The best data science and machine learning articles. Written by data scientist for data scientist (and business people)

Datathons

Flieber, Syrup Tech, and SupChains Launch AI Supply Chain Forecasting Competition
Flieber, Syrup Tech, and SupChains Announce Strategic Partnership to Launch the VN1 Forecasting Challenge to Drive AI Innovation in Supply Chain ManagementFlieber, Syrup Tech, and SupChains are thrilled to announce the launch of the VN1 Forecasting Challenge, a datathon aimed at revolutionizing AI-driven supply chain forecasting. Led by Nicolas Vandeput and hosted by the datathon platform Datasource.ai, this competition challenges participants to develop advanced predictive models for supply chain management, with $20,000 in prizes up for grabs for the global AI & Data Science communities.Empowering AI-Driven Supply Chain SolutionsThe VN1 Forecasting Challenge is a collaborative effort by Flieber, Syrup Tech, and SupChains to push the boundaries of AI in supply chain management. By leveraging their combined expertise, the challenge aims to uncover innovative solutions to enhance supply chain efficiency, reduce waste, and drive profitability."Supply chain operations need to evolve to keep up with the demands of modern marketplaces," said Fabricio Miranda, CEO of Flieber. "This challenge provides a platform for data scientists and AI experts to showcase their skills and contribute to the future of supply chain management."Participants will use historical sales, inventory, and pricing data to develop robust predictive models that can accurately forecast sales trends for various products across different clients and warehouses.The VN1 Forecasting Challenge boasts a total prize pool of $20,000, distributed among the top-performing AI scientists. This competition is perfect for showcasing your skills and pushing forecasting models to the limit, but it's also meant to be a learning playground. The competition starts on the 15th of August, 2014. Participants can already register here: https://www.datasource.ai:443/en/home/data-science-competitions-for-startups/vn1-forecasting-accuracy-challenge-phase-1/descriptionAbout the PartnersFlieber is a multichannel inventory planning platform designed for modern commerce. Founded in 2019 and serving hundreds of brands, agencies and aggregators, Flieber offers a suite of tools that allows operations teams to make better inventory decisions in a fraction of the time. Features include: AI-based demand forecasting, multi-node inventory forecasting, replenishment simulator, native integrations with the main sales channels and warehousing systems, among others.Syrup Tech specializes in AI-driven demand forecasting and inventory optimization for the apparel and footwear industry. Intelligent allocation and buying workflows empower brands to run their businesses more effectively by recommending predictive inventory actions that drive profitability and efficiency. Syrup helps omnichannel brands like Faherty, Salomon, and Desigual reduce overstocks and held inventory while improving ful-price sell-through.SupChains founded by Nicolas Vandeput, SupChains empowers supply chain leaders to deliver higher service levels, increase forecast accuracy, and lower inventory levels by training demand and supply planners and creating leading-edge inventory and forecasting models. With its content (books, webinars, and articles), SupChains is a key player in transforming supply chain practices.The datathon is hosted in Datasource.ai datathon platform.

Data Science

Data-Driven Creativity: Enhancing Video Content through Data Science
In the age of digital marketing and content creation, data-driven creativity is becoming an increasingly important concept. It's the fusion of artistic vision with the insights gleaned from data science to enhance the impact and effectiveness of video content. This 2500-word blog will explore how data science can be leveraged to elevate video content creation, ensuring that it not only engages but also resonates with the intended audience.Introduction to Data-Driven CreativityData-driven creativity marks a groundbreaking shift in video content creation, blending artistic vision with the insights provided by data science. This combination allows creators to break free from conventional creative limits, using data analytics to develop content that is both visually captivating and strategically significant. By delving into viewer behavior, preferences, and interactions, creators can refine their stories and visuals, achieving a deeper connection with their audience. This technique effectively transforms data into a guide for storytelling, steering content towards increased relevance and attractiveness. Consequently, video content becomes a more potent medium for engaging viewers and delivering impactful messages. Fundamentally, data-driven creativity is about converting data points into compelling stories and turning analytical insights into creative masterpieces, thereby redefining the standards of digital video content.Understanding the Role of Data in Video Content CreationExploring the Role of Data in Video Content Creation ventures into the rapidly growing realm of data-driven creativity, where data science emerges as a key instrument in enriching video content. In this realm, data transcends mere figures to become a narrative element, providing rich insights into what audiences prefer, how they behave, and emerging trends. Utilizing data, video creators can break free from conventional creative constraints, shaping their stories to more deeply connect with viewers. This process involves a detailed examination of viewer interactions, demographics, and feedback to hone storytelling skills, aiming to create videos that are not only watched but also emotionally impactful and memorable. Data-driven creativity is a fusion of art and science, where each view, reaction, and comment plays a role in directing the trajectory of video content, enhancing its relevance, engagement, and effect. This marks a transformative phase in content creation, where data equips creators to weave narratives that are not just creatively rich but also finely tuned to the dynamic preferences and interests of their audience.The Process of Gathering and Analyzing DataCollecting and analyzing data forms the foundation of data-driven creativity, especially in the realm of video content enhancement. This process involves the acquisition of key information, including audience demographics, interaction metrics, and performance measures, utilizing sophisticated tools and technologies. These range from social media analytics to advanced data mining applications designed to track a broad spectrum of viewer interactions. Once collected, this data undergoes thorough analysis to identify trends, preferences, and behaviors within the target audience. Such analysis equips content creators with insightful knowledge, allowing them to adjust their video content for greater appeal and connection with their audience. Leveraging these insights, creators can modify elements such as the tone, style, and themes of their content, revolutionizing storytelling methods and ensuring their content is both captivating and impactful. This integration of data science with creative storytelling heralds a transformative phase in video content production, where analytical findings significantly enhance artistic expression.Tailoring Content to Audience PreferencesAdapting content to audience preferences through data-driven creativity signifies a vital evolution in video content production. By incorporating data science, creators gain profound insights into audience behaviors, likes, and engagement patterns. This approach facilitates the creation of content that better resonates with viewers, ensuring everything from the plot to visual elements aligns with their interests. Utilizing analytics such as viewer habits and interaction rates, creators can pinpoint engaging aspects for better video content. Using a high-quality video editor tool is important to make the video look better. This knowledge allows precise adjustments, making the content not only captivating but also highly relevant. Ultimately, incorporating data in video content creation leads to more impactful and resonant viewer experiences, forging a deeper bond between the audience and the content.Enhancing Storytelling with Data InsightsUtilizing data insights to enhance storytelling is a groundbreaking method in video content production. Termed data-driven creativity, this technique blends the storytelling craft with data science accuracy. Content creators leverage analysis of viewer engagement, preferences, and behavior to fine-tune their narratives, ensuring a deeper connection with their audience. This integration results in not only engaging narratives but also ones that are in tune with audience interests and emerging trends. Insights from data grant a clearer understanding of what truly engages viewers, empowering creators to optimize their storytelling for the greatest effect. This modern approach reinvents traditional storytelling into an experience that's both more impactful and centered around the audience, with each creative decision being shaped and enriched by data.Using Data to Predict Future TrendsUtilizing data for future trends in data-driven creativity marks a revolutionary step in improving video content via data science. This technique focuses on analyzing viewer interactions, demographic information, and behavioral tendencies to predict future content direction. Using data enables creators to be proactive, crafting video content that resonates with emerging audience preferences and interests. Such a forward-thinking approach guarantees ongoing relevance in a dynamic digital world and fosters innovation and leadership among content creators. The blend of data analytics and artistic insight leads to the production of not just captivating but also pioneering videos, demonstrating the significant role of data in shaping the future of video content creation.Balancing Creativity and DataAchieving a harmonious blend of creativity and data in video content production is both subtle and potent. Data-driven creativity embodies the convergence of artistic flair and data analytics, providing an innovative method to boost video effectiveness. By weaving in data analysis, video creators unlock insights into what their audience prefers and how they behave, guiding their artistic choices. This integration results in content that is not only enthralling but also deeply meaningful to viewers. It is essential, however, to ensure that data serves as a guide, not a ruler, in the creative journey. This equilibrium keeps the content fresh and appealing while aligning it thoughtfully with data-driven knowledge. In essence, data-driven creativity in video content merges the narrative craft with analytical insights, culminating in videos that are both compelling and influential.Overcoming Challenges in Data-Driven CreativityOvercoming hurdles in data-driven creativity necessitates a nuanced integration of data science into the creation of video content. It involves striking a delicate balance between analytical methodologies and artistic expression, ensuring that data serves as an informative tool rather than a constraint on creativity. Accurate interpretation of data empowers content creators to avoid formulaic outputs, utilizing insights to enrich storytelling and enhance audience engagement. This intricate process demands a comprehensive understanding of the artistry of video creation and the scientific principles behind data analysis. Ethical considerations, including respecting audience privacy and obtaining data consent, are pivotal in this approach. Innovative strategies within data-driven creativity empower creators to produce content that forges deeper connections with viewers, setting new benchmarks in the digital landscape. Embracing these challenges is essential for unlocking the full potential of data-enhanced video content.Ethical Considerations in Data-Driven CreativityIn the domain of data-driven creativity, ethical considerations play a crucial role, especially when utilizing data science to enhance video content. While utilizing data insights can enhance creative processes, it is essential to address privacy concerns and ensure transparent, responsible data usage. Achieving the right equilibrium between creativity and ethical considerations becomes paramount as brands employ data to customize video content. Upholding user privacy and securing informed consent are fundamental principles in ethical data-driven creativity, fostering trust among audiences. Moreover, there is an obligation to avoid perpetuating biases and stereotypes in content creation, championing inclusivity and diversity. Ethical practices not only maintain brand integrity but also contribute to a positive and respectful digital environment for consumers.Tools and Resources for Data-Driven Video CreationExplore the potential of data-driven creativity using state-of-the-art tools and resources for crafting videos. In the current digital landscape, integrating data science and video content is transforming the landscape of creative processes. Immerse yourself in a domain where insights derived from data direct every facet of video production. These tools empower creators to customize content according to audience preferences, ensuring that each video is not only visually captivating but also strategically aligned. From scriptwriting informed by analytics to incorporating personalized visual elements, the utilization of data science takes video content to unprecedented levels. Delve into the crossroads of technology and creativity, where strategies driven by data redefine storytelling, captivating audiences in a personalized and meaningful manner.The Future of Data-Driven Creativity in Video ContentThe evolution of data-driven creativity in video content is set to transform our interaction with digital media. Through the incorporation of data science, creators gain valuable insights into viewer preferences, behavior, and trends. This collaboration enables a personalized and captivating viewing experience, heightening audience engagement. With the utilization of data-driven creativity, content producers can shape videos to suit the unique preferences of their target audience, resulting in more impactful storytelling and brand communication. As technology progresses, we anticipate a shift towards highly personalized content, driven by data insights, leading to innovative approaches in video production. This convergence of creativity and data science holds significant promise for the future development of video content within the digital landscape.ConclusionIn summary, the convergence of data-driven insights and creative components represents a transformative shift in the realm of video content creation. The fusion of Data Science and creativity provides content producers with the tools to precisely tailor videos to audience preferences, resulting in more impactful and engaging content. Leveraging the potential of data facilitates a deeper comprehension of viewer behavior, enabling targeted storytelling. Amidst the digital landscape, the symbiosis of data and creativity not only elevates video content but also fosters innovation and personalized experiences. Looking ahead, embracing Data-Driven Creativity becomes crucial for maintaining a leading edge in the continually evolving landscape of video content creation.

Machine Learning

The Role of AI in Unstructured Data Mining: Challenges and Opportunities
In our fast-paced digital world, we're producing staggering volumes of data every day. This data falls into two key categories: structured, known for its order and efficiency, and unstructured, a captivating puzzle brimming with untapped potential.In this article, we will uncover how AI confronts the complexities of unstructured data, the hurdles it faces, and the intriguing opportunities it opens up to businesses from any kind of industry.Understanding Unstructured DataUnstructured data mining is the technique of extracting valuable and meaningful insights from an abundant well of unstructured data. It uncovers hidden gems of knowledge, making it a crucial pursuit in our data-rich era.In today's digital realm, unstructured data is generated in unprecedented quantities. Billions of text documents, images, and videos come to life daily, creating a treasure trove of information just waiting for organizations to explore.Unlocking the insights hidden within unstructured data can provide organizations with a competitive edge. This data can reveal customer sentiments, emerging trends, and valuable feedback that might otherwise go unnoticed.The Basics of Data MiningHow data mining works is that it discovers patterns, trends, and valuable information within a dataset. It involves various techniques to extract knowledge from raw data. While it's exceptionally effective with structured data, applying data mining to unstructured data requires a unique set of skills and tools.Unstructured Data MiningUnstructured data mining is a method focused on the extraction of valuable information from the vast, unstructured data available. This process uncovers hidden insights, making it a valuable endeavor in today's data-driven world.The AI RevolutionThe AI revolution has given rise to an exciting era of possibilities in unstructured data mining. AI's remarkable capabilities are instrumental in taming the unstructured data landscape, and it involves a multitude of components, including:Machine learning enables AI systems to learn from data, make predictions, and identify patterns, enhancing data mining capabilities.Deep learning uses neural networks to model complex patterns in unstructured data, which is particularly valuable in image and speech recognition.Sentiment analysis gauges emotional tones within textual data, helping to understand public opinion and tailor strategies.Pattern recognition identifies recurring structures in data, aiding in image processing and text mining.Knowledge graphs structure data relationships, improving contextual understanding and data retrieval.Anomaly detection identifies outliers in data, which is essential for fraud detection and data security.Challenges in Unstructured Data MiningAs promising as AI is at handling unstructured data, it's not without its set of challenges. Here, we delve into some of the major hurdles:Data QualityUnstructured data is inherently messy. It's laden with errors, inconsistencies, and biases, which makes it a challenge to extract meaningful insights from this data. AI systems need to be trained rigorously to navigate and decipher this diversity in data quality. Techniques like data cleansing, normalization, and the use of context are essential in ensuring that AI systems provide accurate results.ScalabilityAs the volume of unstructured data grows, AI systems must scale to handle the data influx effectively. Traditional hardware and algorithms might not be sufficient to handle this data influx. Scalable infrastructure and distributed computing become crucial to ensuring that AI systems can process and analyze vast amounts of data efficiently.Privacy ConcernsMining unstructured data often raises ethical questions regarding privacy and data protection. That’s why it’s essential to strike the right balance between data utilization and respecting individual privacy. It's a challenge to ensure that AI systems are used responsibly and in compliance with data protection laws and regulations, such as GDPR in Europe. Techniques like anonymization and consent management play a vital role in addressing these privacy concerns.Opportunities and ApplicationsAI's role in unstructured data mining has opened up a world of opportunities across various industries. Let's explore some of the most promising applications:Customer InsightsUnstructured data, particularly sourced from social media and customer reviews, serves as a goldmine of information on customer behavior and preferences. By leveraging AI algorithms, companies can analyze sentiments, spot emerging trends, and even forecast future buying patterns. With these insights, they can fine-tune their marketing strategies, product development, and customer service to align with their ever-evolving audience's demands.Healthcare DiagnosisThe abundance of unstructured data found in medical records, radiological images, and wearable device data holds the key to transformative advancements. AI-powered systems, known for their proficiency in the analysis of this data, not only facilitate early disease detection but also provide highly individualized treatment plans, ultimately raising the standard of patient care. For example: AI expedites the process of analyzing medical images for anomalies, resulting in a significant reduction in the time required for diagnosing and treating severe conditions.Fraud DetectionWhen it comes to financial institutions, AI is a vital tool for exposing fraudulent activities that often hide within the vast volumes of unstructured transaction data. Through a meticulous examination of transaction patterns and anomalies, AI systems can rapidly pinpoint fraudulent actions, providing businesses with a robust defense against significant financial losses. The ability to detect and thwart fraud in real-time provides a critical advantage, resulting in annual savings of billions of dollars for businesses.ConclusionThe future belongs to those who embrace the AI revolution in unstructured data mining. In this future, data isn't just information; it's the key to success. So, let's move forward, embracing this tomorrow, where possibilities are limitless and opportunities are endless.

Data Science
Machine Learning

Model Evaluation Metrics in Machine Learning
CreditsPredictive models have become a trusted advisor to many businesses and for a good reason. These models can “foresee the future”, and there are many different methods available, meaning any industry can find one that fits their particular challenges.When we talk about predictive models, we are talking either about a regression model (continuous output) or a classification model (nominal or binary output). In classification problems, we use two types of algorithms (dependent on the kind of output it creates):Class output: Algorithms like SVM and KNN create a class output. For instance, in a binary classification problem, the outputs will be either 0 or 1. However, today we have algorithms that can convert these class outputs to probability.Probability output: Algorithms like Logistic Regression, Random Forest, Gradient Boosting, Adaboost, etc. give probability outputs. Converting probability outputs to class output is just a matter of creating a threshold probability.IntroductionWhile data preparation and training a machine learning model is a key step in the machine learning pipeline, it’s equally important to measure the performance of this trained model. How well the model generalizes on the unseen data is what defines adaptive vs non-adaptive machine learning models.By using different metrics for performance evaluation, we should be in a position to improve the overall predictive power of our model before we roll it out for production on unseen data.Without doing a proper evaluation of the ML model using different metrics, and depending only on accuracy, it can lead to a problem when the respective model is deployed on unseen data and can result in poor predictions.This happens because, in cases like these, our models don’t learn but instead memorize;hence, they cannot generalize well on unseen data.Model Evaluation MetricsLet us now define the evaluation metrics for evaluating the performance of a machine learning model, which is an integral component of any data science project. It aims to estimate the generalization accuracy of a model on the future (unseen/out-of-sample) data.Confusion MatrixA confusion matrix is a matrix representation of the prediction results of any binary testing that is often used to describe the performance of the classification model (or “classifier”) on a set of test data for which the true values are known.The confusion matrix itself is relatively simple to understand, but the related terminology can be confusing.Confusion matrix with 2 class labels.Each prediction can be one of the four outcomes, based on how it matches up to the actual value:True Positive (TP): Predicted True and True in reality.True Negative (TN): Predicted False and False in reality.False Positive (FP): Predicted True and False in reality.False Negative (FN): Predicted False and True in reality.Now let us understand this concept using hypothesis testing.A Hypothesis is speculation or theory based on insufficient evidence that lends itself to further testing and experimentation. With further testing, a hypothesis can usually be proven true or false.A Null Hypothesis is a hypothesis that says there is no statistical significance between the two variables in the hypothesis. It is the hypothesis that the researcher is trying to disprove.We would always reject the null hypothesis when it is false, and we would accept the null hypothesis when it is indeed true.Even though hypothesis tests are meant to be reliable, there are two types of errors that can occur.These errors are known as Type 1 and Type II errors.For example, when examining the effectiveness of a drug, the null hypothesis would be that the drug does not affect a disease.Type I Error:- equivalent to False Positives(FP).The first kind of error that is possible involves the rejection of a null hypothesis that is true.Let’s go back to the example of a drug being used to treat a disease. If we reject the null hypothesis in this situation, then we claim that the drug does have some effect on a disease. But if the null hypothesis is true, then, in reality, the drug does not combat the disease at all. The drug is falsely claimed to have a positive effect on a disease.Type II Error:- equivalent to False Negatives(FN).The other kind of error that occurs when we accept a false null hypothesis. This sort of error is called a type II error and is also referred to as an error of the second kind.If we think back again to the scenario in which we are testing a drug, what would a type II error look like? A type II error would occur if we accepted that the drug hs no effect on disease, but in reality, it did.A sample python implementation of the Confusion matrix.import warnings import pandas as pd from sklearn import model_selection from sklearn.linear_model import LogisticRegression from sklearn.metrics import confusion_matrix import matplotlib.pyplot as plt %matplotlib inline #ignore warnings warnings.filterwarnings('ignore') # Load digits dataset url = "http://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data" df = pd.read_csv(url) # df = df.values X = df.iloc[:,0:4] y = df.iloc[:,4] #test size test_size = 0.33 #generate the same set of random numbers seed = 7 #Split data into train and test set. X_train, X_test, y_train, y_test = model_selection.train_test_split(X, y, test_size=test_size, random_state=seed) #Train Model model = LogisticRegression() model.fit(X_train, y_train) pred = model.predict(X_test) #Construct the Confusion Matrix labels = ['Iris-setosa', 'Iris-versicolor', 'Iris-virginica'] cm = confusion_matrix(y_test, pred, labels) print(cm) fig = plt.figure() ax = fig.add_subplot(111) cax = ax.matshow(cm) plt.title('Confusion matrix') fig.colorbar(cax) ax.set_xticklabels([''] + labels) ax.set_yticklabels([''] + labels) plt.xlabel('Predicted Values') plt.ylabel('Actual Values') plt.show()Confusion matrix with 3 class labels.The diagonal elements represent the number of points for which the predicted label is equal to the true label, while anything off the diagonal was mislabeled by the classifier. Therefore, the higher the diagonal values of the confusion matrix the better, indicating many correct predictions.In our case, the classifier predicted all the 13 setosa and 18 virginica plants in the test data perfectly. However, it incorrectly classified 4 of the versicolor plants as virginica.There is also a list of rates that are often computed from a confusion matrix for a binary classifier:1. AccuracyOverall, how often is the classifier correct?Accuracy = (TP+TN)/totalWhen our classes are roughly equal in size, we can use accuracy, which will give us correctly classified values.Accuracy is a common evaluation metric for classification problems. It’s the number of correct predictions made as a ratio of all predictions made.Misclassification Rate(Error Rate): Overall, how often is it wrong. Since accuracy is the percent we correctly classified (success rate), it follows that our error rate (the percentage we got wrong) can be calculated as follows:Misclassification Rate = (FP+FN)/totalWe use the sklearn module to compute the accuracy of a classification task, as shown below.#import modules import warnings import pandas as pd import numpy as np from sklearn import model_selection from sklearn.linear_model import LogisticRegression from sklearn import datasets from sklearn.metrics import accuracy_score #ignore warnings warnings.filterwarnings('ignore') # Load digits dataset iris = datasets.load_iris() # # Create feature matrix X = iris.data # Create target vector y = iris.target #test size test_size = 0.33 #generate the same set of random numbers seed = 7 #cross-validation settings kfold = model_selection.KFold(n_splits=10, random_state=seed) #Model instance model = LogisticRegression() #Evaluate model performance scoring = 'accuracy' results = model_selection.cross_val_score(model, X, y, cv=kfold, scoring=scoring) print('Accuracy -val set: %.2f%% (%.2f)' % (results.mean()*100, results.std())) #split data X_train, X_test, y_train, y_test = model_selection.train_test_split(X, y, test_size=test_size, random_state=seed) #fit model model.fit(X_train, y_train) #accuracy on test set result = model.score(X_test, y_test) print("Accuracy - test set: %.2f%%" % (result*100.0))The classification accuracy is 88% on the validation set.2. PrecisionWhen it predicts yes, how often is it correct?Precision=TP/predicted yesWhen we have a class imbalance, accuracy can become an unreliable metric for measuring our performance. For instance, if we had a 99/1 split between two classes, A and B, where the rare event, B, is our positive class, we could build a model that was 99% accurate by just saying everything belonged to class A. Clearly, we shouldn’t bother building a model if it doesn’t do anything to identify class B; thus, we need different metrics that will discourage this behavior. For this, we use precision and recall instead of accuracy.3. Recall or SensitivityWhen it’s actually yes, how often does it predict yes?True Positive Rate = TP/actual yesRecall gives us the true positive rate (TPR), which is the ratio of true positives to everything positive.In the case of the 99/1 split between classes A and B, the model that classifies everything as A would have a recall of 0% for the positive class, B (precision would be undefined — 0/0). Precision and recall provide a better way of evaluating model performance in the face of a class imbalance. They will correctly tell us that the model has little value for our use case.Just like accuracy, both precision and recall are easy to compute and understand but require thresholds. Besides, precision and recall only consider half of the confusion matrix:4. F1 ScoreThe F1 score is the harmonic mean of the precision and recall, where an F1 score reaches its best value at 1 (perfect precision and recall) and worst at 0.Why harmonic mean? Since the harmonic mean of a list of numbers skews strongly toward the least elements of the list, it tends (compared to the arithmetic mean) to mitigate the impact of large outliers and aggravate the impact of small ones.An F1 score punishes extreme values more. Ideally, an F1 Score could be an effective evaluation metric in the following classification scenarios:When FP and FN are equally costly — meaning they miss on true positives or find false positives — both impact the model almost the same way, as in our cancer detection classification exampleAdding more data doesn’t effectively change the outcome effectivelyTN is high (like with flood predictions, cancer predictions, etc.)A sample python implementation of the F1 score.import warnings import pandas from sklearn import model_selection from sklearn.linear_model import LogisticRegression from sklearn.metrics import log_loss from sklearn.metrics import precision_recall_fscore_support as score, precision_score, recall_score, f1_score warnings.filterwarnings('ignore') url = "https://raw.githubusercontent.com/jbrownlee/Datasets/master/pima-indians-diabetes.data.csv" dataframe = pandas.read_csv(url) dat = dataframe.values X = dat[:,:-1] y = dat[:,-1] test_size = 0.33 seed = 7 model = LogisticRegression() #split data X_train, X_test, y_train, y_test = model_selection.train_test_split(X, y, test_size=test_size, random_state=seed) model.fit(X_train, y_train) precision = precision_score(y_test, pred) print('Precision: %f' % precision) # recall: tp / (tp + fn) recall = recall_score(y_test, pred) print('Recall: %f' % recall) # f1: tp / (tp + fp + fn) f1 = f1_score(y_test, pred) print('F1 score: %f' % f1)5. SpecificityWhen it’s no, how often does it predict no?True Negative Rate=TN/actual noIt is the true negative rate or the proportion of true negatives to everything that should have been classified as negative.Note that, together, specificity and sensitivity consider the full confusion matrix:6. Receiver Operating Characteristics (ROC) CurveMeasuring the area under the ROC curve is also a very useful method for evaluating a model. By plotting the true positive rate (sensitivity) versus the false-positive rate (1 — specificity), we get the Receiver Operating Characteristic (ROC) curve. This curve allows us to visualize the trade-off between the true positive rate and the false positive rate.The following are examples of good ROC curves. The dashed line would be random guessing (no predictive value) and is used as a baseline; anything below that is considered worse than guessing. We want to be toward the top-left corner:A sample python implementation of the ROC curves.#Classification Area under curve import warnings import pandas from sklearn import model_selection from sklearn.linear_model import LogisticRegression from sklearn.metrics import roc_auc_score, roc_curve warnings.filterwarnings('ignore') url = "https://raw.githubusercontent.com/jbrownlee/Datasets/master/pima-indians-diabetes.data.csv" dataframe = pandas.read_csv(url) dat = dataframe.values X = dat[:,:-1] y = dat[:,-1] seed = 7 #split data X_train, X_test, y_train, y_test = model_selection.train_test_split(X, y, test_size=test_size, random_state=seed) model.fit(X_train, y_train) # predict probabilities probs = model.predict_proba(X_test) # keep probabilities for the positive outcome only probs = probs[:, 1] auc = roc_auc_score(y_test, probs) print('AUC - Test Set: %.2f%%' % (auc*100)) # calculate roc curve fpr, tpr, thresholds = roc_curve(y_test, probs) # plot no skill plt.plot([0, 1], [0, 1], linestyle='--') # plot the roc curve for the model plt.plot(fpr, tpr, marker='.') plt.xlabel('False positive rate') plt.ylabel('Sensitivity/ Recall') # show the plot plt.show()In the example above, the AUC is relatively close to 1 and greater than 0.5. A perfect classifier will have the ROC curve go along the Y-axis and then along the X-axisLog LossLog Loss is the most important classification metric based on probabilities.As the predicted probability of the true class gets closer to zero, the loss increases exponentially:It measures the performance of a classification model where the prediction input is a probability value between 0 and 1. Log loss increases as the predicted probability diverge from the actual label. The goal of any machine learning model is to minimize this value. As such, smaller log loss is better, with a perfect model having a log loss of 0.A sample python implementation of the Log Loss.#Classification LogLoss import warnings import pandas from sklearn import model_selection from sklearn.linear_model import LogisticRegression from sklearn.metrics import log_loss warnings.filterwarnings('ignore') url = "https://raw.githubusercontent.com/jbrownlee/Datasets/master/pima-indians-diabetes.data.csv" dataframe = pandas.read_csv(url) dat = dataframe.values X = dat[:,:-1] y = dat[:,-1] seed = 7 #split data X_train, X_test, y_train, y_test = model_selection.train_test_split(X, y, test_size=test_size, random_state=seed) model.fit(X_train, y_train) #predict and compute logloss pred = model.predict(X_test) accuracy = log_loss(y_test, pred) print("Logloss: %.2f" % (accuracy))Logloss: 8.02
Jaccard IndexJaccard Index is one of the simplest ways to calculate and find out the accuracy of a classification ML model. Let’s understand it with an example. Suppose we have a labeled test set, with labels as –y = [0,0,0,0,0,1,1,1,1,1]And our model has predicted the labels as –y1 = [1,1,0,0,0,1,1,1,1,1]The above Venn diagram shows us the labels of the test set and the labels of the predictions, and their intersection and union.Jaccard Index or Jaccard similarity coefficient is a statistic used in understanding the similarities between sample sets. The measurement emphasizes the similarity between finite sample sets and is formally defined as the size of the intersection divided by the size of the union of the two labeled sets, with formula as –Jaccard Index or Intersection over Union(IoU)So, for our example, we can see that the intersection of the two sets is equal to 8 (since eight values are predicted correctly) and the union is 10 + 10–8 = 12. So, the Jaccard index gives us the accuracy as –So, the accuracy of our model, according to Jaccard Index, becomes 0.66, or 66%.Higher the Jaccard index higher the accuracy of the classifier.A sample python implementation of the Jaccard index.import numpy as np def compute_jaccard_similarity_score(x, y): intersection_cardinality = len(set(x).intersection(set(y))) union_cardinality = len(set(x).union(set(y))) return intersection_cardinality / float(union_cardinality) score = compute_jaccard_similarity_score(np.array([0, 1, 2, 5, 6]), np.array([0, 2, 3, 5, 7, 9])) print "Jaccard Similarity Score : %s" %score passJaccard Similarity Score : 0.375Kolomogorov Smirnov chartK-S or Kolmogorov-Smirnov chart measures the performance of classification models. More accurately, K-S is a measure of the degree of separation between positive and negative distributions.The cumulative frequency for the observed and hypothesized distributions is plotted against the ordered frequencies. The vertical double arrow indicates the maximal vertical difference.The K-S is 100 if the scores partition the population into two separate groups in which one group contains all the positives and the other all the negatives. On the other hand, If the model cannot differentiate between positives and negatives, then it is as if the model selects cases randomly from the population. The K-S would be 0.In most classification models the K-S will fall between 0 and 100, and that the higher the value the better the model is at separating the positive from negative cases.The K-S may also be used to test whether two underlying one-dimensional probability distributions differ. It is a very efficient way to determine if two samples are significantly different from each other.A sample python implementation of the Kolmogorov-Smirnov.from scipy.stats import kstest import random # N = int(input("Enter number of random numbers: ")) N = 10 actual =[] print("Enter outcomes: ") for i in range(N): # x = float(input("Outcomes of class "+str(i + 1)+": ")) actual.append(random.random()) print(actual) x = kstest(actual, "norm") print(x)The Null hypothesis used here assumes that the numbers follow the normal distribution. It returns statistics and p-value. If the p-value is < alpha, we reject the Null hypothesis.Alpha is defined as the probability of rejecting the null hypothesis given the null hypothesis(H0) is true. For most of the practical applications, alpha is chosen as 0.05.Gain and Lift ChartGain or Lift is a measure of the effectiveness of a classification model calculated as the ratio between the results obtained with and without the model. Gain and lift charts are visual aids for evaluating the performance of classification models. However, in contrast to the confusion matrix that evaluates models on the whole population gain or lift chart evaluates model performance in a portion of the population.The higher the lift (i.e. the further up it is from the baseline), the better the model.The following gains chart, run on a validation set, shows that with 50% of the data, the model contains 90% of targets, Adding more data adds a negligible increase in the percentage of targets included in the model.Gain/lift chartLift charts are often shown as a cumulative lift chart, which is also known as a gains chart. Therefore, gains charts are sometimes (perhaps confusingly) called “lift charts”, but they are more accurately cumulative lift charts.It is one of their most common uses is in marketing, to decide if a prospective client is worth calling.Gini CoefficientThe Gini coefficient or Gini Index is a popular metric for imbalanced class values. The coefficient ranges from 0 to 1 where 0 represents perfect equality and 1 represents perfect inequality. Here, if the value of an index is higher, then the data will be more dispersed.Gini coefficient can be computed from the area under the ROC curve using the following formula:Gini Coefficient = (2 * ROC_curve) — 1ConclusionUnderstanding how well a machine learning model is going to perform on unseen data is the ultimate purpose behind working with these evaluation metrics. Metrics like accuracy, precision, recall are good ways to evaluate classification models for balanced datasets, but if the data is imbalanced and there’s a class disparity, then other methods like ROC/AUC, Gini coefficient perform better in evaluating the model performance.Well, this concludes this article. I hope you guys have enjoyed reading it, feel free to share your comments/thoughts/feedback in the comment section.Thanks for reading !!!

Data Science

DataSource AI Hosts KTM AG's Inaugural AI Challenge: "Code the Light Fantastic"
DataSource AI announces the launch of the KTM AG inaugural AI Challenge, an unprecedented 3-month online competition that aims to revolutionise two-wheeler innovation through artificial intelligence and deep learning. KTM AG is a global frontrunner in two-wheeler innovation, known for pushing the boundaries of what's possible in the world of motorcycles. With a rich history of groundbreaking engineering and a commitment to cutting-edge technology, KTM AG has set new standards in performance, design, and safety. As a global leader in two-wheeler innovation, KTM AG invites participants to embark on this groundbreaking innovation journey. At the core of this competition lies a challenge set to redefine the future of motorcycle lighting systems. Participants are tasked with developing an algorithm for a high-beam lighting system utilizing a pixel matrix. Participants can find detailed guidelines in the Datathon competition. The datathon unfolds in a 3-tiered cascade model: This Code Challenge by KTM AG promises not only substantial rewards but also an exciting opportunity to shape the future of two-wheeler technology, along with supporting the participants to upscale and test their knowledge in a global AI competition. The cumulative budget for this remarkable Code Challenge by KTM AG is a substantial €24,000, motivating participants with not only the opportunity to push the boundaries of two-wheeler technology but also significant rewards for those who rise to the occasion. With cumulative prizes, contestants have the chance to potentially take home a maximum reward of €10,800 in addition to contributing to cutting-edge advancements in the field. We invite all aspiring innovators, data scientists, and AI enthusiasts to join us in this journey to "Code the Light Fantastic." For more information, rules, and registration details, please register hereAbout DataSource: At DataSource AI, we are driven by a singular mission - to democratise the immense power of data science and AI/ML for businesses of all sizes and budgets. We facilitate AI competitions, for businesses of all sizes and budgets by harnessing our extensive data expert community that's collaborating over our intelligent AI algorithm crowdsourcing platform. Our community is at the heart of what we do. We've built a diverse and talented pool of data experts who are passionate about solving real-world problems. They collaborate, ideate, and innovate, driving forward the frontiers of data science.

Data Science

Top 2 Online Data Science Courses to Improve your Career in 2023
The discipline of Data Science is expanding quickly and has enormous promise. It is used in various sectors, including manufacturing, retail, healthcare, and finance. Today, a wide variety of online Data Science courses are accessible. With so many choices, you might need help choosing the best one. This article will summarize the best Data Science programs so you can choose the program that's best for you. What Is a Data Science Course?The theoretical ideas of data science are taught to novices in a Data Science course. Additionally, you'll learn about the steps involved in Data Science, such as mathematical and statistical analysis, data preparation & staging, data interpretation, data visualization, and methods for presenting data insights in an organizational context. Advanced subjects, such as employing neural networks to develop recommendation engines, are covered in more specialized courses.Why Data Science?In the expanding field of data science, a data scientist earns one of the best jobs. Data science gained popularity and started to be utilised in an expanding number of applications when big data appeared and the necessity to manage these massive volumes of data arose. Data science, which enables companies to derive conclusions on the basis and take measures based on those conclusions, is one of the primary applications of artificial intelligence. Data Scientists are in great demand due to Data Science's importance for all industries. There is fierce rivalry everywhere. But if you can get an advantage over your competitors, you may easily land lucrative positions in demand. Taking data science courses online might give you that advantage. Data science involves:● Analytical capabilities.● A foundational understanding of the field.● Practical abilities to produce outcomes. To understand data science, you don't need to spend years working with big data or have a tonne of expertise in the software sector. You may always study from the greatest online Data Science courses and create a way to join this area while working. These are the top data science programs you can take to further your career and understand the subject. Let's discover more about the top data science courses available online. 2 Online Data Science Courses for 2023 to Advance Your Career1. Program for Business Analytics CertificationThis online Data Science course lasts three months and calls for 8 to 10 hours of study per week. The course created for analytics aspirants is one of the greatest data science courses in India and one of the market's top data science courses. It has more than 100 hours of material. The Data Science course was built with the help of business professionals from organizations like Flipkart, Gardener, and Actify. This is one of the finest online courses for learning the fundamentals of data science since it offers committed mentor assistance, prompt doubt resolution services, and live sessions with subject matter specialists. Students will gain knowledge in statistics, optimization, business problem-solving, and predictive modeling via this course. This online data science course was created for managers, engineers, recent graduates, software and IT workers, and marketing and salespeople. Students will concentrate on corporate problem-solving, insights, and narrative for the very first 3 weeks of the course. In this portion, you will discover how to formulate hypotheses, comprehend business issues, and concentrate on narrative. The following four weeks will be devoted to understanding statistics, optimization, and exploratory data analysis. A case study assignment will also be included. You will study several machine learning approaches to evaluate data and provide insights during the last five weeks, which will be devoted to predictive analysis. There will be three initiatives at the industry level: uber supply-demand gap, customer creditworthiness, and market mix modeling for e-commerce. Students who take this business analyst course have access to various options, including the ability to apply for managerial, business analyst, and data analyst employment. 2. Data Science Master's DegreeIt is among the top online courses for Data Science. This master’s in Data Science program lasts for 18 months and is delivered online. If you engage in expert online data science courses from a recognised and trusted provider, you may put your talents to the test on real assignments.This course is one of the best Data Science courses since it offers a variety of distinctive characteristics. There are several specialization options available for the course. Business intelligence/data analytics, Natural Language Processing, and Deep Learning,Business Analyst course, and Data Engineering are the available specialization areas. In addition to these specializations, the course offers its students a platform to study more than 14 programming languages and technologies that are utilized in the diverse area of data science, as well as industry mentoring and committed career assistance. One of the greatest data science courses in India, it includes tools like Python, Tableau, Hadoop, MySQL, Hive, Excel, PowerBI, MongoDB, Shiny, Keras, TensorFlow, PySpark, HBase, and Apache Airflow. More than 400 hours of learning material are planned for the online data science course. You will get a thorough understanding of data science and topics linked to it through these videos and publications, enabling you to succeed in any data science interviews. For the students to have a practical and hands-on understanding of all the tools and ideas covered in the course, the online data course includes more than ten industrial projects and case studies. The students may study various subjects, languages, and tools throughout the course. The first four weeks will be devoted to learning the fundamentals of Python and how to use Excel to deal with data. The 11 weeks will be devoted to teaching students how to utilize all the tools needed for data science and how to prepare and work with the provided data. You will acquire in-depth information on Python, Excel, and SQL in this part. Learning about machine learning and its many algorithms will be the main emphasis of the next nine weeks. ConclusionThe best online Data Science courses provide a good introduction to the subject, which is how the article can be summed up. They go through the fundamentals of data science, such as handling data, cleansing data, and doing statistical analysis. They also provide a more thorough examination of Data Science and machine learning Anyone interested in pursuing a career in data science must take these courses.

Data Science

5 Tips To Ace Your Job Interview For A Data Scientist Opening
5 Tips To Ace Your Job Interview For A Data Scientist Opening.PNG 795.94 KBImage SourceAspiring data scientists have a bright future ahead of them. They’re about to enter a field that’s exponentially expanding in terms of job growth and career opportunities. Reports say that the sector has seen a 650% job growth since 2012 and, according to predictions, there will be an estimated 11.5 million new jobs by 2026. All that’s left for data scientist hopefuls is to develop their skills and ace their job interviews. While that may be the most daunting part, we're here to give you five tips on how to impress your interviewer and grab that opportunity.
#1- Prepare answers for potential interview questions
In our list of 10 highly probable data scientist interview questions, we highlight some of the most asked questions that you’ll want to prepare for. These include situations related to machine learning, Python, and SQL. For example, you could get asked about the difference between classification and clustering, or the important features of dictionaries. While these questions can depend on your interviewer and the company you’re trying for, it won’t hurt to have prepared answers for these basic questions. Brush up on these topics and do your own research.
#2- Recall your technical abilities
Companies often have a separate technical screening portion prepared for you, but it would also be helpful to run over your technical abilities. This also depends on the specifications of the position you’re applying for; as a data scientist, they might inquire about your efficiency in developing algorithms for the collation and cleaning of datasets. Your interviewer could ask if you’ve had the chance to create an original algorithm of your own. If you’ve done any data projects, they might also inquire about the challenges you faced and how you were able to deal with them.
#3- Communicate your strengths
It would be helpful if you could confidently explain and articulate what kind of data scientist you are. To do this, you must know where your strengths lie and what your niche is. In any job interview, companies often ask about an applicant’s strengths because the way they approach this question says a lot about them. Think about what you could contribute to a team and what type of role you see yourself thriving in. Then, figure out a way to communicate why you think your unique strengths are an asset to the company.
#4- When prompted, ask questions of your own
Interviewers love when an applicant shows engagement and interest in the company. Throughout the process, jot down the questions that might come to you, and don’t be afraid to ask away when prompted. The questions you ask in an interview could be a chance for you to learn more about your potential employer and the work environment. You could ask them simple questions like, “what is the most enjoyable part of working here?”, or “what are the company’s goals over the next few years?” This shows them your passion and dedication for the role.
#5- Stay updated on trends in the data science space
The data science industry is always changing, and there’s always something new to learn every day. If you want to gain an edge against your competitors, make sure you’re on top of the latest data science trends and news. One way to do so is to always be on the lookout for upcoming data science conferences and seminars you can attend. Attending events could earn you connections and help you learn things you won’t find in textbooks. You could also do some supplementary reading of the latest research papers. This will show your interviewers that you’re a motivated self-starter.With time, effort, and these five tips in mind, you’ll be ready to answer any question thrown at you. Interviews are just the first step towards the career of your dreams, so make sure you prepare for every opportunity presented to you.

Deep learning

When to Avoid Deep Learning
IntroductionThis article is intended for data scientists who may consider using deep learning algorithms, and want to know more about the cons of implementing these type of models into your work. Deep learning algorithms have many benefits, are powerful, and can be fun to show off. However, there are a few times when you should avoid them. I will be discussing those times when you should stop using deep learning below, so keep on reading if you would like a deeper dive into deep learning.When You Want to Easily ExplainPhoto by Malte Helmhold on Unsplash [2].Because other algorithms have been around longer, they have countless amounts of documentation, including examples and functions that make interpretability easier. It is also how the other algorithms work themselves. Deep learning can be intimidating to data scientists for this reason as well, it can be a turn-off to use a deep learning algorithm when you are unsure of how to explain it to a stakeholder.Here are 3 examples of when you would have trouble explaining deep learning:When you want to describe the top features of your model — the features become hidden inputs, so you will not know what caused a certain prediction to happen, and if you need to prove to stakeholders or customers why a certain output was achieved, it can be more of a black boxWhen you want to tune your hyperparameters like learning rate and batch sizeWhen you want to explain how the algorithm works itself — for example, if you were to present the algorithm itself to stakeholders, they might get lost, because even a simplified approach is still difficult to understandHere are 3 examples of how you could explain those same situations from above from non-deep learning algorithms:When you want to explain your top features, you can easily access SHAP libraries, say for the algorithm CatBoost, once your model is fitted, you can simply make a summary plot from feat = model.get_feature_importance() and then use the summary_plot() to rank the features by feature name, so that you can present a nice plot to stakeholders (and yourself for that matter)Example of ranked SHAP output from a non-deep learning model [3].As a solution, some other algorithms have made it plenty easy to tune your hyperparameters by randomized grid search or a more structured, set grid search method. There are even some algorithms that tune themselves so you do not have to worry about complicated tuningExplaining how other algorithms work can be a lot easier, like decision trees, for example, you can easily show a yes or no, 0/1 chart that shows a simple answer for features that lead to a prediction, like yes it is raining, yes it is winter, would provide for yes it is going to be coldOverall, deep learning algorithms are useful and powerful, so there is definitely a time and place for them, but there are other algorithms that you can use instead, as we will discuss below.When You Can Use Other AlgorithmsPhoto by Luca Bravo on Unsplash [4].To be frank, there are a few go-to algorithms that can give you a great model with great results rather quickly. Some of these algorithms include Linear Regression, Decision Trees, Random Forest, XGBoost, and CatBoost. These are alternatives that are more simple.Here are examples of why you would want to use a non-deep learning algorithm, becuase you have so many other, simpler, non-deep learning options:They can be easier and faster to set up, for example, deep learning can require you to have your model add sequential, dense layers, and compile it, which can be more complex, and take longer than simply having a regressor or classifier and fitting it with non-deep learning algorithmsI personally find more errors that can result from this more complex deep learning code and documentation for how to fix it can be confusing or old and not be applicable, using an algorithm like Random Forest instead, can have much more documentation on errors that are easy to understandTraining on a deep learning algorithm may not be complicated sometimes, but when predicting from an endpoint, it might be confusing on how to feed values to predict on, whereas some models, you can simply have the values in an encoded list of ordered valuesI would say that you can of course try out deep learning algorithms, but before you do that, it might be best to start with a simpler solution. It can depend on things like how often you will train and make predictions, or if it is a one-off task. There are some other reasons why you would not want to use a deep learning algorithm, like when you have a small dataset and small budget, as we will discuss below.When You Have a Small Dataset and BudgetPhoto by Hello I’m Nik on Unsplash [5].Oftentimes, you can be working as a data scientist at a smaller company, or perhaps at a startup. In these cases, you would not have much data and you might not have a big budget. You would, therefore, try to avoid the use of deep learning algorithms. Sometimes you can even have a small dataset that is just a few thousand rows and few features, you could simply run an alternative model instead locally, rather than spending a lot of money by serving it frequently.Here is when you should second guess using a deep learning algorthim based on costs and data availability:Small data availability is usually the case for a lot of companies (but is not always the case), and deep learning performs better on information with a lot of dataYou might be performing a one-off task, as in the model only predicts one time — and you can run it locally for free (not all models will be running in production frequently), like a simple Decision Tree Classifier. It might not be worth investing time in a deep learning model.Your company is interested in data science applications but wants to keep the budget small, rather than perform costly executions from a deep learning model, and rather, use a tree-based model with early-stopping-rounds to prevent overfitting, shorten training time, and ultimately reduce costsThere have been times where I brought up deep learning and it was shot down for a variety of reasons, and these reasons were usually the case. But, I do not want to dissuade someone from using deep learning completely, as it is something you should use sometimes in your career, and can be something you do frequently or mainly depending on the circumstances and where you are working.SummaryOverall, before you dive deep into deep learning, realize that there are some times when you should avoid using it for a variety of reasons. There are, of course, more reasons for avoiding it, but there are also reasons for using it too. It is ultimately up to you to look at the pros and cons of deep learning yourself.Here are three times/reasons when you should not use deep learning:* When You Want to Easily Explain
* When You Can Use Other Algorithms
* When You Have Small Dataset and BudgetI hope you found my article both interesting and useful. Please feel free to comment down below if you agree or disagree with reasons for avoiding deep learning. Why or why not? What other reasons do you think you should avoid using deep learning as a data scientist? These can certainly be clarified even further, but I hope I was able to shed some light on deep learning. Thank you for reading!I am not affiliated with any of these companies.Please feel free to check out my profile, Matt Przybyla, and other articles, as well as subscribe to receive email notifications for my blogs by following the link below, or by clicking on the subscribe icon on the top of the screen by the follow icon, and reach out to me on LinkedIn if you have any questions or comments.Subscribe link: https://datascience2.medium.com/subscribeReferences[1] Photo by Nadine Shaabana on Unsplash, (2018)[2] Photo by Malte Helmhold on Unsplash, (2021)[3] M.Przybyla, Example of ranked SHAP output from a non-deep learning model, (2021)[4] Photo by Luca Bravo on Unsplash, (2016)[5] Photo by Hello I’m Nik on Unsplash, (2021)

Programming
Data Science

6 Advanced Statistical Concepts in Data Science
The article contains some of the most commonly used advanced statistical concepts along with their Python implementation.In my previous articles Beginners Guide to Statistics in Data Science and The Inferential Statistics Data Scientists Should Know we have talked about almost all the basics(Descriptive and Inferential) of statistics which are commonly used in understanding and working with any data science case study. In this article, lets go a little beyond and talk about some advance concepts which are not part of the buzz.Concept #1 - Q-Q(quantile-quantile) PlotsBefore understanding QQ plots first understand what is a Quantile?A quantile defines a particular part of a data set, i.e. a quantile determines how many values in a distribution are above or below a certain limit. Special quantiles are the quartile (quarter), the quintile (fifth), and percentiles (hundredth).An example:If we divide a distribution into four equal portions, we will speak of four quartiles. The first quartile includes all values that are smaller than a quarter of all values. In a graphical representation, it corresponds to 25% of the total area of distribution. The two lower quartiles comprise 50% of all distribution values. The interquartile range between the first and third quartile equals the range in which 50% of all values lie that are distributed around the mean. In Statistics, A Q-Q(quantile-quantile) plot is a scatterplot created by plotting two sets of quantiles against one another. If both sets of quantiles came from the same distribution, we should see the points forming a line that’s roughly straight(y=x).Q-Q plotFor example, the median is a quantile where 50% of the data fall below that point and 50% lie above it. The purpose of Q Q plots is to find out if two sets of data come from the same distribution. A 45-degree angle is plotted on the Q Q plot; if the two data sets come from a common distribution, the points will fall on that reference line.It’s very important for you to know whether the distribution is normal or not so as to apply various statistical measures on the data and interpret it in much more human-understandable visualization and their Q-Q plot comes into the picture. The most fundamental question answered by the Q-Q plot is if the curve is Normally Distributed or not.Normally distributed, but why?The Q-Q plots are used to find the type of distribution for a random variable whether it is a Gaussian Distribution, Uniform Distribution, Exponential Distribution, or even Pareto Distribution, etc. You can tell the type of distribution using the power of the Q-Q plot just by looking at the plot. In general, we are talking about Normal distributions only because we have a very beautiful concept of the 68–95–99.7 rule which perfectly fits into the normal distribution So we know how much of the data lies in the range of the first standard deviation, second standard deviation and third standard deviation from the mean. So knowing if a distribution is Normal opens up new doors for us to experiment with Types of Q-Q plots. Source Skewed Q-Q plotsQ-Q plots can find skewness(measure of asymmetry) of the distribution. If the bottom end of the Q-Q plot deviates from the straight line but the upper end is not, then the distribution is Left skewed(Negatively skewed).Now if upper end of the Q-Q plot deviates from the staright line and the lower is not, then the distribution is Right skewed(Positively skewed).Tailed Q-Q plotsQ-Q plots can find Kurtosis(measure of tailedness) of the distribution.The distribution with the fat tail will have both the ends of the Q-Q plot to deviate from the straight line and its centre follows the line, where as a thin tailed distribution will term Q-Q plot with very less or negligible deviation at the ends thus making it a perfect fit for normal distribution.Q-Q plots in Python(Source)Suppose we have the following dataset of 100 values:import numpy as np
#create dataset with 100 values that follow a normal distribution
np.random.seed(0)
data = np.random.normal(0,1, 1000)
#view first 10 values
data[:10] array([ 1.76405235, 0.40015721, 0.97873798, 2.2408932 , 1.86755799,
-0.97727788, 0.95008842, -0.15135721, -0.10321885, 0.4105985 ])To create a Q-Q plot for this dataset, we can use the qqplot() function from the statsmodels library:import statsmodels.api as sm
import matplotlib.pyplot as plt
#create Q-Q plot with 45-degree line added to plot
fig = sm.qqplot(data, line='45')
plt.show()In a Q-Q plot, the x-axis displays the theoretical quantiles. This means it doesn’t show your actual data, but instead, it represents where your data would be if it were normally distributed.The y-axis displays your actual data. This means that if the data values fall along a roughly straight line at a 45-degree angle, then the data is normally distributed.We can see in our Q-Q plot above that the data values tend to closely follow the 45-degree, which means the data is likely normally distributed. This shouldn’t be surprising since we generated the 100 data values by using the numpy.random.normal() function.Consider instead if we generated a dataset of 100 uniformly distributed values and created a Q-Q plot for that dataset:#create dataset of 100 uniformally distributed values
data = np.random.uniform(0,1, 1000)
#generate Q-Q plot for the dataset
fig = sm.qqplot(data, line='45')
plt.show()The data values clearly do not follow the red 45-degree line, which is an indication that they do not follow a normal distribution.Concept #2- Chebyshev's InequalityIn probability, Chebyshev’s Inequality, also known as “Bienayme-Chebyshev” Inequality guarantees that, for a wide class of probability distributions, only a definite fraction of values will be found within a specific distance from the mean of a distribution.Source: https://www.thoughtco.com/chebyshevs-inequality-3126547 Chebyshev’s inequality is similar to The Empirical rule(68-95-99.7); however, the latter rule only applies to normal distributions. Chebyshev’s inequality is broader; it can be applied to any distribution so long as the distribution includes a defined variance and mean.So Chebyshev’s inequality says that at least (1-1/k^2) of data from a sample must fall within K standard deviations from the mean (or equivalently, no more than 1/k^2 of the distribution’s values can be more than k standard deviations away from the mean).Where K --> Positive real numberIf the data is not normally distributed then different amounts of data could be in one standard deviation. Chebyshev’s inequality provides a way to know what fraction of data falls within K standard deviations from the mean for any data distribution.Also read: 22 Statistics Questions to Prepare for Data Science InterviewsCredits: https://calcworkshop.com/joint-probability-distribution/chebyshev-inequality/ Chebyshev’s inequality is of great value because it can be applied to any probability distribution in which the mean and variance are provided.Let us consider an example, Assume 1,000 contestants show up for a job interview, but there are only 70 positions available. In order to select the finest 70 contestants amongst the total contestants, the proprietor gives tests to judge their potential. The mean score on the test is 60, with a standard deviation of 6. If an applicant scores an 84, can they presume that they are getting the job?The results show that about 63 people scored above a 60, so with 70 positions available, a contestant who scores an 84 can be assured they got the job.Chebyshev's Inequality in Python(Source) Create a population of 1,000,000 values, I use a gamma distribution(also works with other distributions) with shape = 2 and scale = 2.import numpy as np
import random
import matplotlib.pyplot as plt
#create a population with a gamma distribution
shape, scale = 2., 2. #mean=4, std=2*sqrt(2)
mu = shape*scale #mean and standard deviation
sigma = scale*np.sqrt(shape)
s = np.random.gamma(shape, scale, 1000000)Now sample 10,000 values from the population.#sample 10000 values
rs = random.choices(s, k=10000)Count the sample that has a distance from the expected value larger than k standard deviation and use the count to calculate the probabilities. I want to depict a trend of probabilities when k is increasing, so I use a range of k from 0.1 to 3.#set k
ks = [0.1,0.5,1.0,1.5,2.0,2.5,3.0]
#probability list
probs = [] #for each k
for k in ks:
#start count
c = 0
for i in rs:
# count if far from mean in k standard deviation
if abs(i - mu) > k * sigma :
c += 1
probs.append(c/10000)Plot the results:plot = plt.figure(figsize=(20,10))
#plot each probability
plt.xlabel('K')
plt.ylabel('probability')
plt.plot(ks,probs, marker='o')
plot.show()
#print each probability
print("Probability of a sample far from mean more than k standard deviation:")
for i, prob in enumerate(probs):
print("k:" + str(ks[i]) + ", probability: " \
+ str(prob)[0:5] + \
" | in theory, probability should less than: " \
+ str(1/ks[i]**2)[0:5])From the above plot and result, we can see that as the k increases, the probability is decreasing, and the probability of each k follows the inequality. Moreover, only the case that k is larger than 1 is useful. If k is less than 1, the right side of the inequality is larger than 1 which is not useful because the probability cannot be larger than 1.Concept #3- Log-Normal DistributionIn probability theory, a Log-normal distribution also known as Galton's distribution is a continuous probability distribution of a random variable whose logarithm is normally distributed.Thus, if the random variable X is log-normally distributed, then Y = ln(X) has a normal distribution. Equivalently, if Y has a normal distribution, then the exponential function of Y i.e, X = exp(Y), has a log-normal distribution. Skewed distributions with low mean and high variance and all positive values fit under this type of distribution. A random variable that is log-normally distributed takes only positive real values. The general formula for the probability density function of the lognormal distribution is:The location and scale parameters are equivalent to the mean and standard deviation of the logarithm of the random variable.The shape of Lognormal distribution is defined by 3 parameters:σis the shape parameter, (and is the standard deviation of the log of the distribution)θ or μ is the location parameter (and is the mean of the distribution)m is the scale parameter (and is also the median of the distribution)The location and scale parameters are equivalent to the mean and standard deviation of the logarithm of the random variable as explained above.If x = θ, then f(x) = 0. The case where θ = 0 and m = 1 is called the standard lognormal distribution. The case where θ equals zero is called the 2-parameter lognormal distribution.The following graph illustrates the effect of the location(μ) and scale(σ) parameter on the probability density function of the lognormal distribution: Source: https://www.sciencedirect.com/topics/mathematics/lognormal-distribution Log-Normal Distribution in Python(Source)Let us consider an example to generate random numbers from a log-normal distribution with μ=1 and σ=0.5 using scipy.stats.lognorm function.import numpy as np
import matplotlib.pyplot as plt
from scipy.stats import lognorm
np.random.seed(42)
data = lognorm.rvs(s=0.5, loc=1, scale=1000, size=1000)
plt.figure(figsize=(10,6))
ax = plt.subplot(111)
plt.title('Generate wrandom numbers from a Log-normal distribution')
ax.hist(data, bins=np.logspace(0,5,200), density=True)
ax.set_xscale("log")
shape,loc,scale = lognorm.fit(data)
x = np.logspace(0, 5, 200)
pdf = lognorm.pdf(x, shape, loc, scale)
ax.plot(x, pdf, 'y')
plt.show()Concept #4- Power Law distributionIn statistics, a Power Law is a functional relationship between two quantities, where a relative change in one quantity results in a proportional relative change in the other quantity, independent of the initial size of those quantities: one quantity varies as a power of another. For instance, considering the area of a square in terms of the length of its side, if the length is doubled, the area is multiplied by a factor of four.A power law distribution has the form Y = k Xα, where:X and Y are variables of interest,α is the law’s exponent,k is a constant.Source: https://en.wikipedia.org/wiki/Power_law Power-law distribution is just one of many probability distributions, but it is considered a valuable tool to assess uncertainty issues that normal distribution cannot handle when they occur at a certain probability.Many processes have been found to follow power laws over substantial ranges of values. From the distribution in incomes, size of meteoroids, earthquake magnitudes, the spectral density of weight matrices in deep neural networks, word usage, number of neighbors in various networks, etc. (Note: The power law here is a continuous distribution. The last two examples are discrete, but on a large scale can be modeled as if continuous).Also read: Statistical Measures of Central TendencyPower-law distribution in Python(Source) Let us plot the Pareto distribution which is one form of a power-law probability distribution. Pareto distribution is sometimes known as the Pareto Principle or ‘80–20’ rule, as the rule states that 80% of society’s wealth is held by 20% of its population. Pareto distribution is not a law of nature, but an observation. It is useful in many real-world problems. It is a skewed heavily tailed distribution.import numpy as np
import matplotlib.pyplot as plt
from scipy.stats import pareto
x_m = 1 #scale
alpha = [1, 2, 3] #list of values of shape parameters
plt.figure(figsize=(10,6))
samples = np.linspace(start=0, stop=5, num=1000)
for a in alpha:
output = np.array([pareto.pdf(x=samples, b=a, loc=0, scale=x_m)])
plt.plot(samples, output.T, label='alpha {0}' .format(a))
plt.xlabel('samples', fontsize=15)
plt.ylabel('PDF', fontsize=15)
plt.title('Probability Density function', fontsize=15)
plt.legend(loc='best')
plt.show()Concept #5- Box cox transformationThe Box-Cox transformation transforms our data so that it closely resembles a normal distribution.The one-parameter Box-Cox transformations are defined as In many statistical techniques, we assume that the errors are normally distributed. This assumption allows us to construct confidence intervals and conduct hypothesis tests. By transforming your target variable, we can (hopefully) normalize our errors (if they are not already normal).Additionally, transforming our variables can improve the predictive power of our models because transformations can cut away white noise.Original distribution(Left) and near-normal distribution after applying Box cox transformation. Source At the core of the Box-Cox transformation is an exponent, lambda (λ), which varies from -5 to 5. All values of λ are considered and the optimal value for your data is selected; The “optimal value” is the one that results in the best approximation of a normal distribution curve. The one-parameter Box-Cox transformations are defined as:and the two-parameter Box-Cox transformations as:Moreover, the one-parameter Box-Cox transformation holds for y > 0, i.e. only for positive values and two-parameter Box-Cox transformation for y > -λ, i.e. negative values. The parameter λ is estimated using the profile likelihood function and using goodness-of-fit tests.If we talk about some drawbacks of Box-cox transformation, then if interpretation is what you want to do, then Box-cox is not recommended. Because if λ is some non-zero number, then the transformed target variable may be more difficult to interpret than if we simply applied a log transform.A second stumbling block is that the Box-Cox transformation usually gives the median of the forecast distribution when we revert the transformed data to its original scale. Occasionally, we want the mean and not the median.Box-Cox transformation in Python(Source)SciPy’s stats package provides a function called boxcox for performing box-cox power transformation that takes in original non-normal data as input and returns fitted data along with the lambda value that was used to fit the non-normal distribution to normal distribution.#load necessary packages
import numpy as np
from scipy.stats import boxcox
import seaborn as sns
#make this example reproducible
np.random.seed(0)
#generate dataset
data = np.random.exponential(size=1000)
fig, ax = plt.subplots(1, 2)
#plot the distribution of data values
sns.distplot(data, hist=False, kde=True,
kde_kws = {'shade': True, 'linewidth': 2},
label = "Non-Normal", color ="red", ax = ax[0])
#perform Box-Cox transformation on original data
transformed_data, best_lambda = boxcox(data)
sns.distplot(transformed_data, hist = False, kde = True,
kde_kws = {'shade': True, 'linewidth': 2},
label = "Normal", color ="red", ax = ax[1])
#adding legends to the subplots
plt.legend(loc = "upper right")
#rescaling the subplots
fig.set_figheight(5)
fig.set_figwidth(10)
#display optimal lambda value
print(f"Lambda value used for Transformation: {best_lambda}")
Concept #6- Poisson distributionIn probability theory and statistics, the Poisson distribution is a discrete probability distribution that expresses the probability of a given number of events occurring in a fixed interval of time or space if these events occur with a known constant mean rate and independently of the time since the last event.In very simple terms, A Poisson distribution can be used to estimate how likely it is that something will happen "X" number of times. Some examples of Poisson processes are customers calling a help center, radioactive decay in atoms, visitors to a website, photons arriving at a space telescope, and movements in a stock price. Poisson processes are usually associated with time, but they do not have to be. The Formula for the Poisson Distribution Is:Where:e is Euler's number (e = 2.71828...)k is the number of occurrencesk! is the factorial of kλ is equal to the expected value of kwhen that is also equal to its varianceLambda(λ) can be thought of as the expected number of events in the interval. As we change the rate parameter, λ, we change the probability of seeing different numbers of events in one interval. The below graph is the probability mass function of the Poisson distribution showing the probability of a number of events occurring in an interval with different rate parameters. Probability Mass function for Poisson Distribution with varying rate parameters.Source The Poisson distribution is also commonly used to model financial count data where the tally is small and is often zero. For one example, in finance, it can be used to model the number of trades that a typical investor will make in a given day, which can be 0 (often), or 1, or 2, etc.As another example, this model can be used to predict the number of "shocks" to the market that will occur in a given time period, say over a decade.Poisson distribution in Pythonfrom numpy import random
import matplotlib.pyplot as plt
import seaborn as sns
lam_list = [1, 4, 9] #list of Lambda values
plt.figure(figsize=(10,6))
samples = np.linspace(start=0, stop=5, num=1000)
for lam in lam_list:
sns.distplot(random.poisson(lam=lam, size=10), hist=False, label='lambda {0}'.format(lam))
plt.xlabel('Poisson Distribution', fontsize=15)
plt.ylabel('Frequency', fontsize=15)
plt.legend(loc='best')
plt.show()As λ becomes bigger, the graph looks more like a normal distribution.I hope you have enjoyed reading this article, If you have any questions or suggestions, please leave a comment. Also read: False Positives vs. False NegativesFeel free to connect me on LinkedIn for any query.Thanks for reading!!!Referenceshttps://calcworkshop.com/joint-probability-distribution/chebyshev-inequality/ https://corporatefinanceinstitute.com/resources/knowledge/data-analysis/chebyshevs-inequality/ https://www.itl.nist.gov/div898/handbook/eda/section3/eda3669.htm https://www.statology.org/q-q-plot-python/ https://gist.github.com/chaipi-chaya/9eb72978dbbfd7fa4057b493cf6a32e7 https://stackoverflow.com/a/41968334/7175247

Python

Top 10 Python Extensions for Visual Studio Code
In this new post we want to talk about the most useful Python extensions for Visual Studio Code. Visual Studio Code is an integrated development environment created by Microsoft for Windows, Linux and macOS. Among its features are debugging, syntax highlighting, smart code completion, snippets, code refactoring and integrated Git. Users can change the theme, keyboard shortcuts, preferences and install extensions that add additional functionality.Precisely we are going to talk about the extensions you can install for VS. Here is a list of our favorites1- PythonLink: https://github.com/Microsoft/vscode-pythonPython extension for Visual Studio CodeA Visual Studio Code extension with rich support for the Python language (for all actively supported versions of the language: >=3.6), including features such as IntelliSense (Pylance), linting, debugging, code navigation, code formatting, refactoring, variable explorer, test explorer, and more!NOTE: Web support -- e.g., github.dev -- is limited.Installed extensionsThe Python extension will automatically install the Pylance and Jupyter extensions to give you the best experience when working with Python files and Jupyter notebooks. However, Pylance is an optional dependency, which means that the Python extension will remain fully functional if it is not installed. You can also uninstall it at the expense of some features if you are using a different language server.2- Python IndentLink: https://github.com/kbrose/vsc-python-indentIt is used to correct Python indentation in Visual Studio Code. How it worksEvery time you press the Enter key in a Python context, this extension will parse your Python file down to the location of your cursor, and determine exactly how much to indent the next line (or two in the case of hanging indents) and how much to indent nearby lines.There are three main cases when determining the correct indent. Review the documentation here: https://github.com/kbrose/vsc-python-indent3- Python Doctring GeneratorLink: https://github.com/NilsJPWerner/autoDocstringVisual Studio Code extension to quickly generate docstrings for python functions.FeaturesQuickly generate a docstring fragment that can be tabbed.Choose from several types of docstring formats.Infer parameter types via pep484 type hints, default values and var names.Support for args, kwargs, decorators, errors and parameter types.Docstring FormatsGoogle (default)docBlockrNumpySphinxPEP0257 (coming soon)UsageThe cursor must be on the line directly below the definition to generate a complete auto-populated docstring.Press enter after opening the docstring with triple quotes ("""" or ''')Keyboard shortcut: ctrl+shift+2 or cmd+shift+2 for macCan be changed in Preferences -> Keyboard shortcuts -> extension.generateDocstringCommand: Generate DocstringRight-click menu: Generate DocstringAlso read: 4 Must-Know Python Pandas Functions for Time Series Analysis4- Python ExtendedLink: https://github.com/tushortz/vscode-Python-ExtendedPython Extended is a vscode snippet that makes it easy to write Python code by providing completion options along with all arguments.UsageRun vscode and in a python file, type the name of the method to complete and press tab or enter on selection.How to installOpen vscode. Press F1, search for "ext install" followed by the extension name, in this case "ext install Python Extended" without the ">". Or if you prefer ">ext install", press enter, search for "Python Extended".5- Python PreviewLink: https://github.com/dongli0x00/python-previewA Visual Studio Code extension with debug preview support for the Python language.RequirementsInstall a version of Python 3.6 or Python 2.7. Make sure that the location of your Python interpreter is included in your PATH environment variable.It is best to install the Python extension for Python Intellisense.6- AREPL for PythonLink: https://github.com/almenon/arepl-vscodeAREPL automatically evaluates Python code in real time as you type.UsageFirst, make sure you have python 3.7 or higher installed.Open a python file and click on the cat in the top right bar to open AREPL. You can click the cat again to close it.Or run AREPL via the search command: control-shift-por use the shortcuts: control-shift-a (current document) / control-shift-q (new document)FeaturesReal-time evaluation: no need to run - AREPL evaluates your code automatically. You can control this (or even disable it) in the settings.Variable display: The final state of your local variables is displayed in a collapsible JSON format.Error display: The moment you make a mistake an error is displayed with the stack trace.Settings: AREPL offers many settings to suit your user experience. Customize the look and feel, bounce time, python options and much more.Aldo read: 3 Python Tricks That Will Improve Your Code7- Python PathLink: https://github.com/mgesbert/vscode-python-pathThis extension adds a set of tools to help generate internal import statements in a Python project.Features"Copy Python Path" is accessible from:Command lineExplorer context menuEditor context menuEditor title context menu8- Python Test ExplorerLink: https://github.com/kondratyev-nv/vscode-python-test-adapterThis extension allows you to run your Python Unittest, Pytest or Testplan tests with the Test Explorer user interface.How to get startedInstall the extensionConfigure Visual Studio Code to discover your tests (see the Configuration section and the documentation for the test framework of your choice:Unittest documentationPytest documentationTestplan documentationOpen the sidebar of the test viewExecute your tests via the Run icon in the Test ExplorerFeaturesDisplays a Test Explorer in the test view in the VS Code sidebar with all detected tests and suites and their statusConvenient error reporting during test detectionUnittest, Pytest and Testplan debuggingDisplays the log of a failed test when the test is selected in the explorerTest rerun when saving testsSupports multi-root workspacesSupports Unittest, Pytest and Testplan test frameworks and their plugins9- Python SnippetsLink: https://github.com/ylcnfrht/vscode-python-snippet-packA snippet package to make working with Python more productive This snippet package contains all of the following Python methodsall built-in python snippets and contains at least one example for each methodall python string snippets contain at least one example for each methodall python list snippets contain at least one example for each methodall Python set snippets contain at least one example for each methodall Python tuple snippets contain at least one example for each methodall python dictionary snippets contain at least one example for each methodAnd it contains many other code snippets (such as if/else, for, while, while/else, try/catch, file process, andclass snippets and class examples for oop (polymorphism, encapsulation, inheritance, etc.).If you don't use a method don't worry this extension contains a lot of code examples for each python method.This extension is not just a code snippet, it will also be useful for learning the python programming language.You will learn all python methods with a lot of code examples.For example, if you want to use the string replacement method, you just need to use .replace.But if you don't know how to use the replace method then use string.replace =>10- JupyterLink: https://github.com/Microsoft/vscode-jupyterA Visual Studio Code extension that provides basic notebook support for language kernels that are compatible with Jupyter Notebooks today. Many language kernels will work without any modifications. To enable advanced features, modifications to the VS Code language extensions may be necessary.Notebook supportThe Jupyter Extension uses VS code's built-in notebook support. This interface offers a number of advantages to notebook users:Out-of-the-box support for VS Code's wide range of basic code editing functions, such as hot output, search and replace, and code folding.Editor extensions such as VIM, bracket coloring, linters and many more are available while editing a cell.Deep integration with the general workbench and file-based features of VS Code, such as outline view (table of contents), breadcrumbs, and other operations.Fast load times for Jupyter notebook (.ipynb) files. Any notebook file is loaded and rendered as quickly as possible, while execution-related operations are initialized behind the scenes.Includes a notebook diff tool, which makes it easy to compare and visualize differences between code cells, results and metadata.Extensibility beyond what the Jupyter extension provides. Extensions can now add their own specific language or runtime to notebooks, such as the .NET and Gather interactive notebooks.Although the Jupyter extension comes with a comprehensive set of the most commonly used renderers for output, the marketplace supports installable custom renderers to make working with your notebooks even more productive. To get started writing your own, check out the VS Code renderer api documentation.You can also read data science posts in Spanish here.ConclusionThere are many extensions that you can use with your Visual Studio Code, and deciding which one to use will involve testing, reviewing utilities, use cases and so on in order to make your work easier while coding!Also read: Why Decorators In Python Are Pure Genius?

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