Classification - 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.'

🤖 HealthCare ChatBot Major -1 (4th year - 7th semester) Health Care Chat-Bot is a Healthcare Domain Chatbot to simulate the predictions of a General Physician. ChatBot can be described as software that can chat with people using artificial intelligence. These software are used to perform tasks such as quickly responding to users, informing them, helping to purchase products and providing better service to customers. We have made a healthcare based chatbot. The three main areas where chatbots can be used are diagnostics, patient engagement outside medical facilities, and mental health. In our major we are working on diagnostic. 📃 Brief A chatbot is an artificially intelligent creature which can converse with humans. This could be text-based, or a spoken conversation. In our project we will be using Python as it is currently the most popular language for creating an AI chatbot. In the middle of AI chatbot, architecture is the Natural Language Processing (NLP) layer. This project aims to build an user-friendly healthcare chatbot which facilitates the job of a healthcare provider and helps improve their performance by interacting with users in a human-like way. Through chatbots one can communicate with text or voice interface and get reply through artificial intelligence Typically, a chat bot will communicate with a real person. Chat bots are used in applications such as E-commerce customer service, Call centres, Internet gaming,etc. Chatbots are programs built to automatically engage with received messages. Chatbots can be programmed to respond the same way each time, to respond differently to messages containing certain keywords and even to use machine learning to adapt their responses to fit the situation. A developing number of hospitals, nursing homes, and even private centres, presently utilize online Chatbots for human services on their sites. These bots connect with potential patients visiting the site, helping them discover specialists, booking their appointments, and getting them access to the correct treatment. In any case, the utilization of artificial intelligence in an industry where individuals’ lives could be in question, still starts misgivings in individuals. It brings up issues about whether the task mentioned above ought to be assigned to human staff. This healthcare chatbot system will help hospitals to provide healthcare support online 24 x 7, it answers deep as well as general questions. It also helps to generate leads and automatically delivers the information of leads to sales. By asking the questions in series it helps patients by guiding what exactly he/she is looking for. 📜 Problem Statement During the pandemic, it is more important than ever to get your regular check-ups and to continue to take prescription medications. The healthier you are, the more likely you are to recover quickly from an illness. In this time patients or health care workers within their practice, providers are deferring elective and preventive visits, such as annual physicals. For some, it is not possible to consult online. In this case, to avoid false information, our project can be of help. 📇 Features Register Screen. Sign-in Screen. Generates database for user login system. Offers you a GUI Based Chatbot for patients for diagnosing. [A pragmatic Approach for Diagnosis] Reccomends an appropriate doctor to you for the following symptom. 📜 Modules Used Our program uses a number of python modules to work properly: tkinter os webbrowser numpy pandas matplotlib 📃 Algorithm We have used Decision tree for our health care based chat bot. Decision Tree is a Supervised learning technique that can be used for both classification and Regression problems, but mostly it is preferred for solving Classification problems. It is a tree-structured classifier, where internal nodes represent the features of a dataset, branches represent the decision rules and each leaf node represents the outcome.It usually mimic human thinking ability while making a decision, so it is easy to understand. :suspect: Project Members Anushka Bansal - 500067844 - R164218014 Shreya Sharma - 500068573 - R164218070 Silvi - 500069092 - R164218072 Ishika Agrawal - 500071154 - R164218097

An ANN is a model based on a collection of connected units or nodes called "artificial neurons", which loosely model the neurons in a biological brain. Each connection, like the synapses in a biological brain, can transmit information, a "signal", from one artificial neuron to another. An artificial neuron that receives a signal can process it and then signal additional artificial neurons connected to it. In common ANN implementations, the signal at a connection between artificial neurons is a real number, and the output of each artificial neuron is computed by some non-linear function of the sum of its inputs. The connections between artificial neurons are called "edges". Artificial neurons and edges typically have a weight that adjusts as learning proceeds. The weight increases or decreases the strength of the signal at a connection. Artificial neurons may have a threshold such that the signal is only sent if the aggregate signal crosses that threshold. Typically, artificial neurons are aggregated into layers. Different layers may perform different kinds of transformations on their inputs. Signals travel from the first layer (the input layer) to the last layer (the output layer), possibly after traversing the layers multiple times. The original goal of the ANN approach was to solve problems in the same way that a human brain would. However, over time, attention moved to performing specific tasks, leading to deviations from biology. Artificial neural networks have been used on a variety of tasks, including computer vision, speech recognition, machine translation, social network filtering, playing board and video games and medical diagnosis. Deep learning consists of multiple hidden layers in an artificial neural network. This approach tries to model the way the human brain processes light and sound into vision and hearing. Some successful applications of deep learning are computer vision and speech recognition.[68] Decision trees Main article: Decision tree learning Decision tree learning uses a decision tree as a predictive model to go from observations about an item (represented in the branches) to conclusions about the item's target value (represented in the leaves). It is one of the predictive modeling approaches used in statistics, data mining, and machine learning. Tree models where the target variable can take a discrete set of values are called classification trees; in these tree structures, leaves represent class labels and branches represent conjunctions of features that lead to those class labels. Decision trees where the target variable can take continuous values (typically real numbers) are called regression trees. In decision analysis, a decision tree can be used to visually and explicitly represent decisions and decision making. In data mining, a decision tree describes data, but the resulting classification tree can be an input for decision making. Support vector machines Main article: Support vector machines Support vector machines (SVMs), also known as support vector networks, are a set of related supervised learning methods used for classification and regression. Given a set of training examples, each marked as belonging to one of two categories, an SVM training algorithm builds a model that predicts whether a new example falls into one category or the other.[69] An SVM training algorithm is a non-probabilistic, binary, linear classifier, although methods such as Platt scaling exist to use SVM in a probabilistic classification setting. In addition to performing linear classification, SVMs can efficiently perform a non-linear classification using what is called the kernel trick, implicitly mapping their inputs into high-dimensional feature spaces. Illustration of linear regression on a data set. Regression analysis Main article: Regression analysis Regression analysis encompasses a large variety of statistical methods to estimate the relationship between input variables and their associated features. Its most common form is linear regression, where a single line is drawn to best fit the given data according to a mathematical criterion such as ordinary least squares. The latter is often extended by regularization (mathematics) methods to mitigate overfitting and bias, as in ridge regression. When dealing with non-linear problems, go-to models include polynomial regression (for example, used for trendline fitting in Microsoft Excel[70]), logistic regression (often used in statistical classification) or even kernel regression, which introduces non-linearity by taking advantage of the kernel trick to implicitly map input variables to higher-dimensional space. Bayesian networks Main article: Bayesian network A simple Bayesian network. Rain influences whether the sprinkler is activated, and both rain and the sprinkler influence whether the grass is wet. A Bayesian network, belief network, or directed acyclic graphical model is a probabilistic graphical model that represents a set of random variables and their conditional independence with a directed acyclic graph (DAG). For example, a Bayesian network could represent the probabilistic relationships between diseases and symptoms. Given symptoms, the network can be used to compute the probabilities of the presence of various diseases. Efficient algorithms exist that perform inference and learning. Bayesian networks that model sequences of variables, like speech signals or protein sequences, are called dynamic Bayesian networks. Generalizations of Bayesian networks that can represent and solve decision problems under uncertainty are called influence diagrams. Genetic algorithms Main article: Genetic algorithm A genetic algorithm (GA) is a search algorithm and heuristic technique that mimics the process of natural selection, using methods such as mutation and crossover to generate new genotypes in the hope of finding good solutions to a given problem. In machine learning, genetic algorithms were used in the 1980s and 1990s.[71][72] Conversely, machine learning techniques have been used to improve the performance of genetic and evolutionary algorithms.[73] Training models Usually, machine learning models require a lot of data in order for them to perform well. Usually, when training a machine learning model, one needs to collect a large, representative sample of data from a training set. Data from the training set can be as varied as a corpus of text, a collection of images, and data collected from individual users of a service. Overfitting is something to watch out for when training a machine learning model. Federated learning Main article: Federated learning Federated learning is an adapted form of distributed artificial intelligence to training machine learning models that decentralizes the training process, allowing for users' privacy to be maintained by not needing to send their data to a centralized server. This also increases efficiency by decentralizing the training process to many devices. For example, Gboard uses federated machine learning to train search query prediction models on users' mobile phones without having to send individual searches back to Google.[74] Applications There are many applications for machine learning, including: Agriculture Anatomy Adaptive websites Affective computing Banking Bioinformatics Brain–machine interfaces Cheminformatics Citizen science Computer networks Computer vision Credit-card fraud detection Data quality DNA sequence classification Economics Financial market analysis[75] General game playing Handwriting recognition Information retrieval Insurance Internet fraud detection Linguistics Machine learning control Machine perception Machine translation Marketing Medical diagnosis Natural language processing Natural language understanding Online advertising Optimization Recommender systems Robot locomotion Search engines Sentiment analysis Sequence mining Software engineering Speech recognition Structural health monitoring Syntactic pattern recognition Telecommunication Theorem proving Time series forecasting User behavior analytics In 2006, the media-services provider Netflix held the first "Netflix Prize" competition to find a program to better predict user preferences and improve the accuracy of its existing Cinematch movie recommendation algorithm by at least 10%. A joint team made up of researchers from AT&T Labs-Research in collaboration with the teams Big Chaos and Pragmatic Theory built an ensemble model to win the Grand Prize in 2009 for $1 million.[76] Shortly after the prize was awarded, Netflix realized that viewers' ratings were not the best indicators of their viewing patterns ("everything is a recommendation") and they changed their recommendation engine accordingly.[77] In 2010 The Wall Street Journal wrote about the firm Rebellion Research and their use of machine learning to predict the financial crisis.[78] In 2012, co-founder of Sun Microsystems, Vinod Khosla, predicted that 80% of medical doctors' jobs would be lost in the next two decades to automated machine learning medical diagnostic software.[79] In 2014, it was reported that a machine learning algorithm had been applied in the field of art history to study fine art paintings and that it may have revealed previously unrecognized influences among artists.[80] In 2019 Springer Nature published the first research book created using machine learning.[81] Limitations Although machine learning has been transformative in some fields, machine-learning programs often fail to deliver expected results.[82][83][84] Reasons for this are numerous: lack of (suitable) data, lack of access to the data, data bias, privacy problems, badly chosen tasks and algorithms, wrong tools and people, lack of resources, and evaluation problems.[85] In 2018, a self-driving car from Uber failed to detect a pedestrian, who was killed after a collision.[86] Attempts to use machine learning in healthcare with the IBM Watson system failed to deliver even after years of time and billions of dollars invested.[87][88] Bias Main article: Algorithmic bias Machine learning approaches in particular can suffer from different data biases. A machine learning system trained on current customers only may not be able to predict the needs of new customer groups that are not represented in the training data. When trained on man-made data, machine learning is likely to pick up the same constitutional and unconscious biases already present in society.[89] Language models learned from data have been shown to contain human-like biases.[90][91] Machine learning systems used for criminal risk assessment have been found to be biased against black people.[92][93] In 2015, Google photos would often tag black people as gorillas,[94] and in 2018 this still was not well resolved, but Google reportedly was still using the workaround to remove all gorillas from the training data, and thus was not able to recognize real gorillas at all.[95] Similar issues with recognizing non-white people have been found in many other systems.[96] In 2016, Microsoft tested a chatbot that learned from Twitter, and it quickly picked up racist and sexist language.[97] Because of such challenges, the effective use of machine learning may take longer to be adopted in other domains.[98] Concern for fairness in machine learning, that is, reducing bias in machine learning and propelling its use for human good is increasingly expressed by artificial intelligence scientists, including Fei-Fei Li, who reminds engineers that "There’s nothing artificial about AI...It’s inspired by people, it’s created by people, and—most importantly—it impacts people. It is a powerful tool we are only just beginning to understand, and that is a profound responsibility.”[99] Model assessments Classification of machine learning models can be validated by accuracy estimation techniques like the holdout method, which splits the data in a training and test set (conventionally 2/3 training set and 1/3 test set designation) and evaluates the performance of the training model on the test set. In comparison, the K-fold-cross-validation method randomly partitions the data into K subsets and then K experiments are performed each respectively considering 1 subset for evaluation and the remaining K-1 subsets for training the model. In addition to the holdout and cross-validation methods, bootstrap, which samples n instances with replacement from the dataset, can be used to assess model accuracy.[100] In addition to overall accuracy, investigators frequently report sensitivity and specificity meaning True Positive Rate (TPR) and True Negative Rate (TNR) respectively. Similarly, investigators sometimes report the false positive rate (FPR) as well as the false negative rate (FNR). However, these rates are ratios that fail to reveal their numerators and denominators. The total operating characteristic (TOC) is an effective method to express a model's diagnostic ability. TOC shows the numerators and denominators of the previously mentioned rates, thus TOC provides more information than the commonly used receiver operating characteristic (ROC) and ROC's associated area under the curve (AUC).[101] Ethics Machine learning poses a host of ethical questions. Systems which are trained on datasets collected with biases may exhibit these biases upon use (algorithmic bias), thus digitizing cultural prejudices.[102] For example, using job hiring data from a firm with racist hiring policies may lead to a machine learning system duplicating the bias by scoring job applicants against similarity to previous successful applicants.[103][104] Responsible collection of data and documentation of algorithmic rules used by a system thus is a critical part of machine learning. Because human languages contain biases, machines trained on language corpora will necessarily also learn these biases.[105][106] Other forms of ethical challenges, not related to personal biases, are more seen in health care. There are concerns among health care professionals that these systems might not be designed in the public's interest but as income-generating machines. This is especially true in the United States where there is a long-standing ethical dilemma of improving health care, but also increasing profits. For example, the algorithms could be designed to provide patients with unnecessary tests or medication in which the algorithm's proprietary owners hold stakes. There is huge potential for machine learning in health care to provide professionals a great tool to diagnose, medicate, and even plan recovery paths for patients, but this will not happen until the personal biases mentioned previously, and these "greed" biases are addressed.[107] Hardware Since the 2010s, advances in both machine learning algorithms and computer hardware have led to more efficient methods for training deep neural networks (a particular narrow subdomain of machine learning) that contain many layers of non-linear hidden units.[108] By 2019, graphic processing units (GPUs), often with AI-specific enhancements, had displaced CPUs as the dominant method of training large-scale commercial cloud AI.[109] OpenAI estimated the hardware compute used in the largest deep learning projects from AlexNet (2012) to AlphaZero (2017), and found a 300,000-fold increase in the amount of compute required, with a doubling-time trendline of 3.4 months.[110][111] Software Software suites containing a variety of machine learning algorithms include the following: Free and open-source so

My notes for Prof. Klaus Obermayer's "Machine Intelligence 1 - Supervised Learning" course at the TU Berlin

Learn, understand, and implement deep neural networks in a math- and programming-friendly approach using Keras and Python. The book focuses on an end-to-end approach to developing supervised learning algorithms in regression and classification with practical business-centric use-cases implemented in Keras. The overall book comprises three sections with two chapters in each section. The first section prepares you with all the necessary basics to get started in deep learning. Chapter 1 introduces you to the world of deep learning and its difference from machine learning, the choices of frameworks for deep learning, and the Keras ecosystem. You will cover a real-life business problem that can be solved by supervised learning algorithms with deep neural networks. You’ll tackle one use case for regression and another for classification leveraging popular Kaggle datasets. Later, you will see an interesting and challenging part of deep learning: hyperparameter tuning; helping you further improve your models when building robust deep learning applications. Finally, you’ll further hone your skills in deep learning and cover areas of active development and research in deep learning. At the end of Learn Keras for Deep Neural Networks, you will have a thorough understanding of deep learning principles and have practical hands-on experience in developing enterprise-grade deep learning solutions in Keras.

Improving short-term prandial blood glucose outcomes for people with type 1 diabetes, a complex disease that affects nearly 10 million people worldwide. We aim to leverage semi-supervised learning to identify unlabelled meals in time-series blood glucose data, develop meal-scoring functions, and explore causal machine-learning techniques.

Machine learning using convolution neural network Required: raspberry pi pi cam compatibile rc car motor driver l293d Please create the respective files: forward idle left right reverse optimized_thetas This project aims to build an autonomous rc car using supervised learning of a neural network with a single hidden layer. We have not used any Machine Learning libraries since we wanted to implement the neural network from scratch to understand the concepts better. We will be referring the DC motor controlling the left/right direction as the front motor and the motor controlling the forward/reverse direction as the back motor. Connect the BACK_MOTOR_DATA_ONE and BACK_MOTOR_DATA_TWO GPIO pins(GPIO17 and GPIO27) of the Raspberry Pi to the Input pins for Motor 1(Input 1, Input 2) and the BACK_MOTOR_ENABLE_PIN GPIO pin(GPIO22) to the Enable pin for Motor 1(Enable 1,2) in the L293D Motor Driver IC. Connect the Output pins for Motor 1(Output 1, Output 2) of the IC to the back motor. Connect the FRONT_MOTOR_DATA_ONE and FRONT_MOTOR_DATA_TWO GPIO pins(GPIO19 and GPIO26) of the Raspberry Pi to the Input pins for Motor 2(Input 3, Input 4) in the IC. Connect the Output pins for Motor 2(Output 3, Output 4) of the IC to the front motor. The PWM_FREQUENCY and INITIAL_PWM_DUTY_CYCLE represent the initial frequency and duty cycle of the PWM output. We have created five class labels namely forward, reverse, left, right and idle and assigned their expected values. All class labels would require a folder of the same name to be present in the current directory. The input images resize to the dimension of the IMAGE_DIMENSION tuple value during training. The LAMBDA and HIDDEN_LAYER_SIZE values represent the default lambda value and the number of nodes in the hidden layer while training the neural network. All these values are configurable in configuration.py. The images for training are captured using interactive_control_train.py, the car is controlled using the direction arrows and all the images are recorded in the same folder along with the corresponding key press. After segregating the images into their corresponding class folders, the neural network is trained using train.py which takes two optional arguments - lambda and hidden layer size; default values would be those specified in the configuration file. At the command prompt, run the following command Once we have the trained model, the RC car is run autonomously using autonomous.py which takes an optional argument for the trained model; default will use the latest model in the optimized_thetas folder. Please feel free to post your doubts on code through my linkedin link: edin.com/in/shreyas-ramachandran-srinivasan-565638117/ CONTROLLING THE CAR The controlling process consists of 4 parts:  The sensor interface layer includes various programming modules worried about getting and time stamping all sensor information.  The discernment layer maps sensor information into inward models. The essential module in this layer is the PI camera, which decides the vehicle's introduction and area. Two distinct modules enable auto to explore in view of ultrasonic sensor and the camera. A street discovering module utilizes the PI camera determined pictures to discover the limit of a street, so the vehicle can focus itself along the side. At last, a surface evaluation module separates parameters of the present street to determine safe vehicle speeds.  The control layer is in charge of managing the controlling, throttle, and brake reaction of the vehicle. A key module is the way organizer, which sets the direction of the vehicle in controlling and speed space.  The vehicle interface layer fills in as the interface to the robot's drive-by-wire framework. It contains all interfaces to the vehicle's brakes, throttle, and controlling wheel. It likewise includes the interface to the vehicle's server, a circuit that manages the physical capacity to a significant number of the framework segments. In the proposed system, the raspberry Pi is used to control the L293D board, which allows motors to be controlled through the raspberry pi through the pulses provided by it. Based on the images obtained, raspberry pi provides PWM pulses tocontrol the L293D controller. L293D is a 16 Pin Motor Driver IC as shown in Figure 9. This is designed to provide bidirectional drive currents at voltages from 5 V to 36 V. Fig 9 L293D Breakout Board It also allows the speed of the motor to be controlled using PWM. It’s a series of high and low. The Duration of high and low determine the voltage supplied to the motor and hence the speed of the motor. PWM Signals: The DC motor speed all in all is specifically relative to the supply voltage, so if lessen the voltage from 9 volts to 4.5 volts, then our speed turn out to be half of what it initially had. Yet, for changing the speed of a dc motor we can't continue changing the supply voltage constantly. The speed controller PWM for a DC motor works by changing the normal voltage provided to the motor.The input signals we have given to PWM controller may be a simple or computerized motion as per the outline of the PWM controller. The PWM controller acknowledges the control flag and modifies the obligation cycle of the PWM motion as indicated by the prerequisites. In these waves frequency is same but the ON and OFF times are different. Recharge power bank of any capacity, here, 2800 mAH is used (operating voltage of 5V DC), can be used to provide supply to central microcontroller. The microcontroller used will separate and supply the required amount of power to each hardware component. This battery power pack is rechargeable and can get charged and used again and again.

The dataset of our work where the application of portable Raman spectroscopy coupled with several supervised machine-learning techniques, is used to discern between diabetic patients (DM2) and healthy controls (Ctrl), with a high degree of accuracy. This script pre-processes the spectra to reproduce Fig. 1 of our paper: Use of Raman Spectroscopy to Screen Diabetes Mellitus with Machine Learning Tools Edgar Guevara, Juan Carlos Torres-Galván, Miguel G. Ramírez-Elías, Claudia Luevano-Contreras and Francisco Javier González Biomedical Optics Express (2018) _______________________________________________________________________________ Copyright (C) 2018 Edgar Guevara, PhD CONACYT-Universidad Autónoma de San Luis Potosí Coordinación para la Innovación y Aplicación de la Ciencia y la Tecnología _______________________________________________________________________________

It is important that credit card companies are able to recognize fraudulent credit card transactions so that customers are not charged for items that they did not purchase. Content The dataset contains transactions made by credit cards in September 2013 by European cardholders. This dataset presents transactions that occurred in two days, where we have 492 frauds out of 284,807 transactions. The dataset is highly unbalanced, the positive class (frauds) account for 0.172% of all transactions. It contains only numerical input variables which are the result of a PCA transformation. Unfortunately, due to confidentiality issues, we cannot provide the original features and more background information about the data. Features V1, V2, … V28 are the principal components obtained with PCA, the only features which have not been transformed with PCA are 'Time' and 'Amount'. Feature 'Time' contains the seconds elapsed between each transaction and the first transaction in the dataset. The feature 'Amount' is the transaction Amount, this feature can be used for example-dependant cost-sensitive learning. Feature 'Class' is the response variable and it takes value 1 in case of fraud and 0 otherwise. Given the class imbalance ratio, we recommend measuring the accuracy using the Area Under the Precision-Recall Curve (AUPRC). Confusion matrix accuracy is not meaningful for unbalanced classification. Update (03/05/2021) A simulator for transaction data has been released as part of the practical handbook on Machine Learning for Credit Card Fraud Detection - https://fraud-detection-handbook.github.io/fraud-detection-handbook/Chapter_3_GettingStarted/SimulatedDataset.html. We invite all practitioners interested in fraud detection datasets to also check out this data simulator, and the methodologies for credit card fraud detection presented in the book. Acknowledgements The dataset has been collected and analysed during a research collaboration of Worldline and the Machine Learning Group (http://mlg.ulb.ac.be) of ULB (Université Libre de Bruxelles) on big data mining and fraud detection. More details on current and past projects on related topics are available on https://www.researchgate.net/project/Fraud-detection-5 and the page of the DefeatFraud project Please cite the following works: Andrea Dal Pozzolo, Olivier Caelen, Reid A. Johnson and Gianluca Bontempi. Calibrating Probability with Undersampling for Unbalanced Classification. In Symposium on Computational Intelligence and Data Mining (CIDM), IEEE, 2015 Dal Pozzolo, Andrea; Caelen, Olivier; Le Borgne, Yann-Ael; Waterschoot, Serge; Bontempi, Gianluca. Learned lessons in credit card fraud detection from a practitioner perspective, Expert systems with applications,41,10,4915-4928,2014, Pergamon Dal Pozzolo, Andrea; Boracchi, Giacomo; Caelen, Olivier; Alippi, Cesare; Bontempi, Gianluca. Credit card fraud detection: a realistic modeling and a novel learning strategy, IEEE transactions on neural networks and learning systems,29,8,3784-3797,2018,IEEE Dal Pozzolo, Andrea Adaptive Machine learning for credit card fraud detection ULB MLG PhD thesis (supervised by G. Bontempi) Carcillo, Fabrizio; Dal Pozzolo, Andrea; Le Borgne, Yann-Aël; Caelen, Olivier; Mazzer, Yannis; Bontempi, Gianluca. Scarff: a scalable framework for streaming credit card fraud detection with Spark, Information fusion,41, 182-194,2018,Elsevier Carcillo, Fabrizio; Le Borgne, Yann-Aël; Caelen, Olivier; Bontempi, Gianluca. Streaming active learning strategies for real-life credit card fraud detection: assessment and visualization, International Journal of Data Science and Analytics, 5,4,285-300,2018,Springer International Publishing Bertrand Lebichot, Yann-Aël Le Borgne, Liyun He, Frederic Oblé, Gianluca Bontempi Deep-Learning Domain Adaptation Techniques for Credit Cards Fraud Detection, INNSBDDL 2019: Recent Advances in Big Data and Deep Learning, pp 78-88, 2019 Fabrizio Carcillo, Yann-Aël Le Borgne, Olivier Caelen, Frederic Oblé, Gianluca Bontempi Combining Unsupervised and Supervised Learning in Credit Card Fraud Detection Information Sciences, 2019 Yann-Aël Le Borgne, Gianluca Bontempi Machine Learning for Credit Card Fraud Detection - Practical Handbook

The basis of this project involves analyzing Amgen future profitability based on its current business environment and financial performance. Technical Analysis, on the other hand, includes reading the charts and using statistical figures to identify the trends in the stock market. The dataset used for this analysis was downloaded from Yahoo finance for year 2009 to 2019. There are multiple variables in the dataset – date, open, high, low, volume. Adjusted close. The columns Open and Close represent the starting and final price at which the stock is traded on a day. High and Low represent the maximum, minimum price of the share for the day. The profit or loss calculation is usually determined by the closing price of a stock for the day, I used the adjusted closing price as the target variable. I downloaded data on the inflation rate, unemployment rate, Industrial Production Index, Consumer Price Index for All Urban Consumers: All Items and Real Gross Domestic Product as independent variables, Quarterly Financial Report: U.S. Corporations: Cash Dividends Charged to Retained Earnings All Manufacturing: All Nondurable Manufacturing: Chemicals: Pharmaceuticals and Medicines Industry, Producer Price Index by Industry: Pharmaceutical Preparation Manufacturing, 30-Year Treasury Constant Maturity Rate, and Producer Price Index by Industry: Pharmaceutical and Medicine Manufacturing Index. The independent variables are economic parameters which was obtained from Federal Reserve Economic Data (FRED) website. Methodology 1. Linear Regression: The linear regression model returns an equation that determines the relationship between the independent variables and the dependent variable. I used linear regression tool in Alteryx with ARIMA tool to forecast the stock prices for the year. The algorithm was trained with the historical data to see how the variables impact on the dependent variable. The test data was used to predict the adjusted closing price for the year and predicted a stock price of $193.38. 2. Support Vector Machines (SVM): Support Vector Networks (SVN), are a popular set of supervised learning algorithms originally developed for classification (categorical target) problems and can be used for regression (numerical target) problems. SVMs are memory efficient and can address many predictor variables. This model finds the best equation of one predictor, a plane (two predictors) or a hyperplane (three or more predictors) that maximally separates the groups of records, based on a measure of distance into different groups based on the target variable. A kernel function provides the measure of distance that causes to records to be placed in the same or different groups and involves taking a function of the predictor variables to define the distance metric. I used the SVM tool in Alteryx with ARIMA tool to forecast the stock prices for the year and predicted a stock price of $189.44. 3. Spline Model: The Spline Model tool was used because it provides the multivariate adaptive regression splines (or MARS) algorithm of Friedman. This statistical learning model self-determines which subset of fields best predict a target field of interest and can capture highly nonlinear relationships and interactions between fields. I used the Spline tool in Alteryx with ARIMA tool to forecast the stock prices for the year and predicted a stock price of $201.84. The results from the models was weighted by comparing the RMSE of each model. A lower RMSE indicates that the model’s predictions were closer to the actual values. However, a simpler model with the same RMSE as a more complex model is generally better, as simpler models are less likely to be overfit. Though the Spline model had a lower RMSE, the Linear Regression model had fewer variables. Thus, we combined the 3 models with the ARIMA forecast in a model ensemble, which allows us to use the results of multiple models. The forecasted stock price is $197.99 with 1.5% increase for 31st December 2019. Apart from economic parameters, stock price is affected by the news about the company and other factors like demonetization or merger/demerger of the companies. There are certain intangible factors which can often be impossible to predict beforehand hence the model predicts that the stock price of Amgen will continue to rise except there is a drastic downturn of the company.

Codes and models for "Semi-supervised machine-learning classification of materials synthesis procedures". (https://doi.org/10.1038/s41524-019-0204-1)

This course dives into the basics of machine learning using an approachable, and well-known programming language, Python. In this course, we will be reviewing two main components: First, you will be learning about the purpose of Machine Learning and where it applies to the real world. Second, you will get a general overview of Machine Learning topics such as supervised vs unsupervised learning, model evaluation, and Machine Learning algorithms. In this course, you practice with real-life examples of Machine learning and see how it affects society in ways you may not have guessed! By just putting in a few hours a week for the next few weeks, this is what you’ll get. 1) New skills to add to your resume, such as regression, classification, clustering, sci-kit learn and SciPy 2) New projects that you can add to your portfolio, including cancer detection, predicting economic trends, predicting customer churn, recommendation engines, and many more. 3) And a certificate in machine learning to prove your competency, and share it anywhere you like online or offline, such as LinkedIn profiles and social media. If you choose to take this course and earn the Coursera course certificate, you will also earn an IBM digital badge upon successful completion of the course.

A recommendation engine that uses supervised learning techniques to predict user’s future trips on the basis of their past trips and preferences. Uses Microsoft Azure to implement machine learning techniques to create a user-specific profile. The recommendation system is built on deep learning (neural networks with backpropagation). The neural network was trained to recommend house listings which coincided with the user’s traits. It is a Spring MVC application using MongoDB and Morphia. 1.3 million listings and user data were stored in MongoDB. The listings that were recommended to the users were fed into MongoDB and were displayed in the application using Google Maps API. Being a frequent Airbnb user myself, my wish for a more personalized experience for planning my future Airbnb trips inspired the concept behind this project.

In a needlework shop made for quantity result, an established curriculum should comply with certain principles and a timetable. The larger your embroidery procedure, the much more you need a defined training program. https://houstonembroideryservice.com/custom-patches/ Having your new-hires discover by "on-the-job osmosis" generally leads to irregular task abilities, an unforeseeable timespan to establish trainees and no chance to determine development and also retention. Extra notably, it does not offer your new employees their finest opportunities to stand out. I have handled big, multiple-shift embroidery stores and also found that having a well-known training educational program allowed me to determine where employees needed added direction. A great training program has actually a specified curriculum connected to a timetable. I such as to customize the program to fit my trial-period time frame, which normally is 90 days. At the end of this period, a competent candidate should have successfully finished the program and also have the ability to execute the custom name patches making skills recognized later in this article. EXPERIENCE LEVELS It may be alluring to hire a knowledgeable operator, and also lots of state work commissions currently include a group for embroidery equipment operators. Make sure to completely examine operators that have worked in various other huge shops. Why? Since some huge stores train operators in very details tasks and their general understanding may be limited. For instance, I when hired a seasoned operator from a shop that stitched for Ocean Pacific (OP) Apparel Corp. Nonetheless, when performing sewouts, I learned that she was uninformed that you might move the starting position of the hoop. At her previous shop, jobs were repeated and there was no demand to train particular skills. Still, you can find some excellent skill that might have just recently moved right into your location or a person returning to the workforce. For these reasons, consult your state work compensation. SELECTING A CANDIDATE While many managers look for candidates with sewing experience, remember that industrial stitching equipment drivers are made use of to sitting while working. Embroidery operators need to depend on their feet all the time, proactively moving the workplace. The candidate also must have good eyesight, be able to recognize shade and also be reasonably in shape. I've located a variety of good driver students by seeing their work habits in one more job setup. For instance, when I go to a lunch counter or coffee shop, I notice employees that rush, as well as have knowledge as well as a great perspective. They make fantastic prospects for learning brand-new skills that could result in possibly greater earnings. TRAINING PRINCIPLES When you construct your training program around the complying with ideas, your students will certainly proceed quicker and consistently. 1. The needlework equipment doesn't have a mind of its very own. Makers might occasionally malfunction as a result of an electric or electronic trouble, but such incidents are unusual. When a new trainee states, "I do not recognize why the machine did that," the instructor must respond in a mild way that the device probably did what the trainee advised it to do. This creates responsibility as opposed to advertising the idea that the equipment does strange and also unpredictable points by itself. 2. The needlework machine can harm you. Students, in addition to skilled drivers, need to have a healthy respect for the machine as well as recognize they could be harmed if safety treatments are not complied with. It's an ideal practice to train all drivers to loudly state "Ready" or "Clear" prior to the maker is engaged. This helps guarantee that no fingers are near the needles or in a location where they could be pinched when the pantograph relocations. 3. Mistakes will certainly take place. Stand up to the temptation to jump ahead of your planned training schedule. Doing so can bring about errors-- potentially pricey ones-- and even damage to the tools. When an error does inevitably occur, stay favorable. This is a fine line to stroll due to the fact that you do not want to cultivate the idea that errors are constantly OKAY, however it's also essential to not damage the trainee's morale. Rather, try to make the negative experience a mentor minute. Assist the student comprehend and verbalize what was learned from the experience. 4. Have students say it in their very own words. Lots of people say they comprehend a principle also when they don't. Have the student repeat your instructions for treatments in their very own words. This is a great means to reveal misunderstandings and also miscommunication. Even if you have actually created treatments, allow students to make their very own notes to help them bear in mind the necessary steps to fill a style, designate needles and also other unknown jobs. 5. Most of us do it the same way. Some huge stores have "set-up drivers" and "job operators." In such setups, even more skilled or extra very trained operators set up new tasks, while less-skilled drivers keep the equipment packed as well as threaded. No matter each worker's training, all operators have to comply with the exact same treatments. Even though every person is asked to comply with store standards, no person knows better than drivers where improvements can be made. If a staff member-- also a trainee-- believes a better means exists to do a job, that person ought to feel comfortable sharing it. If it actually is much better, the new approach should come to be basic shop treatment for all workers. APPLICATION It's vital that trainees have the ability to distinguish great as well as inadequate needlework. During the normal course of organization, collect needlework examples that have describes that are off-register, rugged column stitches as well as various other symptoms of inferior needlework. Ask trainees to evaluate these samples to develop their recognition of high-grade stitching. Begin trainees with easy jobs, like altering string for a brand-new task. Next off, progress to mentor tension essentials and also recognizing good needlework from bad embroidery. Make some brief videos of operations in your store and also publish them for either public or private watching on YouTube. This offers a twin function: Trainees will certainly learn from the video clips and also they can show their loved ones concerning their intriguing new task. When creating your training program, accumulate referral material from the Internet, publication short articles or various other relevant resources. Establish treatments for typical tasks and give written standards. ________________________________________. A Minimum Training Plan for Embroidery Machine Operators &amp; Supervisors. Listed here are the minimum elements that must be consisted of in a training program for drivers as well as for managers. Use this list as a guide, and also attach your own timespan as well as sequence that makes good sense for your store. At the end of your trial duration, utilize it as a checklist to evaluate the student's understanding of each element. You'll be pleased with the all-around and also experienced driver you have educated. Digital Embroidery Machine Operators. Student needs to get an explanation for each of the adhering to products and have the ability to carry out after ideal training time. 1) Understanding Placement Standards. a. How to apply your shop's typical embroidery positioning, such as left upper body or complete back. b. Selecting suitable strategies for marking garments when required. 2) Review of Job Details. a. Read orders for efficiency: string shades, design, placement. b. Ask for verification in the case of doubtful punctuation or instructions that don't appear right. 3) Garment Inspection. a. Counting garments. b. Checking for appropriate garments. c. Checking for defects before using embroidery. 4) Hooping. a. Select the smallest hoop that will certainly fit style. b. Exceptions to the guideline, such as maintaining bulky seams out of hoop location. c. Hooping procedures and also preventing damages to material from hooping. d. When to utilize holding fixtures rather than a standard hoop. 5) Matching Stabilizer to Fabrics. a. When to do a test sew-out for an initial post. b. Evaluate for appropriate support. c. Evaluate whether a topping is needed. 6) Assuring Consistent Placement. a. Determine positioning approach strategy for each and every work type. b. How to note garments. 7) Thread Handling. a. Setting up thread for basic work. b. Setting up threads for small quantities or combined color orders. c. Tying of knot to pull through needle for thread transition. d. Tying of knot for thread storage space, when relevant. e. Purpose of each element in the thread path (pre-tensioners, tensioners check springtime). f. How a stitch is created. g. How thread break detector/bobbin sensors work. h. Handling of metallics, polyesters as well as various other specialty strings. 8) Thread Tensions. a. Tension screening procedures (top and bottom). b. Troubleshooting tension problems. c. Adjusting and cleansing of the bobbin instance. d. Adjusting of the upper tensioners. 9) Needles. a. Matching the appropriate needle to items. b. How and when to alter needles. c. Identifying sewing signs and symptoms that are needle-related. 10) Troubleshooting as well as Machine Management. a. When and when not to back up the equipment to repair missing out on string. b. Identifying source of string breaks. c. Lubricating of the maker-- when, where, just how as well as with what. b. Sewing speeds for various tasks and also sew types. 11) Specialty Techniques. a. Producing premium needlework on completed caps. b. Producing appliqué products (if relevant). Needlework Supervisors (Multi-Machine Shops). 1) Pre-Production. a. Scheduling Principles. I. Matching job specifics for reliable consecutive work series. II. Assigning priorities according to assurance date. b. Procedures for purchasing digitized designs. c. Procedures for hosting upcoming orders. 2) Production. a. Sensible, organized job flow through store. b. Monitoring of supplies and also accessories. c. Matching operators to tasks and machines. d. Tracking of production throughout-- preserving a manufacturing log. e. Account daily or weekly losses and expense of nonconformity. 3) Equipment. a. Oversee upkeep. b. Keep a maintenance log for every machine. 4) Training. a. Organize as well as keep recommended reference product for operator students. b. Evaluate students' progression. c. Identify under-skilled drivers and offer aid.

The project is aimed at developing new tools for classifying videos of human-machine interactions in the Internet-of-Things (IOT) domain. Namely, given videos of humans interacting with IoT devices (e.e., smart appliances such as fridge, toaster, washing machines, Alexa, etc), the aim is to (1) design predictive video features, which (2) can be extracted efficiently in real-time to classify videos in terms of the activity being performed (opening or closing a fridge, loading or unloading a washing machine, etc.). The grand goal and motivation for the work is to generate labels for IoT network traffic, simply by training cameras onto IoT devices in the various IoT labs across US universities. Thus, the project aims to solve a main bottleneck in research at the intersection of Machine Learning and IoT, namely, the scarcity of labeled IoT traffic data to solve ML problems such as activity and anomaly detection using supervised or unsupervised detection procedures.

imbalanced-learn library, supervised learning, Scikit-learn machine learning library for Python (sklearn), supervised learning in linear models (linear regression & logistic regression), dataset split into training & testing datasets, Accuracy/Precision/sensitivity(recall)/F1 score, confusion matrix, SVM(Support Vector Machine – support vector, hyperplane), data preprocessing: - labelling (encoding – convert all text columns to numeric label), - Data Scale & Normalization (Standard scaler – mean = 0, variance = 1), Decision Trees, Ensemble Learning – Random Forest (weak/moderate/strong learner), Bootstrap Aggregation, Boosting [Adaptive boosting (AdaBoost), Gradient Boosting, for Class imbalance (solution 1: Oversampling (Random oversampling, synthetic minority oversampling technique (SMOTE)), solution 2: Undersampling (Random undersampling, Cluster Centroid undersampling, solution 3: SMOTEENN)

This repository contains a Gcode (NIST RS-274/ISO 6983-1:2009) based dataset for signature and anomaly based intrusion detection for 3D printers and CNC machines through supervised and semi supervised learning.

Module 1 – Introduction to Data Science: Introduction to fault-tolerant distributed file systems and computing. The whole data science process illustrated with industrial case-studies. A practical introduction to the scalable data processing to ingest, extract, load, transform, and explore (un)structured datasets. Scalable machine learning pipelines to model, train/fit, validate, select, tune, test, and predict or estimate in an unsupervised and supervised setting using nonparametric and partitioning methods such as random forests. Introduction to distributed vertex-programming.

Predict whether a mammogram mass is benign or malignant We'll be using the "mammographic masses" public dataset from the UCI repository (source: https://archive.ics.uci.edu/ml/datasets/Mammographic+Mass) This data contains 961 instances of masses detected in mammograms, and contains the following attributes: 1. BI-RADS assessment: 1 to 5 (ordinal) 2. Age: patient's age in years (integer) 3. Shape: mass shape: round=1 oval=2 lobular=3 irregular=4 (nominal) 4. Margin: mass margin: circumscribed=1 microlobulated=2 obscured=3 ill-defined=4 spiculated=5 (nominal) 5. Density: mass density high=1 iso=2 low=3 fat-containing=4 (ordinal) 6. Severity: benign=0 or malignant=1 (binominal) BI-RADS is an assesment of how confident the severity classification is; it is not a "predictive" attribute and so we will discard it. The age, shape, margin, and density attributes are the features that we will build our model with, and "severity" is the classification we will attempt to predict based on those attributes. Although "shape" and "margin" are nominal data types, which sklearn typically doesn't deal with well, they are close enough to ordinal that we shouldn't just discard them. The "shape" for example is ordered increasingly from round to irregular. A lot of unnecessary anguish and surgery arises from false positives arising from mammogram results. If we can build a better way to interpret them through supervised machine learning, it could improve a lot of lives. we will apply several different supervised machine learning techniques to this data set, and see which one yields the highest accuracy as measured with K-Fold cross validation (K=10). we will apply: * Decision tree * Random forest * KNN * Naive Bayes * SVM * Logistic Regression * And, as a bonus challenge, a neural network using Keras.

This project will investigate whether customer account information can be used to build machine learning models to successfully predict whether or not a customer will default on their next month's payment. This is a supervised learning problem; the data set has two different labels ((1=yes will default next month, 0=no will not default next month). I will evaluate the performance of six machine learning model frameworks to understand if the dataset has the ability to accurately predict whether a customer will default on their account in the next month. The six models used are Logistic Regression, KNN classifier, Bagging classifier, AdaBoost classifier, XGBoost classifier, and Random forest classifier. The hyperparameters will be be optimized and model performance will be evaluated using a confusion matrix, classification report (Precision, Recall, and F1), and Area under the Receiver operating characteristic curve (AUCROC).

Predict whether a mammogram mass is benign or malignant We'll be using the "mammographic masses" public dataset from the UCI repository (source: https://archive.ics.uci.edu/ml/datasets/Mammographic+Mass) This data contains 961 instances of masses detected in mammograms, and contains the following attributes: BI-RADS assessment: 1 to 5 (ordinal) Age: patient's age in years (integer) Shape: mass shape: round=1 oval=2 lobular=3 irregular=4 (nominal) Margin: mass margin: circumscribed=1 microlobulated=2 obscured=3 ill-defined=4 spiculated=5 (nominal) Density: mass density high=1 iso=2 low=3 fat-containing=4 (ordinal) Severity: benign=0 or malignant=1 (binominal) BI-RADS is an assesment of how confident the severity classification is; it is not a "predictive" attribute and so we will discard it. The age, shape, margin, and density attributes are the features that we will build our model with, and "severity" is the classification we will attempt to predict based on those attributes. Although "shape" and "margin" are nominal data types, which sklearn typically doesn't deal with well, they are close enough to ordinal that we shouldn't just discard them. The "shape" for example is ordered increasingly from round to irregular. A lot of unnecessary anguish and surgery arises from false positives arising from mammogram results. If we can build a better way to interpret them through supervised machine learning, it could improve a lot of lives.

1. Business Understanding In applying Machine Learning techniques to solve business problems, it is necessary to follow a project lifecycle in order to ensure that the model implemented is aligned with the objective and takes into consideration all the aspects of the business issue at hand. This lifecycle is typically the following: - Business Understanding - Data Understanding - Data Preparation - Modeling - Evaluation - Deployment However, this lifecycle cannot be considered as a linear process and, depending on the outcomes of each stage, it will be required from the analysts to go back to the previous stage and adapt their analysis until optimal results are obtained. For this reason, the business understanding part of the analysis is crucial, as it will set the objectives of the study and will enable to understand from a business perspective how each variable at hand can influence these objec-tives, how real-life events and phenomena might affect the outcome and how to account for the variability and unpre-dictability of financial and business data. In this study, we are attempting to solve the issue faced by financial institutions in assessing the likelihood of default of potential borrowers in order to take the right decision in approving or rejecting loan applications. Our business under-standing objective is to determine which of their clients will default or not on a loan. 5 This should be done through the analysis of data provided by clients or collected from historical performance. Variables such as the income of the borrower, the number of years they have been employed and their home ownership can provide information to firms on the financial strength of borrowers and whether or not they have collateral as an insurance against default and are important determinants of whether or not a borrower will default. Historical credit information are also crucial to determine the current likelihood of default, as patterns are most likely to be repeated. Hence, banks can use previous credit ratings and the number of loans already outstanding in order to determine if the borrower has been reliable in the past or if it is current overleveraged, and therefore likely to default on new loans. Finally, the nature, amount and term of the loan a borrower is applying for can also determine their likelihood of default as loans with a longer maturity, higher principal or for certain purposes can lead to higher default probabilities. All these variables should be taken into consideration and have a relationship with the default outcome of a borrower. Applying a supervised learning algorithm will allow us to use existing datasets containing this information and observe if and how they relate to the status of existing associated loans during the same timeframe as we would consider for future predictions. Therefore, we could train a model on existing historical data containing the previous feautures. This data would need to be collected from at least one period prior to our prediction date and equal or higher to the time needed for the variables to affect our target. The variables selected should always be available to us at the time where predictions would have to be done (hence at the time a loan is requested). As an example, the outstanding amounts on a loan or the number of loans already paid could not be used as predictors for default. Finally, the model trained would be evaluated on a test dataset for which the outcome is already known, in order to verify the accuracy of our predictions, before being deployed to predict default for future loans. Our data mining objec-tives are to predict with high accuracy and F1-Score the binary outcome of the model and the probability of default. Accuracy and F1-Score are most appropriate because both false positive and false negative values should be minimized as much as possible. Indeed, a bank would lose money by approving a loan to a defaulting customer, or by losing the business of a non-defaulting customer that would be falsly flagged as a defaulter. We will also attempt to maximize our ROC-AUC score, which is an important metric that represents the ability of a model to distinguish between different classes. The results of the evaluation and deployment stages might lead us to reassess the data understanding, preparation and modeling stages in order to fine-tune our results. Our analysis will start with the description and analysis of our dataset as developed in the next part.

Supervised Machine Learning : Classification - Parkinson Disease

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A comprehensive machine learning and deep learning pipeline for detecting insider threats using the CERT r4.1 dataset. This project combines unsupervised anomaly detection, supervised machine learning, and advanced deep learning architectures to identify anomalous user behavior in enterprise environments.

This Machine Learning repository encompasses theory, hands-on labs, and two projects. Project 1 analyzes customer segmentation for marketing using clustering, while Project 2 applies supervised classification in marketing and sales.

This repo contains all my work for Project 1 of Udacity's Machine Learning Basic Nano-degree Program. In this project, I applied supervised learning techniques and an analytical mind on data collected for the U.S. census to help CharityML (a fictitious charity organisation) identify people most likely to donate to their cause. I first explored the data to learn how the census data is recorded. Next, I applied a series of transformations and preprocessing techniques to manipulate the data into a workable format. Then I evaluated several supervised learners of my choice on the data, and considered which is best suited for the solution. Afterwards, I optimised the model I had selected and presented it as my solution to CharityML. Finally, I explored the chosen model and its predictions under the hood, to see just how well its performing when considering the data it has been given. Predicted selling price to the statistics.

Data Science and Analytics Internship at The Sparks Foundation This repository contains all the tasks for the Data Science and Analytics Intern at The Sparks Foundation. TASK-1 Improve our LinkedIn profile. TASK-2 To Explore Supervised Machine Learning In this regression task we will predict the percentage of marks that a student is expected to score based upon the number of hours they studied. This is a simple linear regression task as it involves just two variables. TASK-3 To Explore Unsupervised Machine Learning From the given ‘Iris’ dataset, predict the optimum number of clusters and represent it visually. TASK-4 To Explore Decision Tree Algorithm For the given ‘Iris’ dataset, create the Decision Tree classifier and visualize it graphically. The purpose is if we feed any new data to this classifier, it would be able to predict the right class accordingly. TASK-5 To explore Business Analytics Perform ‘Exploratory Data Analysis’ on the provided dataset ‘SampleSuperstore’. You are the business owner of the retail firm and want to see how your company is performing. You are interested in finding out the weak areas where you can work to make more profit. What all business problems you can derive by looking into the data? You can choose any of the tool of your choice (Python/R/Tableau/PowerBI/Excel)

his data contains 961 instances of masses detected in mammograms, and contains the following attributes: 1. BI-RADS assessment: 1 to 5 (ordinal) 2. Age: patient's age in years (integer) 3. Shape: mass shape: round=1 oval=2 lobular=3 irregular=4 (nominal) 4. Margin: mass margin: circumscribed=1 microlobulated=2 obscured=3 ill-defined=4 spiculated=5 (nominal) 5. Density: mass density high=1 iso=2 low=3 fat-containing=4 (ordinal) 6. Severity: benign=0 or malignant=1 (binominal) BI-RADS is an assesment of how confident the severity classification is; it is not a "predictive" attribute and so we will discard it. The age, shape, margin, and density attributes are the features that we will build our model with, and "severity" is the classification we will attempt to predict based on those attributes. Although "shape" and "margin" are nominal data types, which sklearn typically doesn't deal with well, they are close enough to ordinal that we shouldn't just discard them. The "shape" for example is ordered increasingly from round to irregular. A lot of unnecessary anguish and surgery arises from false positives arising from mammogram results. If we can build a better way to interpret them through supervised machine learning, it could improve a lot of lives.