polynomial Kernel with Kernel Trick Let us look at the image below and understand Kernel Trick in detail. https://www.simplilearn.com/ice9/free_resources_article_thumb/polynomial-kernel-with-kernel-trick.JPG For large dimensional datasets, adding too many polynomial features can slow down the model. You can apply a kernel trick with the effect of polynomial features without actually adding them. The code is shown (SVC class) below trains an SVM classifier using a 3rd-degree polynomial kernel but with a kernel trick. https://www.simplilearn.com/ice9/free_resources_article_thumb/polynomial-kernel-equation-machine-learning.JPG The hyperparameter coefθ controls the influence of high-degree polynomials. Kernel SVM Let us understand in detail about Kernel SVM. Kernel SVMs are used for classification of nonlinear data. In the chart, nonlinear data is projected into a higher dimensional space via a mapping function where it becomes linearly separable. https://www.simplilearn.com/ice9/free_resources_article_thumb/kernel-svm-machine-learning.JPG In the higher dimension, a linear separating hyperplane can be derived and used for classification. A reverse projection of the higher dimension back to original feature space takes it back to nonlinear shape. As mentioned previously, SVMs can be kernelized to solve nonlinear classification problems. You can create a sample dataset for XOR gate (nonlinear problem) from NumPy. 100 samples will be assigned the class sample 1, and 100 samples will be assigned the class label -1. https://www.simplilearn.com/ice9/free_resources_article_thumb/kernel-svm-graph-machine-learning.JPG As you can see, this data is not linearly separable. https://www.simplilearn.com/ice9/free_resources_article_thumb/kernel-svm-non-separable.JPG You now use the kernel trick to classify XOR dataset created earlier. https://www.simplilearn.com/ice9/free_resources_article_thumb/kernel-svm-xor-machine-learning.JPG Naïve Bayes Classifier What is Naive Bayes Classifier? Have you ever wondered how your mail provider implements spam filtering or how online news channels perform news text classification or even how companies perform sentiment analysis of their audience on social media? All of this and more are done through a machine learning algorithm called Naive Bayes Classifier. Naive Bayes Named after Thomas Bayes from the 1700s who first coined this in the Western literature. Naive Bayes classifier works on the principle of conditional probability as given by the Bayes theorem. Advantages of Naive Bayes Classifier Listed below are six benefits of Naive Bayes Classifier. Very simple and easy to implement Needs less training data Handles both continuous and discrete data Highly scalable with the number of predictors and data points As it is fast, it can be used in real-time predictions Not sensitive to irrelevant features Bayes Theorem We will understand Bayes Theorem in detail from the points mentioned below. According to the Bayes model, the conditional probability P(Y|X) can be calculated as: P(Y|X) = P(X|Y)P(Y) / P(X) This means you have to estimate a very large number of P(X|Y) probabilities for a relatively small vector space X. For example, for a Boolean Y and 30 possible Boolean attributes in the X vector, you will have to estimate 3 billion probabilities P(X|Y). To make it practical, a Naïve Bayes classifier is used, which assumes conditional independence of P(X) to each other, with a given value of Y. This reduces the number of probability estimates to 2*30=60 in the above example. Naïve Bayes Classifier for SMS Spam Detection Consider a labeled SMS database having 5574 messages. It has messages as given below: https://www.simplilearn.com/ice9/free_resources_article_thumb/naive-bayes-spam-machine-learning.JPG Each message is marked as spam or ham in the data set. Let’s train a model with Naïve Bayes algorithm to detect spam from ham. The message lengths and their frequency (in the training dataset) are as shown below: https://www.simplilearn.com/ice9/free_resources_article_thumb/naive-bayes-spam-spam-detection.JPG Analyze the logic you use to train an algorithm to detect spam: Split each message into individual words/tokens (bag of words). Lemmatize the data (each word takes its base form, like “walking” or “walked” is replaced with “walk”). Convert data to vectors using scikit-learn module CountVectorizer. Run TFIDF to remove common words like “is,” “are,” “and.” Now apply scikit-learn module for Naïve Bayes MultinomialNB to get the Spam Detector. This spam detector can then be used to classify a random new message as spam or ham. Next, the accuracy of the spam detector is checked using the Confusion Matrix. For the SMS spam example above, the confusion matrix is shown on the right. Accuracy Rate = Correct / Total = (4827 + 592)/5574 = 97.21% Error Rate = Wrong / Total = (155 + 0)/5574 = 2.78% https://www.simplilearn.com/ice9/free_resources_article_thumb/confusion-matrix-machine-learning.JPG Although confusion Matrix is useful, some more precise metrics are provided by Precision and Recall. https://www.simplilearn.com/ice9/free_resources_article_thumb/precision-recall-matrix-machine-learning.JPG Precision refers to the accuracy of positive predictions. https://www.simplilearn.com/ice9/free_resources_article_thumb/precision-formula-machine-learning.JPG Recall refers to the ratio of positive instances that are correctly detected by the classifier (also known as True positive rate or TPR). https://www.simplilearn.com/ice9/free_resources_article_thumb/recall-formula-machine-learning.JPG Precision/Recall Trade-off To detect age-appropriate videos for kids, you need high precision (low recall) to ensure that only safe videos make the cut (even though a few safe videos may be left out). The high recall is needed (low precision is acceptable) in-store surveillance to catch shoplifters; a few false alarms are acceptable, but all shoplifters must be caught. Learn about Naive Bayes in detail. Click here! Decision Tree Classifier Some aspects of the Decision Tree Classifier mentioned below are. Decision Trees (DT) can be used both for classification and regression. The advantage of decision trees is that they require very little data preparation. They do not require feature scaling or centering at all. They are also the fundamental components of Random Forests, one of the most powerful ML algorithms. Unlike Random Forests and Neural Networks (which do black-box modeling), Decision Trees are white box models, which means that inner workings of these models are clearly understood. In the case of classification, the data is segregated based on a series of questions. Any new data point is assigned to the selected leaf node. https://www.simplilearn.com/ice9/free_resources_article_thumb/decision-tree-classifier-machine-learning.JPG Start at the tree root and split the data on the feature using the decision algorithm, resulting in the largest information gain (IG). This splitting procedure is then repeated in an iterative process at each child node until the leaves are pure. This means that the samples at each node belonging to the same class. In practice, you can set a limit on the depth of the tree to prevent overfitting. The purity is compromised here as the final leaves may still have some impurity. The figure shows the classification of the Iris dataset. https://www.simplilearn.com/ice9/free_resources_article_thumb/decision-tree-classifier-graph.JPG IRIS Decision Tree Let’s build a Decision Tree using scikit-learn for the Iris flower dataset and also visualize it using export_graphviz API. https://www.simplilearn.com/ice9/free_resources_article_thumb/iris-decision-tree-machine-learning.JPG The output of export_graphviz can be converted into png format: https://www.simplilearn.com/ice9/free_resources_article_thumb/iris-decision-tree-output.JPG Sample attribute stands for the number of training instances the node applies to. Value attribute stands for the number of training instances of each class the node applies to. Gini impurity measures the node’s impurity. A node is “pure” (gini=0) if all training instances it applies to belong to the same class. https://www.simplilearn.com/ice9/free_resources_article_thumb/impurity-formula-machine-learning.JPG For example, for Versicolor (green color node), the Gini is 1-(0/54)2 -(49/54)2 -(5/54) 2 ≈ 0.168 https://www.simplilearn.com/ice9/free_resources_article_thumb/iris-decision-tree-sample.JPG Decision Boundaries Let us learn to create decision boundaries below. For the first node (depth 0), the solid line splits the data (Iris-Setosa on left). Gini is 0 for Setosa node, so no further split is possible. The second node (depth 1) splits the data into Versicolor and Virginica. If max_depth were set as 3, a third split would happen (vertical dotted line). https://www.simplilearn.com/ice9/free_resources_article_thumb/decision-tree-boundaries.JPG For a sample with petal length 5 cm and petal width 1.5 cm, the tree traverses to depth 2 left node, so the probability predictions for this sample are 0% for Iris-Setosa (0/54), 90.7% for Iris-Versicolor (49/54), and 9.3% for Iris-Virginica (5/54) CART Training Algorithm Scikit-learn uses Classification and Regression Trees (CART) algorithm to train Decision Trees. CART algorithm: Split the data into two subsets using a single feature k and threshold tk (example, petal length < “2.45 cm”). This is done recursively for each node. k and tk are chosen such that they produce the purest subsets (weighted by their size). The objective is to minimize the cost function as given below: https://www.simplilearn.com/ice9/free_resources_article_thumb/cart-training-algorithm-machine-learning.JPG The algorithm stops executing if one of the following situations occurs: max_depth is reached No further splits are found for each node Other hyperparameters may be used to stop the tree: min_samples_split min_samples_leaf min_weight_fraction_leaf max_leaf_nodes Gini Impurity or Entropy Entropy is one more measure of impurity and can be used in place of Gini. https://www.simplilearn.com/ice9/free_resources_article_thumb/gini-impurity-entrophy.JPG It is a degree of uncertainty, and Information Gain is the reduction that occurs in entropy as one traverses down the tree. Entropy is zero for a DT node when the node contains instances of only one class. Entropy for depth 2 left node in the example given above is: https://www.simplilearn.com/ice9/free_resources_article_thumb/entrophy-for-depth-2.JPG Gini and Entropy both lead to similar trees. DT: Regularization The following figure shows two decision trees on the moons dataset. https://www.simplilearn.com/ice9/free_resources_article_thumb/dt-regularization-machine-learning.JPG The decision tree on the right is restricted by min_samples_leaf = 4. The model on the left is overfitting, while the model on the right generalizes better. Random Forest Classifier Let us have an understanding of Random Forest Classifier below. A random forest can be considered an ensemble of decision trees (Ensemble learning). Random Forest algorithm: Draw a random bootstrap sample of size n (randomly choose n samples from the training set). Grow a decision tree from the bootstrap sample. At each node, randomly select d features. Split the node using the feature that provides the best split according to the objective function, for instance by maximizing the information gain. Repeat the steps 1 to 2 k times. (k is the number of trees you want to create, using a subset of samples) Aggregate the prediction by each tree for a new data point to assign the class label by majority vote (pick the group selected by the most number of trees and assign new data point to that group). Random Forests are opaque, which means it is difficult to visualize their inner workings. https://www.simplilearn.com/ice9/free_resources_article_thumb/random-forest-classifier-graph.JPG However, the advantages outweigh their limitations since you do not have to worry about hyperparameters except k, which stands for the number of decision trees to be created from a subset of samples. RF is quite robust to noise from the individual decision trees. Hence, you need not prune individual decision trees. The larger the number of decision trees, the more accurate the Random Forest prediction is. (This, however, comes with higher computation cost). Key Takeaways Let us quickly run through what we have learned so far in this Classification tutorial. Classification algorithms are supervised learning methods to split data into classes. They can work on Linear Data as well as Nonlinear Data. Logistic Regression can classify data based on weighted parameters and sigmoid conversion to calculate the probability of classes. K-nearest Neighbors (KNN) algorithm uses similar features to classify data. Support Vector Machines (SVMs) classify data by detecting the maximum margin hyperplane between data classes. Naïve Bayes, a simplified Bayes Model, can help classify data using conditional probability models. Decision Trees are powerful classifiers and use tree splitting logic until pure or somewhat pure leaf node classes are attained. Random Forests apply Ensemble Learning to Decision Trees for more accurate classification predictions. Conclusion This completes ‘Classification’ tutorial. In the next tutorial, we will learn 'Unsupervised Learning with Clustering.'
asad70 / Reddit Sentiment AnalysisThis program goes thru reddit, finds the most mentioned tickers and uses Vader SentimentIntensityAnalyzer to calculate the ticker compound value.
jaygooby / Ttfb.shMeasures time-to-first-byte in seconds, for single or multiple URLs. Can calculate fastest, slowest & median TTFB values, and optionally log all response headers. Uses curl and the calculation `%{time_starttransfer} - %{time_appconnect}` which doesn't include any connection overhead, to better approximate devtool’s TTFB figure.
9elements / Min Max CalculatorA tool that calculates the CSS clamp formula to interpolate between two values in a given viewport range.
ManojKumarPatnaik / Major Project ListA list of practical projects that anyone can solve in any programming language (See solutions). These projects are divided into multiple categories, and each category has its own folder. To get started, simply fork this repo. CONTRIBUTING See ways of contributing to this repo. You can contribute solutions (will be published in this repo) to existing problems, add new projects, or remove existing ones. Make sure you follow all instructions properly. Solutions You can find implementations of these projects in many other languages by other users in this repo. Credits Problems are motivated by the ones shared at: Martyr2’s Mega Project List Rosetta Code Table of Contents Numbers Classic Algorithms Graph Data Structures Text Networking Classes Threading Web Files Databases Graphics and Multimedia Security Numbers Find PI to the Nth Digit - Enter a number and have the program generate PI up to that many decimal places. Keep a limit to how far the program will go. Find e to the Nth Digit - Just like the previous problem, but with e instead of PI. Enter a number and have the program generate e up to that many decimal places. Keep a limit to how far the program will go. Fibonacci Sequence - Enter a number and have the program generate the Fibonacci sequence to that number or to the Nth number. Prime Factorization - Have the user enter a number and find all Prime Factors (if there are any) and display them. Next Prime Number - Have the program find prime numbers until the user chooses to stop asking for the next one. Find Cost of Tile to Cover W x H Floor - Calculate the total cost of the tile it would take to cover a floor plan of width and height, using a cost entered by the user. Mortgage Calculator - Calculate the monthly payments of a fixed-term mortgage over given Nth terms at a given interest rate. Also, figure out how long it will take the user to pay back the loan. For added complexity, add an option for users to select the compounding interval (Monthly, Weekly, Daily, Continually). Change Return Program - The user enters a cost and then the amount of money given. The program will figure out the change and the number of quarters, dimes, nickels, pennies needed for the change. Binary to Decimal and Back Converter - Develop a converter to convert a decimal number to binary or a binary number to its decimal equivalent. Calculator - A simple calculator to do basic operators. Make it a scientific calculator for added complexity. Unit Converter (temp, currency, volume, mass, and more) - Converts various units between one another. The user enters the type of unit being entered, the type of unit they want to convert to, and then the value. The program will then make the conversion. Alarm Clock - A simple clock where it plays a sound after X number of minutes/seconds or at a particular time. Distance Between Two Cities - Calculates the distance between two cities and allows the user to specify a unit of distance. This program may require finding coordinates for the cities like latitude and longitude. Credit Card Validator - Takes in a credit card number from a common credit card vendor (Visa, MasterCard, American Express, Discoverer) and validates it to make sure that it is a valid number (look into how credit cards use a checksum). Tax Calculator - Asks the user to enter a cost and either a country or state tax. It then returns the tax plus the total cost with tax. Factorial Finder - The Factorial of a positive integer, n, is defined as the product of the sequence n, n-1, n-2, ...1, and the factorial of zero, 0, is defined as being 1. Solve this using both loops and recursion. Complex Number Algebra - Show addition, multiplication, negation, and inversion of complex numbers in separate functions. (Subtraction and division operations can be made with pairs of these operations.) Print the results for each operation tested. Happy Numbers - A happy number is defined by the following process. Starting with any positive integer, replace the number by the sum of the squares of its digits, and repeat the process until the number equals 1 (where it will stay), or it loops endlessly in a cycle which does not include 1. Those numbers for which this process ends in 1 are happy numbers, while those that do not end in 1 are unhappy numbers. Display an example of your output here. Find the first 8 happy numbers. Number Names - Show how to spell out a number in English. You can use a preexisting implementation or roll your own, but you should support inputs up to at least one million (or the maximum value of your language's default bounded integer type if that's less). Optional: Support for inputs other than positive integers (like zero, negative integers, and floating-point numbers). Coin Flip Simulation - Write some code that simulates flipping a single coin however many times the user decides. The code should record the outcomes and count the number of tails and heads. Limit Calculator - Ask the user to enter f(x) and the limit value, then return the value of the limit statement Optional: Make the calculator capable of supporting infinite limits. Fast Exponentiation - Ask the user to enter 2 integers a and b and output a^b (i.e. pow(a,b)) in O(LG n) time complexity. Classic Algorithms Collatz Conjecture - Start with a number n > 1. Find the number of steps it takes to reach one using the following process: If n is even, divide it by 2. If n is odd, multiply it by 3 and add 1. Sorting - Implement two types of sorting algorithms: Merge sort and bubble sort. Closest pair problem - The closest pair of points problem or closest pair problem is a problem of computational geometry: given n points in metric space, find a pair of points with the smallest distance between them. Sieve of Eratosthenes - The sieve of Eratosthenes is one of the most efficient ways to find all of the smaller primes (below 10 million or so). Graph Graph from links - Create a program that will create a graph or network from a series of links. Eulerian Path - Create a program that will take as an input a graph and output either an Eulerian path or an Eulerian cycle, or state that it is not possible. An Eulerian path starts at one node and traverses every edge of a graph through every node and finishes at another node. An Eulerian cycle is an eulerian Path that starts and finishes at the same node. Connected Graph - Create a program that takes a graph as an input and outputs whether every node is connected or not. Dijkstra’s Algorithm - Create a program that finds the shortest path through a graph using its edges. Minimum Spanning Tree - Create a program that takes a connected, undirected graph with weights and outputs the minimum spanning tree of the graph i.e., a subgraph that is a tree, contains all the vertices, and the sum of its weights is the least possible. Data Structures Inverted index - An Inverted Index is a data structure used to create full-text search. Given a set of text files, implement a program to create an inverted index. Also, create a user interface to do a search using that inverted index which returns a list of files that contain the query term/terms. The search index can be in memory. Text Fizz Buzz - Write a program that prints the numbers from 1 to 100. But for multiples of three print “Fizz” instead of the number and for the multiples of five print “Buzz”. For numbers which are multiples of both three and five print “FizzBuzz”. Reverse a String - Enter a string and the program will reverse it and print it out. Pig Latin - Pig Latin is a game of alterations played in the English language game. To create the Pig Latin form of an English word the initial consonant sound is transposed to the end of the word and an ay is affixed (Ex.: "banana" would yield anana-bay). Read Wikipedia for more information on rules. Count Vowels - Enter a string and the program counts the number of vowels in the text. For added complexity have it report a sum of each vowel found. Check if Palindrome - Checks if the string entered by the user is a palindrome. That is that it reads the same forwards as backward like “racecar” Count Words in a String - Counts the number of individual words in a string. For added complexity read these strings in from a text file and generate a summary. Text Editor - Notepad-style application that can open, edit, and save text documents. Optional: Add syntax highlighting and other features. RSS Feed Creator - Given a link to RSS/Atom Feed, get all posts and display them. Quote Tracker (market symbols etc) - A program that can go out and check the current value of stocks for a list of symbols entered by the user. The user can set how often the stocks are checked. For CLI, show whether the stock has moved up or down. Optional: If GUI, the program can show green up and red down arrows to show which direction the stock value has moved. Guestbook / Journal - A simple application that allows people to add comments or write journal entries. It can allow comments or not and timestamps for all entries. Could also be made into a shoutbox. Optional: Deploy it on Google App Engine or Heroku or any other PaaS (if possible, of course). Vigenere / Vernam / Ceasar Ciphers - Functions for encrypting and decrypting data messages. Then send them to a friend. Regex Query Tool - A tool that allows the user to enter a text string and then in a separate control enter a regex pattern. It will run the regular expression against the source text and return any matches or flag errors in the regular expression. Networking FTP Program - A file transfer program that can transfer files back and forth from a remote web sever. Bandwidth Monitor - A small utility program that tracks how much data you have uploaded and downloaded from the net during the course of your current online session. See if you can find out what periods of the day you use more and less and generate a report or graph that shows it. Port Scanner - Enter an IP address and a port range where the program will then attempt to find open ports on the given computer by connecting to each of them. On any successful connections mark the port as open. Mail Checker (POP3 / IMAP) - The user enters various account information include web server and IP, protocol type (POP3 or IMAP), and the application will check for email at a given interval. Country from IP Lookup - Enter an IP address and find the country that IP is registered in. Optional: Find the Ip automatically. Whois Search Tool - Enter an IP or host address and have it look it up through whois and return the results to you. Site Checker with Time Scheduling - An application that attempts to connect to a website or server every so many minute or a given time and check if it is up. If it is down, it will notify you by email or by posting a notice on the screen. Classes Product Inventory Project - Create an application that manages an inventory of products. Create a product class that has a price, id, and quantity on hand. Then create an inventory class that keeps track of various products and can sum up the inventory value. Airline / Hotel Reservation System - Create a reservation system that books airline seats or hotel rooms. It charges various rates for particular sections of the plane or hotel. For example, first class is going to cost more than a coach. Hotel rooms have penthouse suites which cost more. Keep track of when rooms will be available and can be scheduled. Company Manager - Create a hierarchy of classes - abstract class Employee and subclasses HourlyEmployee, SalariedEmployee, Manager, and Executive. Everyone's pay is calculated differently, research a bit about it. After you've established an employee hierarchy, create a Company class that allows you to manage the employees. You should be able to hire, fire, and raise employees. Bank Account Manager - Create a class called Account which will be an abstract class for three other classes called CheckingAccount, SavingsAccount, and BusinessAccount. Manage credits and debits from these accounts through an ATM-style program. Patient / Doctor Scheduler - Create a patient class and a doctor class. Have a doctor that can handle multiple patients and set up a scheduling program where a doctor can only handle 16 patients during an 8 hr workday. Recipe Creator and Manager - Create a recipe class with ingredients and put them in a recipe manager program that organizes them into categories like desserts, main courses, or by ingredients like chicken, beef, soups, pies, etc. Image Gallery - Create an image abstract class and then a class that inherits from it for each image type. Put them in a program that displays them in a gallery-style format for viewing. Shape Area and Perimeter Classes - Create an abstract class called Shape and then inherit from it other shapes like diamond, rectangle, circle, triangle, etc. Then have each class override the area and perimeter functionality to handle each shape type. Flower Shop Ordering To Go - Create a flower shop application that deals in flower objects and use those flower objects in a bouquet object which can then be sold. Keep track of the number of objects and when you may need to order more. Family Tree Creator - Create a class called Person which will have a name, when they were born, and when (and if) they died. Allow the user to create these Person classes and put them into a family tree structure. Print out the tree to the screen. Threading Create A Progress Bar for Downloads - Create a progress bar for applications that can keep track of a download in progress. The progress bar will be on a separate thread and will communicate with the main thread using delegates. Bulk Thumbnail Creator - Picture processing can take a bit of time for some transformations. Especially if the image is large. Create an image program that can take hundreds of images and converts them to a specified size in the background thread while you do other things. For added complexity, have one thread handling re-sizing, have another bulk renaming of thumbnails, etc. Web Page Scraper - Create an application that connects to a site and pulls out all links, or images, and saves them to a list. Optional: Organize the indexed content and don’t allow duplicates. Have it put the results into an easily searchable index file. Online White Board - Create an application that allows you to draw pictures, write notes and use various colors to flesh out ideas for projects. Optional: Add a feature to invite friends to collaborate on a whiteboard online. Get Atomic Time from Internet Clock - This program will get the true atomic time from an atomic time clock on the Internet. Use any one of the atomic clocks returned by a simple Google search. Fetch Current Weather - Get the current weather for a given zip/postal code. Optional: Try locating the user automatically. Scheduled Auto Login and Action - Make an application that logs into a given site on a schedule and invokes a certain action and then logs out. This can be useful for checking webmail, posting regular content, or getting info for other applications and saving it to your computer. E-Card Generator - Make a site that allows people to generate their own little e-cards and send them to other people. Do not use Flash. Use a picture library and perhaps insightful mottos or quotes. Content Management System - Create a content management system (CMS) like Joomla, Drupal, PHP Nuke, etc. Start small. Optional: Allow for the addition of modules/addons. Web Board (Forum) - Create a forum for you and your buddies to post, administer and share thoughts and ideas. CAPTCHA Maker - Ever see those images with letters numbers when you signup for a service and then ask you to enter what you see? It keeps web bots from automatically signing up and spamming. Try creating one yourself for online forms. Files Quiz Maker - Make an application that takes various questions from a file, picked randomly, and puts together a quiz for students. Each quiz can be different and then reads a key to grade the quizzes. Sort Excel/CSV File Utility - Reads a file of records, sorts them, and then writes them back to the file. Allow the user to choose various sort style and sorting based on a particular field. Create Zip File Maker - The user enters various files from different directories and the program zips them up into a zip file. Optional: Apply actual compression to the files. Start with Huffman Algorithm. PDF Generator - An application that can read in a text file, HTML file, or some other file and generates a PDF file out of it. Great for a web-based service where the user uploads the file and the program returns a PDF of the file. Optional: Deploy on GAE or Heroku if possible. Mp3 Tagger - Modify and add ID3v1 tags to MP3 files. See if you can also add in the album art into the MP3 file’s header as well as other ID3v2 tags. Code Snippet Manager - Another utility program that allows coders to put in functions, classes, or other tidbits to save for use later. Organized by the type of snippet or language the coder can quickly lookup code. Optional: For extra practice try adding syntax highlighting based on the language. Databases SQL Query Analyzer - A utility application in which a user can enter a query and have it run against a local database and look for ways to make it more efficient. Remote SQL Tool - A utility that can execute queries on remote servers from your local computer across the Internet. It should take in a remote host, user name, and password, run the query and return the results. Report Generator - Create a utility that generates a report based on some tables in a database. Generates sales reports based on the order/order details tables or sums up the day's current database activity. Event Scheduler and Calendar - Make an application that allows the user to enter a date and time of an event, event notes, and then schedule those events on a calendar. The user can then browse the calendar or search the calendar for specific events. Optional: Allow the application to create re-occurrence events that reoccur every day, week, month, year, etc. Budget Tracker - Write an application that keeps track of a household’s budget. The user can add expenses, income, and recurring costs to find out how much they are saving or losing over a period of time. Optional: Allow the user to specify a date range and see the net flow of money in and out of the house budget for that time period. TV Show Tracker - Got a favorite show you don’t want to miss? Don’t have a PVR or want to be able to find the show to then PVR it later? Make an application that can search various online TV Guide sites, locate the shows/times/channels and add them to a database application. The database/website then can send you email reminders that a show is about to start and which channel it will be on. Travel Planner System - Make a system that allows users to put together their own little travel itinerary and keep track of the airline/hotel arrangements, points of interest, budget, and schedule. Graphics and Multimedia Slide Show - Make an application that shows various pictures in a slide show format. Optional: Try adding various effects like fade in/out, star wipe, and window blinds transitions. Stream Video from Online - Try to create your own online streaming video player. Mp3 Player - A simple program for playing your favorite music files. Add features you think are missing from your favorite music player. Watermarking Application - Have some pictures you want copyright protected? Add your own logo or text lightly across the background so that no one can simply steal your graphics off your site. Make a program that will add this watermark to the picture. Optional: Use threading to process multiple images simultaneously. Turtle Graphics - This is a common project where you create a floor of 20 x 20 squares. Using various commands you tell a turtle to draw a line on the floor. You have moved forward, left or right, lift or drop the pen, etc. Do a search online for "Turtle Graphics" for more information. Optional: Allow the program to read in the list of commands from a file. GIF Creator A program that puts together multiple images (PNGs, JPGs, TIFFs) to make a smooth GIF that can be exported. Optional: Make the program convert small video files to GIFs as well. Security Caesar cipher - Implement a Caesar cipher, both encoding, and decoding. The key is an integer from 1 to 25. This cipher rotates the letters of the alphabet (A to Z). The encoding replaces each letter with the 1st to 25th next letter in the alphabet (wrapping Z to A). So key 2 encrypts "HI" to "JK", but key 20 encrypts "HI" to "BC". This simple "monoalphabetic substitution cipher" provides almost no security, because an attacker who has the encoded message can either use frequency analysis to guess the key, or just try all 25 keys.
mattiasgeniar / Php PercentagesA clean interface to calculate percentages in PHP: changes between values, percentage increases, partial values, ...
Aastha2104 / Parkinson Disease PredictionIntroduction Parkinson’s Disease is the second most prevalent neurodegenerative disorder after Alzheimer’s, affecting more than 10 million people worldwide. Parkinson’s is characterized primarily by the deterioration of motor and cognitive ability. There is no single test which can be administered for diagnosis. Instead, doctors must perform a careful clinical analysis of the patient’s medical history. Unfortunately, this method of diagnosis is highly inaccurate. A study from the National Institute of Neurological Disorders finds that early diagnosis (having symptoms for 5 years or less) is only 53% accurate. This is not much better than random guessing, but an early diagnosis is critical to effective treatment. Because of these difficulties, I investigate a machine learning approach to accurately diagnose Parkinson’s, using a dataset of various speech features (a non-invasive yet characteristic tool) from the University of Oxford. Why speech features? Speech is very predictive and characteristic of Parkinson’s disease; almost every Parkinson’s patient experiences severe vocal degradation (inability to produce sustained phonations, tremor, hoarseness), so it makes sense to use voice to diagnose the disease. Voice analysis gives the added benefit of being non-invasive, inexpensive, and very easy to extract clinically. Background Parkinson's Disease Parkinson’s is a progressive neurodegenerative condition resulting from the death of the dopamine containing cells of the substantia nigra (which plays an important role in movement). Symptoms include: “frozen” facial features, bradykinesia (slowness of movement), akinesia (impairment of voluntary movement), tremor, and voice impairment. Typically, by the time the disease is diagnosed, 60% of nigrostriatal neurons have degenerated, and 80% of striatal dopamine have been depleted. Performance Metrics TP = true positive, FP = false positive, TN = true negative, FN = false negative Accuracy: (TP+TN)/(P+N) Matthews Correlation Coefficient: 1=perfect, 0=random, -1=completely inaccurate Algorithms Employed Logistic Regression (LR): Uses the sigmoid logistic equation with weights (coefficient values) and biases (constants) to model the probability of a certain class for binary classification. An output of 1 represents one class, and an output of 0 represents the other. Training the model will learn the optimal weights and biases. Linear Discriminant Analysis (LDA): Assumes that the data is Gaussian and each feature has the same variance. LDA estimates the mean and variance for each class from the training data, and then uses properties of statistics (Bayes theorem , Gaussian distribution, etc) to compute the probability of a particular instance belonging to a given class. The class with the largest probability is the prediction. k Nearest Neighbors (KNN): Makes predictions about the validation set using the entire training set. KNN makes a prediction about a new instance by searching through the entire set to find the k “closest” instances. “Closeness” is determined using a proximity measurement (Euclidean) across all features. The class that the majority of the k closest instances belong to is the class that the model predicts the new instance to be. Decision Tree (DT): Represented by a binary tree, where each root node represents an input variable and a split point, and each leaf node contains an output used to make a prediction. Neural Network (NN): Models the way the human brain makes decisions. Each neuron takes in 1+ inputs, and then uses an activation function to process the input with weights and biases to produce an output. Neurons can be arranged into layers, and multiple layers can form a network to model complex decisions. Training the network involves using the training instances to optimize the weights and biases. Naive Bayes (NB): Simplifies the calculation of probabilities by assuming that all features are independent of one another (a strong but effective assumption). Employs Bayes Theorem to calculate the probabilities that the instance to be predicted is in each class, then finds the class with the highest probability. Gradient Boost (GB): Generally used when seeking a model with very high predictive performance. Used to reduce bias and variance (“error”) by combining multiple “weak learners” (not very good models) to create a “strong learner” (high performance model). Involves 3 elements: a loss function (error function) to be optimized, a weak learner (decision tree) to make predictions, and an additive model to add trees to minimize the loss function. Gradient descent is used to minimize error after adding each tree (one by one). Engineering Goal Produce a machine learning model to diagnose Parkinson’s disease given various features of a patient’s speech with at least 90% accuracy and/or a Matthews Correlation Coefficient of at least 0.9. Compare various algorithms and parameters to determine the best model for predicting Parkinson’s. Dataset Description Source: the University of Oxford 195 instances (147 subjects with Parkinson’s, 48 without Parkinson’s) 22 features (elements that are possibly characteristic of Parkinson’s, such as frequency, pitch, amplitude / period of the sound wave) 1 label (1 for Parkinson’s, 0 for no Parkinson’s) Project Pipeline pipeline Summary of Procedure Split the Oxford Parkinson’s Dataset into two parts: one for training, one for validation (evaluate how well the model performs) Train each of the following algorithms with the training set: Logistic Regression, Linear Discriminant Analysis, k Nearest Neighbors, Decision Tree, Neural Network, Naive Bayes, Gradient Boost Evaluate results using the validation set Repeat for the following training set to validation set splits: 80% training / 20% validation, 75% / 25%, and 70% / 30% Repeat for a rescaled version of the dataset (scale all the numbers in the dataset to a range from 0 to 1: this helps to reduce the effect of outliers) Conduct 5 trials and average the results Data a_o a_r m_o m_r Data Analysis In general, the models tended to perform the best (both in terms of accuracy and Matthews Correlation Coefficient) on the rescaled dataset with a 75-25 train-test split. The two highest performing algorithms, k Nearest Neighbors and the Neural Network, both achieved an accuracy of 98%. The NN achieved a MCC of 0.96, while KNN achieved a MCC of 0.94. These figures outperform most existing literature and significantly outperform current methods of diagnosis. Conclusion and Significance These robust results suggest that a machine learning approach can indeed be implemented to significantly improve diagnosis methods of Parkinson’s disease. Given the necessity of early diagnosis for effective treatment, my machine learning models provide a very promising alternative to the current, rather ineffective method of diagnosis. Current methods of early diagnosis are only 53% accurate, while my machine learning model produces 98% accuracy. This 45% increase is critical because an accurate, early diagnosis is needed to effectively treat the disease. Typically, by the time the disease is diagnosed, 60% of nigrostriatal neurons have degenerated, and 80% of striatal dopamine have been depleted. With an earlier diagnosis, much of this degradation could have been slowed or treated. My results are very significant because Parkinson’s affects over 10 million people worldwide who could benefit greatly from an early, accurate diagnosis. Not only is my machine learning approach more accurate in terms of diagnostic accuracy, it is also more scalable, less expensive, and therefore more accessible to people who might not have access to established medical facilities and professionals. The diagnosis is also much simpler, requiring only a 10-15 second voice recording and producing an immediate diagnosis. Future Research Given more time and resources, I would investigate the following: Create a mobile application which would allow the user to record his/her voice, extract the necessary vocal features, and feed it into my machine learning model to diagnose Parkinson’s. Use larger datasets in conjunction with the University of Oxford dataset. Tune and improve my models even further to achieve even better results. Investigate different structures and types of neural networks. Construct a novel algorithm specifically suited for the prediction of Parkinson’s. Generalize my findings and algorithms for all types of dementia disorders, such as Alzheimer’s. References Bind, Shubham. "A Survey of Machine Learning Based Approaches for Parkinson Disease Prediction." International Journal of Computer Science and Information Technologies 6 (2015): n. pag. International Journal of Computer Science and Information Technologies. 2015. Web. 8 Mar. 2017. Brooks, Megan. "Diagnosing Parkinson's Disease Still Challenging." Medscape Medical News. National Institute of Neurological Disorders, 31 July 2014. Web. 20 Mar. 2017. Exploiting Nonlinear Recurrence and Fractal Scaling Properties for Voice Disorder Detection', Little MA, McSharry PE, Roberts SJ, Costello DAE, Moroz IM. BioMedical Engineering OnLine 2007, 6:23 (26 June 2007) Hashmi, Sumaiya F. "A Machine Learning Approach to Diagnosis of Parkinson’s Disease."Claremont Colleges Scholarship. Claremont College, 2013. Web. 10 Mar. 2017. Karplus, Abraham. "Machine Learning Algorithms for Cancer Diagnosis." Machine Learning Algorithms for Cancer Diagnosis (n.d.): n. pag. Mar. 2012. Web. 20 Mar. 2017. Little, Max. "Parkinsons Data Set." UCI Machine Learning Repository. University of Oxford, 26 June 2008. Web. 20 Feb. 2017. Ozcift, Akin, and Arif Gulten. "Classifier Ensemble Construction with Rotation Forest to Improve Medical Diagnosis Performance of Machine Learning Algorithms." Computer Methods and Programs in Biomedicine 104.3 (2011): 443-51. Semantic Scholar. 2011. Web. 15 Mar. 2017. "Parkinson’s Disease Dementia." UCI MIND. N.p., 19 Oct. 2015. Web. 17 Feb. 2017. Salvatore, C., A. Cerasa, I. Castiglioni, F. Gallivanone, A. Augimeri, M. Lopez, G. Arabia, M. Morelli, M.c. Gilardi, and A. Quattrone. "Machine Learning on Brain MRI Data for Differential Diagnosis of Parkinson's Disease and Progressive Supranuclear Palsy."Journal of Neuroscience Methods 222 (2014): 230-37. 2014. Web. 18 Mar. 2017. Shahbakhi, Mohammad, Danial Taheri Far, and Ehsan Tahami. "Speech Analysis for Diagnosis of Parkinson’s Disease Using Genetic Algorithm and Support Vector Machine."Journal of Biomedical Science and Engineering 07.04 (2014): 147-56. Scientific Research. July 2014. Web. 2 Mar. 2017. "Speech and Communication." Speech and Communication. Parkinson's Disease Foundation, n.d. Web. 22 Mar. 2017. Sriram, Tarigoppula V. S., M. Venkateswara Rao, G. V. Satya Narayana, and D. S. V. G. K. Kaladhar. "Diagnosis of Parkinson Disease Using Machine Learning and Data Mining Systems from Voice Dataset." SpringerLink. Springer, Cham, 01 Jan. 1970. Web. 17 Mar. 2017.
sotos82 / SolarSystemSimulatorGameA simulation of our Solar System and a game at the same time made mainly for educational purposes (it gives a good sense of the time and space scale of out Solar System). The planets the moon and the Space Crafts follow realistic (elliptical) orbits in phase space and the relative sizes of planets and moon are correct. For the orbital elements of the planets I used values according to the NASA planetary fact sheet. The radius of all planets and moon is increased by a factor of 30 to avoid floating point errors since the GPU internally is using single precision arithmetics. The planetary orbits are pre-computed at start time using a 4rth-order Runge-Kutta integrator. Specifically the 2nd Kepler-law (which is a differential equation) is solved for the values according to the Nasa Fact-Sheet for the planets of our solar system and the moon. The trajectories of the Space-Crafts are calculated at run time using a 2nd order Leapfrog Integrator with initial conditions based on user input and Earth's position and velocity at the time the Space Craft is launched. The goal of the game is to launch Space Crafts and complete the missions. Check out the video in the right for gameplay footage. Note that in order to play the game you need a mouse with a wheel. Programmed with C# and powered by Unity.
MobleyLab / FreeSolvExperimental and calculated small molecule hydration free energies
peek-travel / Swift CurrencyInteract with and calculate values of currencies in a type-safe way.
MBKraus / Python Portfolio VaR ToolPython-based portfolio / stock widget which sources data from Yahoo Finance and calculates different types of Value-at-Risk (VaR) metrics and many other (ex-post) risk/return characteristics both on an individual stock and portfolio-basis, stand-alone and vs. a benchmark of choice (constructed with wxPython)
Viralmaniar / MurMurHashThis little tool is to calculate a MurmurHash value of a favicon to hunt phishing websites on the Shodan platform.
bigtreetech / BIGTREETECH S42B V1.0The closed-loop drive is to feedback the rotation angle of the stepping motor to the control panel, compare the distance that needs to be rotated with the distance of the actual rotation, calculate the error value, and then compensate, so as to prevent the problem of multi-step and lost step. The closed-loop drive can completely overcome the lost step of the open-loop stepping motor, and can also significantly improve the performance of the motor at high speed
akashaero / Intrinsic Value CalculatorThis open-source, and convenient python tool is designed to calculate fair value of a stock for given revenue growth and free cash flow margin assumptions of a company. Users can run the tool in batch mode for multiple stock valuations in one go. Download the packaged application in the latest release!
HintikkaKimmo / SurebetSurebet is Python library for easily calculate betting odds, arbritrage betting opportunities and calculate value bet sizes
JuliaIntervals / IntervalConstraintProgramming.jlCalculate rigorously the feasible region for a set of real-valued inequalities with Julia
bideeen / Building A Trading Strategy With Pythontrading strategy is a fixed plan to go long or short in markets, there are two common trading strategies: the momentum strategy and the reversion strategy. Firstly, the momentum strategy is also called divergence or trend trading. When you follow this strategy, you do so because you believe the movement of a quantity will continue in its current direction. Stated differently, you believe that stocks have momentum or upward or downward trends, that you can detect and exploit. Some examples of this strategy are the moving average crossover, the dual moving average crossover, and turtle trading: The moving average crossover is when the price of an asset moves from one side of a moving average to the other. This crossover represents a change in momentum and can be used as a point of making the decision to enter or exit the market. You’ll see an example of this strategy, which is the “hello world” of quantitative trading later on in this tutorial. The dual moving average crossover occurs when a short-term average crosses a long-term average. This signal is used to identify that momentum is shifting in the direction of the short-term average. A buy signal is generated when the short-term average crosses the long-term average and rises above it, while a sell signal is triggered by a short-term average crossing long-term average and falling below it. Turtle trading is a popular trend following strategy that was initially taught by Richard Dennis. The basic strategy is to buy futures on a 20-day high and sell on a 20-day low. Secondly, the reversion strategy, which is also known as convergence or cycle trading. This strategy departs from the belief that the movement of a quantity will eventually reverse. This might seem a little bit abstract, but will not be so anymore when you take the example. Take a look at the mean reversion strategy, where you actually believe that stocks return to their mean and that you can exploit when it deviates from that mean. That already sounds a whole lot more practical, right? Another example of this strategy, besides the mean reversion strategy, is the pairs trading mean-reversion, which is similar to the mean reversion strategy. Whereas the mean reversion strategy basically stated that stocks return to their mean, the pairs trading strategy extends this and states that if two stocks can be identified that have a relatively high correlation, the change in the difference in price between the two stocks can be used to signal trading events if one of the two moves out of correlation with the other. That means that if the correlation between two stocks has decreased, the stock with the higher price can be considered to be in a short position. It should be sold because the higher-priced stock will return to the mean. The lower-priced stock, on the other hand, will be in a long position because the price will rise as the correlation will return to normal. Besides these two most frequent strategies, there are also other ones that you might come across once in a while, such as the forecasting strategy, which attempts to predict the direction or value of a stock, in this case, in subsequent future time periods based on certain historical factors. There’s also the High-Frequency Trading (HFT) strategy, which exploits the sub-millisecond market microstructure. That’s all music for the future for now; Let’s focus on developing your first trading strategy for now! A Simple Trading Strategy As you read above, you’ll start with the “hello world” of quantitative trading: the moving average crossover. The strategy that you’ll be developing is simple: you create two separate Simple Moving Averages (SMA) of a time series with differing lookback periods, let’s say, 40 days and 100 days. If the short moving average exceeds the long moving average then you go long, if the long moving average exceeds the short moving average then you exit. Remember that when you go long, you think that the stock price will go up and will sell at a higher price in the future (= buy signal); When you go short, you sell your stock, expecting that you can buy it back at a lower price and realize a profit (= sell signal). This simple strategy might seem quite complex when you’re just starting out, but let’s take this step by step: First define your two different lookback periods: a short window and a long window. You set up two variables and assign one integer per variable. Make sure that the integer that you assign to the short window is shorter than the integer that you assign to the long window variable! Next, make an empty signals DataFrame, but do make sure to copy the index of your aapl data so that you can start calculating the daily buy or sell signal for your aapl data. Create a column in your empty signals DataFrame that is named signal and initialize it by setting the value for all rows in this column to 0.0. After the preparatory work, it’s time to create the set of short and long simple moving averages over the respective long and short time windows. Make use of the rolling() function to start your rolling window calculations: within the function, specify the window and the min_period, and set the center argument. In practice, this will result in a rolling() function to which you have passed either short_window or long_window, 1 as the minimum number of observations in the window that are required to have a value, and False, so that the labels are not set at the center of the window. Next, don’t forget to also chain the mean() function so that you calculate the rolling mean. After you have calculated the mean average of the short and long windows, you should create a signal when the short moving average crosses the long moving average, but only for the period greater than the shortest moving average window. In Python, this will result in a condition: signals['short_mavg'][short_window:] > signals['long_mavg'][short_window:]. Note that you add the [short_window:] to comply with the condition “only for the period greater than the shortest moving average window”. When the condition is true, the initialized value 0.0 in the signal column will be overwritten with 1.0. A “signal” is created! If the condition is false, the original value of 0.0 will be kept and no signal is generated. You use the NumPy where() function to set up this condition. Much the same like you read just now, the variable to which you assign this result is signals['signal'][short_window], because you only want to create signals for the period greater than the shortest moving average window! Lastly, you take the difference of the signals in order to generate actual trading orders. In other words, in this column of your signals DataFrame, you’ll be able to distinguish between long and short positions, whether you’re buying or selling stock.
Rastaman4e / 1NICEHASH PLATFORM TERMS OF USE AND NICEHASH MINING TERMS OF SERVICE PLEASE READ THESE NICEHASH PLATFORM TERMS OF USE AND NICEHASH MINING TERMS OF SERVICE (“Terms”) CAREFULLY BEFORE USING THE THE PLATFORM OR SERVICES DESCRIBED HEREIN. BY SELECTING “I AGREE”, ACCESSING THE PLATFORM, USING NICEHASH MINING SERVICES OR DOWNLOADING OR USING NICEHASH MINING SOFTWARE, YOU ARE ACKNOWLEDGING THAT YOU HAVE READ THESE TERMS, AS AMENDED FROM TIME TO TIME, AND YOU ARE AGREEING TO BE BOUND BY THEM. IF YOU DO NOT AGREE TO THESE TERMS, OR ANY SUBSEQUENT AMENDMENTS, CHANGES OR UPDATES, DO NOT ACCESS THE PLATFORM, USE NICEHASH MINING SERVICES OR USE THE NICEHASH MINING SOFTWARE. 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Depending on your country of residence or incorporation or registered office, you may not be able to use all the functions of the NiceHash Platform or services provided therein. It is your responsibility to follow the rules and laws in your country of residence and/or country from which you access the NiceHash Platform. DEFINITIONS NiceHash Platform means a website located on the following web address: www.nicehash.com. NiceHash Mining Services mean all services provided by NiceHash, namely the provision of the NiceHash Platform, NiceHash Hashing power marketplace, NiceHash API, NiceHash OS, NiceHash Mining Software including licence for NiceHash Miner, NiceHash Private Endpoint, NiceHash Account, NiceHash mobile apps, and all other software products, applications and services associated with these products, except for the provision of NiceHash Exchange Services. NiceHash Exchange Service means a service which allows trading of digital assets in the form of digital tokens or cryptographic currency for our users by offering them a trading venue, helping them find a trading counterparty and providing the means for transaction execution. NiceHash Exchange Services are provided by NICEX Ltd and accessible at the NiceHash Platform under NiceHash Exchange Terms of Service. Hashing power marketplace means an infrastructure provided by the NiceHash which enables the Hashing power providers to point their rigs towards NiceHash stratum servers where Hashing power provided by different Hashing power providers is gathered and sold as generic Hashing power to the Hashing power buyers. Hashing power buyer means a legal entity or individual who buys the gathered and generic hashing power on the Hashing power marketplace from undefined Hashing power providers. Hashing power provider means a legal entity or individual who sells his hashing power on the Hashing power marketplace to undefined Hashing power buyers. NiceHash Mining Software means NiceHash Miner and any other software available via the NiceHash Platform. NiceHash Miner means a comprehensive software with graphical user interface and web interface, owned by NiceHash. NiceHash Miner is a process manager software which enables the Hashing power providers to point their rigs towards NiceHash stratum servers and sell their hashing power to the Hashing power buyers. NiceHash Miner also means any and all of its code, compilations, updates, upgrades, modifications, error corrections, patches and bug fixes and similar. NiceHash Miner does not mean third party software compatible with NiceHash Miner (Third Party Plugins and Miners). NiceHash QuickMiner means a software accessible at https://www.nicehash.com/quick-miner which enables Hashing power providers to point their PCs or rigs towards NiceHash stratum servers and sell their hashing power to the Hashing power buyers. NiceHash QuickMiner is intended as a tryout tool. Hashing power rig means all hardware which produces hashing power that represents computation power which is required to calculate the hash function of different type of cryptocurrency. Secondary account is an account managed by third party from which the Account holder deposits funds to his NiceHash Wallet or/and to which the Account holder withdraws funds from his NiceHash Wallet. Stratum is a lightweight mining protocol: https://slushpool.com/help/manual/stratum-protocol. NiceHash Account means an online account available on the NiceHash Platform and created by completing the registration procedure on the NiceHash Platform. Account holder means an individual or legal entity who completes the registration procedure and successfully creates the NiceHash Account. Minimum Age means 18 years old or older, if in order for NiceHash to lawfully provide the Services to you without parental consent (including using your personal data). NiceHash Wallet means a wallet created automatically for the Account holder and provided by the NiceHash Wallet provider. NiceHash does not hold funds on behalf of the Account holder but only transfers Account holder’s requests regarding the NiceHash Wallet transaction to the NiceHash Wallet provider who executes the requested transactions. In this respect NiceHash only processes and performs administrative services related to the payments regarding the NiceHash Mining Services and NiceHash Exchange Services, if applicable. NiceHash Wallet provider is a third party which on the behalf of the Account holder provides and manages the NiceHash Wallet, holds, stores and transfers funds and hosts NiceHash Wallet. For more information about the NiceHash Wallet provider, see the following website: https://www.bitgo.com/. Blockchain network is a distributed database that is used to maintain a continuously growing list of records, called blocks. Force Majeure Event means any governmental or relevant regulatory regulations, acts of God, war, riot, civil commotion, fire, flood, or any disaster or an industrial dispute of workers unrelated to you or NiceHash. Any act, event, omission, happening or non-happening will only be considered Force Majeure if it is not attributable to the wilful act, neglect or failure to take reasonable precautions of the affected party, its agents, employees, consultants, contractors and sub-contractors. SALE AND PURCHASE OF HASHING POWER Hashing power providers agree to sell and NiceHash agrees to proceed Hashing power buyers’ payments for the provided hashing power on the Hashing power marketplace, on the Terms set forth herein. According to the applicable principle get-paid-per-valid-share (pay as you go principle) Hashing power providers will be paid only for validated and accepted hashing power to their NiceHash Wallet or other wallet, as indicated in Account holder’s profile settings or in stratum connection username. In some cases, no Hashing power is sent to Hashing power buyers or is accepted by NiceHash Services, even if Hashing power is generated on the Hashing power rigs. These cases include usage of slower hardware as well as software, hardware or network errors. In these cases, Hashing power providers are not paid for such Hashing power. Hashing power buyers agree to purchase and NiceHash agrees to process the order and forward the purchased hashing power on the Hashing power marketplace, on the Terms set forth herein. According to the applicable principle pay-per-valid-share (pay as you go principle) Hashing power buyers will pay from their NiceHash Wallet only for the hashing power that was validated by our engine. When connection to the mining pool which is selected on the Hashing power order is lost or when an order is cancelled during its lifetime, Hashing power buyer pays for additional 10 seconds worth of hashing power. Hashing power order is charged for extra hashing power when mining pool which is selected on the Hashing power order, generates rapid mining work changes and/or rapid mining job switching. All payments including any fees will be processed in crypto currency and NiceHash does not provide an option to sale and purchase of the hashing power in fiat currency. RISK DISCLOSURE If you choose to use NiceHash Platform, Services and NiceHash Wallet, it is important that you remain aware of the risks involved, that you have adequate technical resources and knowledge to bear such risks and that you monitor your transactions carefully. General risk You understand that NiceHash Platform and Services, blockchain technology, Bitcoin, all other cryptocurrencies and cryptotokens, proof of work concept and other associated and related technologies are new and untested and outside of NiceHash’s control. You acknowledge that there are major risks associated with these technologies. In addition to the risks disclosed below, there are risks that NiceHash cannot foresee and it is unreasonable to believe that such risk could have been foreseeable. The performance of NiceHash’s obligation under these Terms will terminate if market or technology circumstances change to such an extent that (i) these Terms clearly no longer comply with NiceHash’s expectations, (ii) it would be unjust to enforce NiceHash’s obligations in the general opinion or (iii) NiceHash’s obligation becomes impossible. NiceHash Account abuse You acknowledge that there is risk associated with the NiceHash Account abuse and that you have been fully informed and warned about it. The funds stored in the NiceHash Wallet may be disposed by third party in case the third party obtains the Account holder’s login credentials. The Account holder shall protect his login credentials and his electronic devices where the login credentials are stored against unauthorized access. Regulatory risks You acknowledge that there is risk associated with future legislation which may restrict, limit or prohibit certain aspects of blockchain technology which may also result in restriction, limitation or prohibition of NiceHash Services and that you have been fully informed and warned about it. Risk of hacking You acknowledge that there is risk associated with hacking NiceHash Services and NiceHash Wallet and that you have been fully informed and warned about it. Hacker or other groups or organizations may attempt to interfere with NiceHash Services or NiceHash Wallet in any way, including without limitation denial of services attacks, Sybil attacks, spoofing, smurfing, malware attacks, mining attacks or consensus-based attacks. Cryptocurrency risk You acknowledge that there is risk associated with the cryptocurrencies which are used as payment method and that you have been fully informed and warned about it. Cryptocurrencies are prone to, but not limited to, value volatility, transaction costs and times uncertainty, lack of liquidity, availability, regulatory restrictions, policy changes and security risks. NiceHash Wallet risk You acknowledge that there is risk associated with funds held on the NiceHash Wallet and that you have been fully informed and warned about it. You acknowledge that NiceHash Wallet is provided by NiceHash Wallet provider and not NiceHash. You acknowledge and agree that NiceHash shall not be responsible for any NiceHash Wallet provider’s services, including their accuracy, completeness, timeliness, validity, copyright compliance, legality, decency, quality or any other aspect thereof. NiceHash does not assume and shall not have any liability or responsibility to you or any other person or entity for any Hash Wallet provider’s services. Hash Wallet provider’s services and links thereto are provided solely as a convenience to you and you access and use them entirely at your own risk and subject to NiceHash Wallet provider’s terms and conditions. Since the NiceHash Wallet is a cryptocurrency wallet all funds held on it are entirely uninsured in contrast to the funds held on the bank account or other financial institutions which are insured. Connection risk You acknowledge that there are risks associated with usage of NiceHash Services which are provided through the internet including, but not limited to, the failure of hardware, software, configuration and internet connections and that you have been fully informed and warned about it. You acknowledge that NiceHash will not be responsible for any configuration, connection or communication failures, disruptions, errors, distortions or delays you may experience when using NiceHash Services, however caused. Hashing power provision risk You acknowledge that there are risks associated with the provisions of the hashing power which is provided by the Hashing power providers through the Hashing power marketplace and that you have been fully informed and warned about it. You acknowledge that NiceHash does not provide the hashing power but only provides the Hashing power marketplace as a service. Hashing power providers’ Hashing power rigs are new and untested and outside of NiceHash’s control. There is a major risk that the Hashing power rigs (i) will stop providing hashing power, (ii) will provide hashing power in an unstable way, (iii) will be wrongly configured or (iv) provide insufficient speed of the hashing power. Hashing power rigs as hardware could be subject of damage, errors, electricity outage, misconfiguration, connection or communication failures and other malfunctions. NiceHash will not be responsible for operation of Hashing power rigs and its provision of hashing power. By submitting a Hashing power order you agree to Hashing power no-refund policy – all shares forwarded to mining pool, selected on the Hashing power order are final and non-refundable. Hashing power profitability risk You acknowledge that there is risk associated with the profitability of the hashing power provision and that you have been fully informed and warned about it. You acknowledge that all Hashing power rig’s earning estimates and profitability calculations on NiceHash Platform are only for informational purposes and were made based on the Hashing power rigs set up in the test environments. NiceHash does not warrant that your Hashing power rigs would achieve the same profitability or earnings as calculated on NiceHash Platform. There is risk that your Hashing power rig would not produce desired hashing power quantity and quality and that your produced hashing power would differentiate from the hashing power produced by our Hashing power rigs set up in the test environments. There is risk that your Hashing power rigs would not be as profitable as our Hashing power rigs set up in the test environments or would not be profitable at all. WARRANTIES NiceHash Platform and Mining Services are provided on the “AS IS” and “AS AVAILABLE” basis, including all faults and defects. To the maximum extent permitted by applicable law, NiceHash makes no representations and warranties and you waive all warranties of any kind. Particularly, without limiting the generality of the foregoing, the NiceHash makes no representations and warranties, whether express, implied, statutory or otherwise regarding NiceHash Platform and Mining Services or other services related to NiceHash Platform and provided by third parties, including any warranty that such services will be uninterrupted, harmless, secure or not corrupt or damaged, meet your requirements, achieve any intended results, be compatible or work with any other software, applications, systems or services, meet any performance or error free or that any errors or defects can or will be corrected. Additionally NiceHash makes no representations and warranties, whether express, implied, statutory or otherwise of merchantability, suitability, reliability, availability, timeliness, accuracy, satisfactory quality, fitness for a particular purpose or quality, title and non-infringement with respect to any of the Mining Services or other services related to NiceHash Platform and provided by third parties, or quiet enjoyment and any warranties arising out of any course of dealing, course of performance, trade practice or usage of NiceHash Platform and Mining Services including information, content and material contained therein. Especially NiceHash makes no representations and warranties, whether express, implied, statutory or otherwise regarding any payment services and systems, NiceHash Wallet which is provided by third party or any other financial services which might be related to the NiceHash Platform and Mining Services. You acknowledge that you do not rely on and have not been induced to accept the NiceHash Platform and Mining Services according to these Terms on the basis of any warranties, representations, covenants, undertakings or any other statement whatsoever, other than expressly set out in these Terms that neither the NiceHash nor any of its respective agents, officers, employees or advisers have given any such warranties, representations, covenants, undertakings or other statements. LIABILITY NiceHash and their respective officers, employees or agents will not be liable to you or anyone else, to the maximum extent permitted by applicable law, for any damages of any kind, including, but not limited to, direct, consequential, incidental, special or indirect damages (including but not limited to lost profits, trading losses or damages that result from use or loss of use of NiceHash Services or NiceHash Wallet), even if NiceHash has been advised of the possibility of such damages or losses, including, without limitation, from the use or attempted use of NiceHash Platform and Mining Services, NiceHash Wallet or other related websites or services. NiceHash does not assume any obligations to users in connection with the unlawful alienation of Bitcoins, which occurred on 6. 12. 2017 with NICEHASH, d. o. o., and has been fully reimbursed with the completion of the NiceHash Repayment Program. NiceHash will not be responsible for any compensation, reimbursement, or damages arising in connection with: (i) your inability to use the NiceHash Platform and Mining Services, including without limitation as a result of any termination or suspension of the NiceHash Platform or these Terms, power outages, maintenance, defects, system failures, mistakes, omissions, errors, defects, viruses, delays in operation or transmission or any failure of performance, (ii) the cost of procurement of substitute goods or services, (iii) any your investments, expenditures, or commitments in connection with these Terms or your use of or access to the NiceHash Platform and Mining Services, (iv) your reliance on any information obtained from NiceHash, (v) Force Majeure Event, communications failure, theft or other interruptions or (vi) any unauthorized access, alteration, deletion, destruction, damage, loss or failure to store any data, including records, private key or other credentials, associated with NiceHash Platform and Mining Services or NiceHash Wallet. Our aggregate liability (including our directors, members, employees and agents), whether in contract, warranty, tort (including negligence, whether active, passive or imputed), product liability, strict liability or other theory, arising out of or relating to the use of NiceHash Platform and Mining Services, or inability to use the Platform and Services under these Terms or under any other document or agreement executed and delivered in connection herewith or contemplated hereby, shall in any event not exceed 100 EUR per user. You will defend, indemnify, and hold NiceHash harmless and all respective employees, officers, directors, and representatives from and against any claims, demand, action, damages, loss, liabilities, costs and expenses (including reasonable attorney fees) arising out of or relating to (i) any third-party claim concerning these Terms, (ii) your use of, or conduct in connection with, NiceHash Platform and Mining Services, (iii) any feedback you provide, (iv) your violation of these Terms, (v) or your violation of any rights of any other person or entity. If you are obligated to indemnify us, we will have the right, in our sole discretion, to control any action or proceeding (at our expense) and determine whether we wish to settle it. If we are obligated to respond to a third-party subpoena or other compulsory legal order or process described above, you will also reimburse us for reasonable attorney fees, as well as our employees’ and contractors’ time and materials spent responding to the third-party subpoena or other compulsory legal order or process at reasonable hourly rates. The Services and the information, products, and services included in or available through the NiceHash Platform may include inaccuracies or typographical errors. Changes are periodically added to the information herein. Improvements or changes on the NiceHash Platform can be made at any time. NICEHASH ACCOUNT The registration of the NiceHash Account is made through the NiceHash Platform, where you are required to enter your email address and password in the registration form. After successful completion of registration, the confirmation email is sent to you. After you confirm your registration by clicking on the link in the confirmation email the NiceHash Account is created. NiceHash will send you proof of completed registration once the process is completed. When you create NiceHash Account, you agree to (i) create a strong password that you change frequently and do not use for any other website, (ii) implement reasonable and appropriate measures designed to secure access to any device which has access to your email address associated with your NiceHash Account and your username and password for your NiceHash Account, (iii) maintain the security of your NiceHash Account by protecting your password and by restricting access to your NiceHash Account; (iv) promptly notify us if you discover or otherwise suspect any security breaches related to your NiceHash Account so we can take all required and possible measures to secure your NiceHash Account and (v) take responsibility for all activities that occur under your NiceHash Account and accept all risks of any authorized or unauthorized access to your NiceHash Account, to the maximum extent permitted by law. Losing access to your email, registered at NiceHash Platform, may also mean losing access to your NiceHash Account. You may not be able to use the NiceHash Platform or Mining Services, execute withdrawals and other security sensitive operations until you regain access to your email address, registered at NiceHash Platform. If you wish to change the email address linked to your NiceHash Account, we may ask you to complete a KYC procedure for security purposes. This step serves solely for the purpose of identification in the process of regaining access to your NiceHash Account. Once the NiceHash Account is created a NiceHash Wallet is automatically created for the NiceHash Account when the request for the first deposit to the NiceHash Wallet is made by the user. Account holder’s NiceHash Wallet is generated by NiceHash Wallet provider. Account holder is strongly suggested to enhance the security of his NiceHash Account by adding an additional security step of Two-factor authentication (hereinafter “2FA”) when logging into his account, withdrawing funds from his NiceHash Wallet or placing a new order. Account holder can enable this security feature in the settings of his NiceHash Account. In the event of losing or changing 2FA code, we may ask the Account holder to complete a KYC procedure for security reasons. This step serves solely for the purpose of identification in the process of reactivating Account holders 2FA and it may be subject to an a In order to use certain functionalities of the NiceHash Platform, such as paying for the acquired hashing power, users must deposit funds to the NiceHash Wallet, as the payments for the hashing power could be made only through NiceHash Wallet. Hashing power providers have two options to get paid for the provided hashing power: (i) by using NiceHash Wallet to receive the payments or (ii) by providing other Bitcoin address where the payments shall be received to. Hashing power providers provide their Bitcoin address to NiceHash by providing such details via Account holder’s profile settings or in a form of a stratum username while connecting to NiceHash stratum servers. Account holder may load funds on his NiceHash Wallet from his Secondary account. Account holder may be charged fees by the Secondary account provider or by the blockchain network for such transaction. NiceHash is not responsible for any fees charged by Secondary account providers or by the blockchain network or for the management and security of the Secondary accounts. Account holder is solely responsible for his use of Secondary accounts and Account holder agrees to comply with all terms and conditions applicable to any Secondary accounts. The timing associated with a load transaction will depend in part upon the performance of Secondary accounts providers, the performance of blockchain network and performance of the NiceHash Wallet provider. NiceHash makes no guarantee regarding the amount of time it may take to load funds on to NiceHash Wallet. NiceHash Wallet shall not be used by Account holders to keep, save and hold funds for longer period and also not for executing other transactions which are not related to the transactions regarding the NiceHash Platform. The NiceHash Wallet shall be used exclusively and only for current and ongoing transactions regarding the NiceHash Platform. Account holders shall promptly withdraw any funds kept on the NiceHash Wallet that will not be used and are not intended for the reasons described earlier. Commission fees may be charged by the NiceHash Wallet provider, by the blockchain network or by NiceHash for any NiceHash Wallet transactions. Please refer to the NiceHash Platform, for more information about the commission fees for NiceHash Wallet transactions which are applicable at the time of the transaction. NiceHash reserves the right to change these commission fees according to the provisions to change these Terms at any time for any reason. You have the right to use the NiceHash Account only in compliance with these Terms and other commercial terms and principles published on the NiceHash Platform. In particular, you must observe all regulations aimed at ensuring the security of funds and financial transactions. Provided that the balance of funds in your NiceHash Wallet is greater than any minimum balance requirements needed to satisfy any of your open orders, you may withdraw from your NiceHash Wallet any amount of funds, up to the total amount of funds in your NiceHash Wallet in excess of such minimum balance requirements, to Secondary Account, less any applicable withdrawal fees charged by NiceHash or by the blockchain network for such transaction. Withdrawals are not processed instantly and may be grouped with other withdrawal requests. Some withdrawals may require additional verification information which you will have to provide in order to process the withdrawal. It may take up to 24 hours before withdrawal is fully processed and distributed to the Blockchain network. Please refer to the NiceHash Platform for more information about the withdrawal fees and withdrawal processing. NiceHash reserves the right to change these fees according to the provisions to change these Terms at any time for any reason. You have the right to close the NiceHash Account. In case you have funds on your NiceHash Wallet you should withdraw funds from your account prior to requesting NiceHash Account closure. After we receive your NiceHash Account closure request we will deactivate your NiceHash Account. You can read more about closing the NiceHash Account in our Privacy Policy. Your NiceHash Account may be deactivated due to your inactivity. Your NiceHash account may be locked and a mandatory KYC procedure is applied for security reasons, if it has been more than 6 month since your last login. NiceHash or any of its partners or affiliates are not responsible for the loss of the funds, stored on or transferred from the NiceHash Wallet, as well as for the erroneous implementation of the transactions made via NiceHash Wallet, where such loss or faulty implementation of the transaction are the result of a malfunction of the NiceHash Wallet and the malfunction was caused by you or the NiceHash Wallet provider. You are obliged to inform NiceHash in case of loss or theft, as well as in the case of any possible misuse of the access data to your NiceHash Account, without any delay, and demand change of access data or closure of your existing NiceHash Account and submit a request for new access data. NiceHash will execute the change of access data or closure of the NiceHash Account and the opening of new NiceHash Account as soon as technically possible and without any undue delay. All information pertaining to registration, including a registration form, generation of NiceHash Wallet and detailed instructions on the use of the NiceHash Account and NiceHash Wallet are available at NiceHash Platform. The registration form as well as the entire system is properly protected from unwanted interference by third parties. KYC PROCEDURE NiceHash is appropriately implementing AML/CTF and security measures to diligently detect and prevent any malicious or unlawful use of NiceHash Services or use, which is strictly prohibited by these Terms, which are deemed as your agreement to provide required personal information for identity verification. Security measures include a KYC procedure, which is aimed at determining the identity of an individual user or an organisation. We may ask you to complete this procedure before enabling some or all functionalities of the NiceHash platform and provide its services. A KYC procedure might be applied as a security measure when: changing the email address linked to your NiceHash Account, losing or changing your 2FA code; logging in to your NiceHash Account for the first time after the launch of the new NiceHash Platform in August 2019, gaining access to all or a portion of NiceHash Services, NiceHash Wallet and its related services or any portion thereof if they were disabled due to and activating your NiceHash Account if it has been deactivated due to its inactivity and/or security or other reasons. HASHING POWER TRANSACTIONS General NiceHash may, at any time and in our sole discretion, (i) refuse any order submitted or provided hashing power, (ii) cancel an order or part of the order before it is executed, (iii) impose limits on the order amount permitted or on provided hashing power or (iv) impose any other conditions or restrictions upon your use of the NiceHash Platform and Mining Services without prior notice. For example, but not limited to, NiceHash may limit the number of open orders that you may establish or limit the type of supported Hashing power rigs and mining algorithms or NiceHash may restrict submitting orders or providing hashing power from certain locations. Please refer to the NiceHash Platform, for more information about terminology, hashing power transactions’ definitions and descriptions, order types, order submission, order procedure, order rules and other restrictions and limitations of the hashing power transactions. NiceHash reserves the right to change any transaction, definitions, description, order types, procedure, rules, restrictions and limitations at any time for any reason. Orders, provision of hashing power, payments, deposits, withdrawals and other transactions are accepted only through the interface of the NiceHash Platform, NiceHash API and NiceHash Account and are fixed by the software and hardware tools of the NiceHash Platform. If you do not understand the meaning of any transaction option, NiceHash strongly encourages you not to utilize any of those options. Hashing Power Order In order to submit an Hashing Power Order via the NiceHash Account, the Hashing power buyer must have available funds in his NiceHash Wallet. Hashing power buyer submits a new order to buy hashing power via the NiceHash Platform or via the NiceHash API by setting the following parameters in the order form: NiceHash service server location, third-party mining pool, algorithm to use, order type, set amount he is willing to spend on this order, set price per hash he is willing to pay, optionally approximate limit maximum hashing power for his order and other parameters as requested and by confirming his order. Hashing power buyer may submit an order in maximum amount of funds available on his NiceHash Wallet at the time of order submission. Order run time is only approximate since order’s lifetime is based on the number of hashes that it delivers. Particularly during periods of high volume, illiquidity, fast movement or volatility in the marketplace for any digital assets or hashing power, the actual price per hash at which some of the orders are executed may be different from the prevailing price indicated on NiceHash Platform at the time of your order. You understand that NiceHash is not liable for any such price fluctuations. In the event of market disruption, NiceHash Services disruption, NiceHash Hashing Power Marketplace disruption or manipulation or Force Majeure Event, NiceHash may do one or more of the following: (i) suspend access to the NiceHash Account or NiceHash Platform, or (ii) prevent you from completing any actions in the NiceHash Account, including closing any open orders. Following any such event, when trading resumes, you acknowledge that prevailing market prices may differ significantly from the prices available prior to such event. When Hashing power buyer submits an order for purchasing of the Hashing power via NiceHash Platform or via the NiceHash API he authorizes NiceHash to execute the order on his behalf and for his account in accordance with such order. Hashing power buyer acknowledges and agrees that NiceHash is not acting as his broker, intermediary, agent or advisor or in any fiduciary capacity. NiceHash executes the order in set order amount minus NiceHash’s processing fee. Once the order is successfully submitted the order amount starts to decrease in real time according to the payments for the provided hashing power. Hashing power buyer agrees to pay applicable processing fee to NiceHash for provided services. The NiceHash’s fees are deducted from Hashing power buyer’s NiceHash Wallet once the whole order is exhausted and completed. Please refer to the NiceHash Platform, for more information about the fees which are applicable at the time of provision of services. NiceHash reserves the right to change these fees according to the provisions to change these Terms at any time for any reason. The changed fees will apply only for the NiceHash Services provided after the change of the fees. All orders submitted prior the fee change but not necessary completed prior the fee change will be charged according to the fees applicable at the time of the submission of the order. NiceHash will attempt, on a commercially reasonable basis, to execute the Hashing power buyer’s purchase of the hashing power on the Hashing power marketplace under these Terms according to the best-effort delivery approach. In this respect NiceHash does not guarantee that the hashing power will actually be delivered or verified and does not guarantee any quality of the NiceHash Services. Hashing power buyer may cancel a submitted order during order’s lifetime. If an order has been partially executed, Hashing power buyer may cancel the unexecuted remainder of the order. In this case the NiceHash’s processing fee will apply only for the partially executed order. NiceHash reserves the right to refuse any order cancellation request once the order has been submitted. Selling Hashing Power and the Provision of Hashing Power In order to submit the hashing power to the NiceHash stratum server the Hashing power provider must first point its Hashing power rig to the NiceHash stratum server. Hashing power provider is solely responsible for configuration of his Hashing power rig. The Hashing power provider gets paid by Hashing power buyers for all validated and accepted work that his Hashing power rig has produced. The provided hashing power is validated by NiceHash’s stratum engine and validator. Once the hashing power is validated the Hashing power provider is entitled to receive the payment for his work. NiceHash logs all validated hashing power which was submitted by the Hashing power provider. The Hashing power provider receives the payments of current globally weighted average price on to his NiceHash Wallet or his selected personal Bitcoin address. The payments are made periodically depending on the height of payments. NiceHash reserves the right to hold the payments any time and for any reason by indicating the reason, especially if the payments represent smaller values. Please refer to the NiceHash Platform, for more information about the height of payments for provided hashing power, how the current globally weighted average price is calculated, payment periods, payment conditions and conditions for detention of payments. NiceHash reserves the right to change this payment policy according to the provisions to change these Terms at any time for any reason. All Hashing power rig’s earnings and profitability calculations on NiceHash Platform are only for informational purposes. NiceHash does not warrant that your Hashing power rigs would achieve the same profitability or earnings as calculated on NiceHash Platform. You hereby acknowledge that it is possible that your Hashing power rigs would not be as profitable as indicated in our informational calculations or would not be profitable at all. Hashing power provider agrees to pay applicable processing fee to NiceHash for provided Services. The NiceHash’s fees are deducted from all the payments made to the Hashing power provider for his provided work. Please refer to the NiceHash Platform, for more information about the fees which are applicable at the time of provision of services. Hashing power provider which has not submitted any hashing power to the NiceHash stratum server for a period of 90 days agrees that a processing fee of 0.00001000 BTC or less, depending on the unpaid mining balance, will be deducted from his unpaid mining balance. NiceHash reserves the right to change these fees according to the provisions to change these Terms at any time for any reason. The changed fees will apply only for the NiceHash Services provided after the change of the fees. NiceHash will attempt, on a commercially reasonable basis, to execute the provision of Hashing power providers’ hashing power on the Hashing power marketplace under these Terms according to the best-effort delivery approach. In this respect NiceHash does not guarantee that the hashing power will actually be delivered or verified and does not guarantee any quality of the NiceHash Services. Hashing power provider may disconnect the Hashing power rig from the NiceHash stratum server any time. NiceHash reserves the right to refuse any Hashing power rig once the Hashing power rig has been pointed towards NiceHash stratum server. RESTRICTIONS When accessing the NiceHash Platform or using the Mining Services or NiceHash Wallet, you warrant and agree that you: will not use the Services for any purpose that is unlawful or prohibited by these Terms, will not violate any law, contract, intellectual property or other third-party right or commit a tort, are solely responsible for your conduct while accessing the NiceHash Platform or using the Mining Services or NiceHash Wallet, will not access the NiceHash Platform or use the Mining Services in any manner that could damage, disable, overburden, or impair the provision of the Services or interfere with any other party's use and enjoyment of the Services, will not misuse and/or maliciously use Hashing power rigs, you will particularly refrain from using network botnets or using NiceHash Platform or Mining Services with Hashing power rigs without the knowledge or awareness of Hashing power rig owner(s), will not perform or attempt to perform any kind of malicious attacks on blockchains with the use of the NiceHash Platform or Mining Services, intended to maliciously gain control of more than 50% of the network's mining hash rate, will not use the NiceHash Platform or Mining Services for any kind of market manipulation or disruption, such as but not limited to NiceHash Mining Services disruption and NiceHash Hashing Power Marketplace manipulation. In case of any of the above mentioned events, NiceHash reserves the right to immediately suspend your NiceHash Account, freeze or block the funds in the NiceHash Wallet, and suspend your access to NiceHash Platform, particularly if NiceHash believes that such NiceHash Account are in violation of these Terms or Privacy Policy, or any applicable laws and regulation. RIGHTS AND OBLIGATIONS In the event of disputes with you, NiceHash is obliged to prove that the NiceHash service which is the subject of the dispute was not influenced by technical or other failure. You will have possibility to check at any time, subject to technical availability, the transactions details, statistics and available balance of the funds held on the NiceHash Wallet, through access to the NiceHash Account. You may not obtain or attempt to obtain any materials or information through any means not intentionally made available or provided to you or public through the NiceHash Platform or Mining Services. We may, in our sole discretion, at any time, for any or no reason and without liability to you, with prior notice (i) terminate all rights and obligations between you and NiceHash derived from these Terms, (ii) suspend your access to all or a portion of NiceHash Services, NiceHash Wallet and its related services or any portion thereof and delete or deactivate your NiceHash Account and all related information and files in such account (iii) modify, suspend or discontinue, temporarily or permanently, any portion of NiceHash Platform or (iv) provide enhancements or improvements to the features and functionality of the NiceHash Platform, which may include patches, bug fixes, updates, upgrades and other modifications. Any such change may modify or delete certain portion, features or functionalities of the NiceHash Services. You agree that NiceHash has no obligation to (i) provide any updates, or (ii) continue to provide or enable any particular portion, features or functionalities of the NiceHash Services to you. You further agree that all changes will be (i) deemed to constitute an integral part of the NiceHash Platform, and (ii) subject to these Terms. In the event of your breach of these Terms, including but not limited to, for instance, in the event that you breach any term of these Terms, due to legal grounds originating in anti-money laundering and know your client regulation and procedures, or any other relevant applicable regulation, all right and obligations between you and NiceHash derived from these Terms terminate automatically if you fail to comply with these Terms within the notice period of 8 days after you have been warned by NiceHash about the breach and given 8 days period to cure the breaches. NiceHash reserves the right to keep these rights and obligations in force despite your breach of these Terms. In the event of termination, NiceHash will attempt to return you any funds stored on your NiceHash Wallet not otherwise owed to NiceHash, unless NiceHash believes you have committed fraud, negligence or other misconduct. You acknowledge that the NiceHash Services and NiceHash Wallet may be suspended for maintenance. Technical information about the hashing power transactions, including information about chosen server locations, algorithms used, selected mining pools, your business or activities, including all financial and technical information, specifications, technology together with all details of prices, current transaction performance and future business strategy represent confidential information and trade secrets. NiceHash shall, preserve the confidentiality of all before mentioned information and shall not disclose or cause or permit to be disclosed without your permission any of these information to any person save to the extent that such disclosure is strictly to enable you to perform or comply with any of your obligations under these Terms, or to the extent that there is an irresistible legal requirement on you or NiceHash to do so; or where the information has come into the public domain otherwise than through a breach of any of the terms of these Terms. NiceHash shall not be entitled to make use of any of these confidential information and trade secrets other than during the continuance of and pursuant to these Terms and then only for the purpose of carrying out its obligations pursuant to these Terms. NICEHASH MINER LICENSE (NICEHASH MINING SOFTWARE LICENSE) NiceHash Mining Software whether on disk, in read only memory, or any other media or in any other form is licensed, not sold, to you by NiceHash for use only under these Terms. NiceHash retains ownership of the NiceHash Mining Software itself and reserves all rights not expressly granted to you. Subject to these Terms, you are granted a limited, non-transferable, non-exclusive and a revocable license to download, install and use the NiceHash Mining Software. You may not distribute or make the NiceHash Mining Software available over a network where it could be used by multiple devices at the same time. You may not rent, lease, lend, sell, redistribute, assign, sublicense host, outsource, disclose or otherwise commercially exploit the NiceHash Mining Software or make it available to any third party. There is no license fee for the NiceHash Mining Software. NiceHash reserves the right to change the license fee policy according to the provisions to change these Terms any time and for any reason, including to decide to start charging the license fee for the NiceHash Mining Software. You are responsible for any and all applicable taxes. You may not, and you agree not to or enable others to, copy, decompile, reverse engineer, reverse compile, disassemble, attempt to derive the source code of, decrypt, modify, or create derivative works of the NiceHash Mining Software or any services provided by the NiceHash Mining Software, or any part thereof (except as and only to the extent any foregoing restriction is prohibited by applicable law or to the extent as may be permitted by the licensing terms governing use of open-sourced components included with the NiceHash Mining Software). If you choose to allow automatic updates, your device will periodically check with NiceHash for updates and upgrades to the NiceHash Mining Software and, if an update or upgrade is available, the update or upgrade will automatically download and install onto your device and, if applicable, your peripheral devices. You can turn off the automatic updates altogether at any time by changing the automatic updates settings found within the NiceHash Mining Software. You agree that NiceHash may collect and use technical and related information, including but not limited to technical information about your computer, system and application software, and peripherals, that is gathered periodically to facilitate the provision of software updates, product support and other services to you (if any) related to the NiceHash Mining Software and to verify compliance with these Terms. NiceHash may use this information, as long as it is in a form that does not personally identify you, to improve our NiceHash Services. NiceHash Mining Software contains features that rely upon information about your selected mining pools. You agree to our transmission, collection, maintenance, processing, and use of all information obtained from you about your selected mining pools. You can opt out at any time by going to settings in the NiceHash Mining Software. NiceHash may provide interest-based advertising to you. If you do not want to receive relevant ads in the NiceHash Mining Software, you can opt out at any time by going to settings in the NiceHash Mining Software. If you opt out, you will continue to receive the same number of ads, but they may be less relevant because they will not be based on your interest. NiceHash Mining Software license is effective until terminated. All provisions of these Terms regarding the termination apply also for the NiceHash Mining Software license. Upon the termination of NiceHash Mining Software license, you shall cease all use of the NiceHash Mining Software and destroy or delete all copies, full or partial, of the NiceHash Mining Software. THIRD PARTY MINERS AND PLUGINS Third Party Miners and Plugins are a third party software which enables the best and most efficient mining operations. NiceHash Miner integrates third party mining software using a third party miner plugin system. Third Party Mining Software is a closed source software which supports mining algorithms for cryptocurrencies and can be integrated into NiceHash Mining Software. Third Party Miner Plugin enables the connection between NiceHash Mining Software and Third Party Mining Software and it can be closed, as well as open sourced. NiceHash Mining Software user interface enables the user to manually select which available Third Party Miners and Plugins will be downloaded and integrated. Users can select or deselect Third Party Miners and Plugins found in the Plugin Manager window. Some of the available Third Party Miners and Plugins which are most common are preselected by NiceHash, but can be deselected, depending on users' needs. The details of the Third Party Miners and Plugins available for NiceHash Mining Software are accessible within the NiceHash Mining Software user interface. The details include, but not limited to, the author of the software and applicable license information, if applicable information about developer fee for Third Party Miners, software version etc. Developer fees may apply to the use of Third Party Miners and Plugins. NiceHash will not be liable, to the maximum extent permitted by applicable law, for any damages of any kind, including, but not limited to, direct, consequential, incidental, special or indirect damages, arising out of using Third Party Miners and Plugins. The latter includes, but is not limited to: i) any power outages, maintenance, defects, system failures, mistakes, omissions, errors, defects, viruses, delays in operation or transmission or any failure of performance; ii) any unauthorized access, alteration, deletion, destruction, damage, loss or failure to store any data, including records, private key or other credentials, associated with usage of Third Party Miners and Plugins and ii) Force Majeure Event, communications failure, theft or other interruptions. If you choose to allow automatic updates, your device will periodically check with NiceHash for updates and upgrades to the installed Third Party Miners and Plugins, if an update or upgrade is available, the update or upgrade will automatically download and install onto your device and, if applicable, your peripheral devices. You can turn off the automatic updates altogether at any time by changing the automatic updates settings found within the NiceHash Mining Software. NICEHASH QUICKMINER NiceHash QuickMiner is a software application that allows the visitors of the NiceHash Quick Miner web page, accessible athttps://www.nicehash.com/quick-miner, to connect their PC or a mining rig to the NiceHash Hashing Power Marketplace. Visitors of the NiceHash Quick Miner web page can try out and experience crypto currency mining without having to register on the NiceHash Platform and create a NiceHash Account. Users are encouraged to do so as soon as possible in order to collect the funds earned using NiceHash Quick Miner. Users can download NiceHash QuickMiner free of charge. In order to operate NiceHash QuickMiner software needs to automatically detect technical information about users' computer hardware. You agree that NiceHash may collect and use technical and related information. For more information please refer to NiceHash Privacy Policy. Funds arising from the usage of NiceHash QuickMiner are transferred to a dedicated cryptocurrency wallet owned and managed by NiceHash. NiceHash QuickMiner Users expressly agree and acknowledge that completing the registration process and creating a NiceHash Account is necessary in order to collect the funds arising from the usage of NiceHash QuickMiner. Users of NiceHash QuickMiner who do not successfully register a NiceHash Account will lose their right to claim funds arising from their usage of NiceHash QuickMiner. Those funds, in addition to the condition that the user has not been active on the NiceHash QuickMiner web page for consecutive 7 days, will be donated to the charity of choice. NICEHASH PRIVATE ENDPOINT NiceHash Private Endpoint is a network interface that connects users privately and securely to NiceHash Stratum servers. Private Endpoint uses a private IP address and avoids additional latency caused by DDOS protection. All NiceHash Private Mining Proxy servers are managed by NiceHash and kept up-to-date. Users can request a dedicated private access endpoint by filling in the form for NiceHash Private Endpoint Solution available at the NiceHash Platform. In the form the user specifies the email address, country, number of connections and locations and algorithms used. Based on the request NiceHash prepares an individualized offer based on the pricing stipulated on the NiceHash Platform, available at https://www.nicehash.com/private-endpoint-solution. NiceHash may request additional information from the users of the Private Endpoint Solution in order to determine whether we are obligated to collect VAT from you, including your VAT identification number. INTELLECTUAL PROPERTY NiceHash retains all copyright and other intellectual property rights, including inventions, discoveries, knowhow, processes, marks, methods, compositions, formulae, techniques, information and data, whether or not patentable, copyrightable or protectable in trademark, and any trademarks, copyrights or patents based thereon over all content and other materials contained on NiceHash Platform or provided in connection with the Services, including, without limitation, the NiceHash logo and all designs, text, graphics, pictures, information, data, software, source code, as well as the compilation thereof, sound files, other files and the selection and arrangement thereof. This material is protected by international copyright laws and other intellectual property right laws, namely trademark. These Terms shall not be understood and interpreted in a way that they would mean assignment of copyright or other intellectual property rights, unless it is explicitly defined so in these Terms. NiceHash hereby grants you a limited, nonexclusive and non-sublicensable license to access and use NiceHash’s copyrighted work and other intellectual property for your personal or internal business use. Such license is subject to these Terms and does not permit any resale, the distribution, public performance or public display, modifying or otherwise making any derivative uses, use, publishing, transmission, reverse engineering, participation in the transfer or sale, or any way exploit any of the copyrighted work and other intellectual property other than for their intended purposes. This granted license will automatically terminate if NiceHash suspends or terminates your access to the Services, NiceHash Wallet or closes your NiceHash Account. NiceHash will own exclusive rights, including all intellectual property rights, to any feedback including, but not limited to, suggestions, ideas or other information or materials regarding NiceHash Services or related products that you provide, whether by email, posting through our NiceHash Platform, NiceHash Account or otherwise and you irrevocably assign any and all intellectual property rights on such feedback unlimited in time, scope and territory. Any Feedback you submit is non-confidential and shall become the sole property of NiceHash. NiceHash will be entitled to the unrestricted use, modification or dissemination of such feedback for any purpose, commercial or otherwise, without acknowledgment or compensation to you. You waive any rights you may have to the feedback. We have the right to remove any posting you make on NiceHash Platform if, in our opinion, your post does not comply with the content standards defined by these Terms. PRIVACY POLICY Please refer to our NiceHash Platform and Mining Services Privacy Policy published on the NiceHash Platform for information about how we collect, use and share your information, as well as what options do you have with regards to your personal information. COMMUNICATION AND SUPPORT You agree and consent to receive electronically all communications, agreements, documents, receipts, notices and disclosures that NiceHash provides in connection with your NiceHash Account or use of the NiceHash Platform and Services. You agree that NiceHash may provide these communications to you by posting them via the NiceHash Account or by emailing them to you at the email address you provide. You should maintain copies of electronic communications by printing a paper copy or saving an electronic copy. It is your responsibility to keep your email address updated in the NiceHash Account so that NiceHash can communicate with you electronically. You understand and agree that if NiceHash sends you an electronic communication but you do not receive it because your email address is incorrect, out of date, blocked by your service provider, or you are otherwise unable to receive electronic communications, it will be deemed that you have been provided with the communication. You can update your NiceHash Account preferences at any time by logging into your NiceHash Account. If your email address becomes invalid such that electronic communications sent to you by NiceHash are returned, NiceHash may deem your account to be inactive and close it. You may give NiceHash a notice under these Terms by sending an email to support@nicehash.com or contact NiceHash through support located on the NiceHash Platform. All communication and notices pursuant to these Terms must be given in English language. FEES Please refer to the NiceHash Platform for more information about the fees or administrative costs which are applicable at the time of provision of services. NiceHash reserves the right to change these fees according to the provisions to change these Terms at any time for any reason. The changed fees will apply only for the Services provided after the change of the fees. You authorize us, or our designated payment processor, to charge or deduct your NiceHash Account for any applicable fees in connection with the transactions completed via the Services. TAX It is your responsibility to determine what, if any, taxes apply to the transactions you complete or services you provide via the NiceHash Platform, Mining Services and NiceHash Wallet, it is your responsibility to report and remit the correct tax to the appropriate tax authority and all your factual and potential tax obligations are your concern. You agree that NiceHash is not in any case and under no conditions responsible for determining whether taxes apply to your transactions or services or for collecting, reporting, withholding or remitting any taxes arising from any transactions or services. You also agree that NiceHash is not in any case and under no conditions bound to compensate for your tax obligation or give you any advice related to tax issues. All fees and charges payable by you to NiceHash are exclusive of any taxes, and shall certain taxes be applicable, they shall be added on top of the payable amounts. Upon our request, you will provide to us any information that we reasonably request to determine whether we are obligated to collect VAT from you, including your VAT identification number. If any deduction or withholding is required by law, you will notify NiceHash and will pay NiceHash any additional amounts necessary to ensure that the net amount received by NiceHash, after any deduction and withholding, equals the amount NiceHash would have received if no deduction or withholding had been required. Additionally, you will provide NiceHash with documentation showing that the withheld and deducted amounts have been paid to the relevant taxing authority. FINAL PROVISIONS Natural persons and legal entities that are not capable of holding legal rights and obligations are not allowed to create NiceHash Account and use NiceHash Platform or other related services. If NiceHash becomes aware that such natural person or legal entity has created the NiceHash Account or has used NiceHash Services, NiceHash will delete such NiceHash Account and disable any Services and block access to NiceHash Account and NiceHash Services to such natural person or legal entity. If you register to use the NiceHash Services on behalf of a legal entity, you represent and warrant that (i) such legal entity is duly organized and validly existing under the applicable laws of the jurisdiction of its organization; and (ii) you are duly authorized by such legal entity to act on its behalf. These Terms do not create any third-party beneficiary rights in any individual or entity. These Terms forms the entire agreement and understanding relating to the subject matter hereof and supersede any previous and contemporaneous agreements, arrangements or understandings relating to the subject matter hereof to the exclusion of any terms implied by law that may be excluded by contract. If at any time any provision of these Terms is or becomes illegal, invalid or unenforceable, the legality, validity and enforceability of every other provisions will not in any way be impaired. Such illegal, invalid or unenforceable provision of these Terms shall be deemed to be modified and replaced by such legal, valid and enforceable provision or arrangement, which corresponds as closely as possible to our and your will and business purpose pursued and reflected in these Terms. Headings of sections are for convenience only and shall not be used to limit or construe such sections. No failure to enforce nor delay in enforcing, on our side to the Terms, any right or legal remedy shall function as a waiver thereof, nor shall any individual or partial exercise of any right or legal remedy prevent any further or other enforcement of these rights or legal remedies or the enforcement of any other rights or legal remedies. NiceHash reserves the right to make changes, amendments, supplementations or modifications from time to time to these Terms including but not limited to changes of licence agreement for NiceHash Mining Software and of any fees and compensations policies, in its sole discretion and for any reason. We suggest that you review these Terms periodically for changes. If we make changes to these Terms, we will provide you with notice of such changes, such as by sending an email, providing notice on the NiceHash Platform, placing a popup window after login to the NiceHash Account or by posting the amended Terms on the NiceHash Platform and updating the date at the top of these Terms. The amended Terms will be deemed effective immediately upon posting for any new users of the NiceHash Services. In all other cases, the amended Terms will become effective for preexisting users upon the earlier of either: (i) the date users click or press a button to accept such changes in their NiceHash Account, or (ii) continued use of NiceHash Services 30 days after NiceHash provides notice of such changes. Any amended Terms will apply prospectively to use of the NiceHash Services after such changes become effective. The notice of change of these Terms is considered as notice of termination of all rights and obligations between you and NiceHash derived from these Terms with notice period of 30 days, if you do not accept the amended Terms. If you do not agree to any amended Terms, (i) the agreement between you and NiceHash is terminated by expiry of 30 days period which starts after NiceHash provides you a notice of change of these Terms, (ii) you must discontinue using NiceHash Services and (iii) you must inform us regarding your disagreement with the changes and request closure of your NiceHash Account. If you do not inform us regarding your disagreement and do not request closure of you NiceHash Account, we will deem that you agree with the changed Terms. You may not assign or transfer your rights or obligations under these Terms without the prior written consent of NiceHash. NiceHash may assign or transfer any or all of its rights under these Terms, in whole or in part, without obtaining your consent or approval. These Terms shall be governed by and construed and enforced in accordance with the Laws of the British Virgin Islands, and shall be interpreted in all respects as a British Virgin Islands contract. Any transaction, dispute, controversy, claim or action arising from or related to your access or use of the NiceHash Platform or these Terms of Service likewise shall be governed by the Laws of the British Virgin Islands, exclusive of choice-of-law principles. The rights and remedies conferred on NiceHash by, or pursuant to, these Terms are cumulative and are in addition, and without prejudice, to all other rights and remedies otherwise available to NiceHash at law. NiceHash may transfer its rights and obligations under these Terms to other entities which include, but are not limited to H-BIT, d.o.o. and NICEX Ltd, or any other firm or business entity that directly or indirectly acquires all or substantially all of the assets or business of NICEHASH Ltd. If you do not consent to any transfer, you may terminate this agreement and close your NiceHash Account. These Terms are not boilerplate. If you disagree with any of them, believe that any should not apply to you, or wish to negotiate these Terms, please contact NiceHash and immediately navigate away from the NiceHash Platform. Do not use the NiceHash Mining Services, NiceHash Wallet or other related services until you and NiceHash have agreed upon new terms of service. Last updated: March 1, 2021
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