Top 10 Data Science Leaders You Should Follow

Admond Lee
Apr 30, 2021

Contents Outline

Top 10 Data Science Leaders You Should Follow

Apr 30, 2021 10 minutes read

Having great mentors to learn from has been a huge blessing throughout my data science career, or even in life.

I still remember the defining moment when I decided to transition from physics into data science during my final year in university.

The jump was… scary.

The self-doubt was real. The fear was real.

Believe it or not, I was just like any other aspiring data scientists who started out by learning from online courses, reading relevant textbooks and joining Kaggle.

But I still felt something was missing.

Throughout that period, I had been learning by myself without really understanding how data science “works” in real world and how to become a great data scientist.

I felt that I needed some guidance. I needed to learn from mentors.

And this was when I started learning from the data science leaders on LinkedIn, which tremendously helped in my learning journey and even boosted my data science career to the extent I’d never imagine possible with abundant learning opportunities.

In fact, they are the reason why I’m giving back to the data science community by sharing my experience, knowledge and even my mistakes on LinkedIn and Medium.

If you’ve been following my articles, I shared the first list of data science leaders to follow one year ago and the list is by no means exhaustive, hence the writing of this article on top of that.

If you’re in data science field, I strongly encourage you to follow these giants — which I’ll list down in the section below — and be a part of our data science community to learn from the best and share your experience and knowledge.

Trust me, learning from these giants could turn out to be one of the life-changing decisions you’ll ever make (at least for me).

Enough of talking… Let’s get started!


Top 10 Data Science Leaders You Should Follow in 2020


1. Cassie Kozyrkov



Cassie Kozyrkov is a legend, to say the least. Period.

She currently works as a Chief Decision Scientist at Google who is also an expert in the intersection of statistics, data science, and AI.

Personally, I think she is seriously a rare breed for having deep technical expertise while — at the same time — having the capabilities to break things down into simple ideas for people to understand.

Her thought leadership and ideas in the fields of machine learning, artificial intelligence, and data science have never failed to amaze me as they always give me new perspectives and knowledge which I’d not know have learned from anywhere else.

And that, my friend, is the power of learning from others (aka mentors).

If you’re interested in learning from Cassie — which I highly recommend — check out her posts, articles and videos on Linkedin, Medium, Twitter and YouTube.

In fact, she was the one who inspired me to write this article — P-values Explained By Data Scientist — after watching her sharing on p-value!


What is a p-value? (by Cassie Kozyrkov)

2. Ben Taylor


Ben is a good friend of mine and we first met on LinkedIn during a discussion of certain data science topics.

He is a veteran thought leader around AI with over 16 years of machine learning experience.

He currently works at DataRobot as a Chief AI Evangelist — and yes — he is the go-to guy if you want to learn more about AI and understand the latest trend in this field.

Ben — in my opinion — is by far one of the sharpest minds in the field of AI and he constantly demystifies some of the common misunderstanding in this field with the goal of democratizing the use AI.

If you did video interviews using HireVue requested by companies before during your job search journey…

Guess what?

Ben was the one who led the data science team and developed the first video interview prediction engines called HireVue Insights, and many more AI products!

3. Dat Tran


I’m sure you’re familiar with Dat Tran if you’re active in the data science community on LinkedIn as he constantly shares the latest technology and deep learning updates to the public with a lot of great advice.

If you still have no idea who he is. Do yourself a favour. Check out his sharing on Linkedin and Medium!

Dat Tran is currently leading Axel Springer AI which is the artificial intelligence unit of Axel Springer SE — the largest digital publishing house in Europe — to make AI accessible to everyone within Axel Springer and hence drive innovation with the focus on Computer Vision and NLP problems.

By the way, he is also a renowned keynote speaker and mentor in this field who has helped the data science community at large.

4. Kevin Tran


When I first started out in data science, Kevin was one of the first few data scientists that I met on LinkedIn by sharing our thoughts and learning from each other.

His broad experience in working at various tech startups and deep expertise in developing credit and risk models further justify why I love his sharing so much.

Also, His advice to become a great data scientist in real world environment — technical or non-technical — has been tremendously practical and helpful to my learning journey as we continue to grow together.

Follow his advice and you’ll know what I mean. 😄

5. Jose Marcial Portilla


I’m sure this name sounds familiar to you if you’ve ever taken data science courses on Udemy.

You see.

When I first got started in my data science journey, I didn’t get started by reading a textbook, I didn’t get started by learning from Kaggle.

Instead, I got started by taking this online course — Python for Data Science and Machine Learning Bootcamp taught by Jose Marcial — that gave me a strong foundation and understanding of data science and machine learning in general to move forward.

He is one of the best course creators and instructors that I’ve seen so far and his courses are easy to understand and most importantly, practical to get you started!

If you’re a beginner in data science and want to know gain a better understanding of what this field looks like as well as its applications, then check out his course.

6. Koo Ping Shung



Koo Ping Shung is definitely one of the titans in data science and AI space.

From his humble beginning as a fresh economics graduate to becoming an expert in data science training, instructor and consulting, helping companies build up in-house data science capabilities, is so inspiring!


More interestingly, he is also the co-founder of DataScience SG — one of the largest and active Data Science meetup group in Singapore.

The goal is to educate the public on what to expect from data science, what the job of data scientist truly consists of and also helping business to understand how they can benefit from data science.

I personally went to the meetup a few times for different topics as well as to network with professionals in this space. Definitely the best meetup ever as everybody is so welcoming and has the hunger to learn and grow together.

7. Lex Fridman


Lex Fridman is a research scientist at MIT working on human-centered AI and deep learning approaches to shared autonomy in self-driving cars.

What’s even more mind-blowing is that he did podcasts both as a guest with Joe Rogan (part 1 & part 2) and as a host (Artificial Intelligence podcast with, for example, Elon Musk) before.

Elon Musk: Tesla Autopilot | Artificial Intelligence (AI) Podcast

If you want to understand more about the interaction between human behaviour and AI, or you want to stay at the forefront of AI technology and applications, check out his podcasts, YouTube channel, and of course, his LinkedIn posts where he shared meaningful insights on AI.

Personally, I find his podcasts and Linkedin posts very insightful, especially on the evolution of AI and its interesting connection with human behaviour.

8. Kristen Kehrer


Currently Kristen Kehrer is a Data Science Instructor at UC Berkeley Extension and the founder of Data Moves Me.

Since 2010, Kristen has been delivering innovative and actionable machine learning solutions across multiple industries, including the utilities, healthcare, and eCommerce.

Throughout my whole data science career, Kristen has been my role model/mentor to learn from with her deep experience and knowledge in SQL and analytics space.

In particular, I love the discussion between her and other prominent data scientists like Favio Vázquez on LinkedIn and most importantly, the podcast interview — Humans of Data Science (HoDS)— with Kate Strachnyi!

As you may have already realized, the data science community on LinkedIn is a close-knit community where we interact with one another to share and learn together.

9. Srivatsan Srinivasan


Srivatsan is currently a Chief Data Scientist/Architect at Cognizant.

His experience in building complex analytical pipelines, machine learning models for extremely complex business process, and helping companies with transformation in data and analytics space, has definitely boosted my understanding in many areas in big data, cloud, and AI.

Reading his insightful posts on LinkedIn has been one of the most rewarding learning activities to constantly keep myself updated with the latest AI technology and the best practices in data science space.

Check out his posts and you’ll know what I mean. I know you’ll love them.😅

10. David Langer


As long as you’re on LinkedIn and looking for data science posts (or content in general) to learn from, chances are you may already have known David.

Besides being a VP of Analytics at Schedulicity, he is also famously known for his great data science teaching as an instructor, blogger and YouTuber.

My vision is a world filled with data literate professionals. — David Langer

He is a huge advocate of 20% of analytics that drive 80% of ROI.

Personally, I’ve been learning a lot from his sharing on LinkedIn and I’d say he is definitely one of the prominent educators in data science space.



Final Thoughts



Thank you for reading.

There’s a long list of data science leaders to follow here, isn’t it?

Trust me.

Learning from mentors who have walked the path will save you tons of time compared to learning everything on your own. Even better, you’ll be well equipped with the knowledge of data science (technical & soft skills) as well as how to become a great data scientist.

And of course, the list of data science leaders here is by no means exhaustive. These are just some of the top data science leaders whom I’ve been following and learning from since the beginning of my data science journey.

And I hope you’ll find their sharing insightful and helpful to you.

⭐ Comment below if you know any other data science leaders that we should follow!👇🏻

Inspired by their contribution, I’m giving back to the data science community by sharing my knowledge and experience along the way to hopefully help more aspiring data scientists.

At the end of the day, we — as a part of the data science community — are here and will always be here to share, help, learn and grow together.

And this is what a community is meant to be.

I hope you enjoyed reading this article and I look forward to having you as part of the data science community.

Remember, keep learning and never stop improving.

As always, if you have any questions or comments feel free to leave your feedback below or you can always reach me on LinkedIn. Till then, see you in the next post! 😄

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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
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.

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