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13 Exciting Data Science Project Ideas & Topics for Beginners [2023]

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Rohit Sharma is the Program Director for the UpGrad-IIIT Bangalore, PG Diploma Data Analytics Program.

Table of Contents

In this Article, you will learn about 13 exciting data science project ideas & topics for beginners.

1. Beginner Level | Data Science Project Ideas

2 . Data Science Projects Ideas |Intermediate Level

3. Advance Level Data Science Projects Ideas

4. Top Data Analytics Projects

Read more to know each in detail.

An Expression on Data Science Project Ideas

Data Science is continuously thriving as a great career option for this generation. It is among the most promising & happening choices altogether. The market is boosting up with more demands for Data Scientists. It has been reported recently that the demand will increase further to many folds in the coming years. So, if you are a data science beginner, the best thing you can do is work on some real-time data science project ideas.

You can also check out our  free courses offered by upGrad under Data Science.

So, if you are an aspiring Data Scientist, it is highly recommended to practice skills to become an efficient professional for this field. After grabbing some very good theoretical knowledge on Data Science, if you are really looking ahead to explore what it seems like to be a professional, then now is the time to do some practical projects.

You must do some of the technical & real-time  Data Science projects  so that it helps you boost your career growth. The more you practice with  Data Science projects , we assure you that you can keep up the pace towards becoming a sound Data Scientist professional.

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Therefore, if you do some live  Data Science Projects , it will enhance your knowledge, technical skills, and overall confidence. But most importantly, if you showcase even a few  Data Science projects  in your resume, then getting a good job is much easier for you. Why so? Because then the interviewer will know that you are really serious about a Data Science career.

Your real-time experience on Live  Data Science Projects  will let you hold a strong grip on Data Science trends & technologies. So, layout your hands on real-time  Data Science projects  & you will know how beneficial it will be for your speedy career growth. After all these discussions, we know that finding that perfect  Data Science Project idea  for your  Data Science project  concerns you even more than its actual implementation.

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In this Data Science blog, we have listed out the names of a few  Data Science Project ideas . And to answer your question – ‘What kind of Data Science project is good to start with?’, we have compiled a few good Data Science Project ideas for you to choose from.

The article also includes some of the best data science projects for beginners, that you can check out. 

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Why Should You Learn Data Science?

Before going further into the different data science project ideas that are available, let’s take a look at some of the reasons why data science projects are considered to be so important in today’s world. 

1. Data is the new driving force behind industries

Needless to say, in today’s technology-driven world, large enterprises across different industries rely heavily on data for everything, starting with their business growth to expansion. Thus, it wouldn’t be too wrong to say that data is the electricity that powers all the industries of today.

Industries make use of data to improve their performance, generate revenue, and provide better customer service. Infact, the automobile industry, too, is harnessing the power of data to improve the safety of their vehicles. Their goal is to create powerful machines that think in the form of data. 

2. Demand And Supply

Although there is a huge abundance of data, there are not enough resources available that can convert this data into powerful products. This basically means that there is still a huge dent in the data scientists, because of a lack of data literacy in the market. 

3. High Paying Job Opportunities

Currently, data science is considered a highly lucrative career. Infact, according to some researchers, a data scientist makes 63% more than the national average salary. Apart from this, data scientists also get to enjoy a position of prestige in the company. This is because companies rely heavily on data scientists to make data-driven decisions and guide the organization in the right direction. 

4. Data Science is the next big thing

As more and more industries are becoming data-driven, there is a constant need for data scientists. The field of technology is becoming more dynamic and new innovations are being made every day. Thus, data science is the career of the future. 

Here are 50  Data Science Project ideas  for you, and in the blog ahead, we are discussing a few of these projects in detail. So let’s begin!

Also, check out our business analytics course  to widen your horizon.

Latest Data Science Project Ideas

We have segmented all the  Data Science Project Ideas  as per the learner’s level. Therefore, you will get a list of a few amazing project briefs for beginner, intermediate & advanced  Data Science project ideas .

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This list of  data science project ideas for students  is suited for beginners, and those just starting out with Python or Data Science in general. These  data science project ideas will get you going with all the practicalities you need to succeed in your career as a data science developer.

Must read : Data structures and algorithms free course !

Further, if you’re looking for  data science project ideas for final year , this list should get you going. So, without further ado, let’s jump straight into some  data science project ideas that will strengthen your base and allow you to climb up the ladder.

1.1 Climate Change Impacts on the Global Food Supply

The first one to make it to the list of data science projects for beginners is climate change impacts on the global food supply.

Frequent Climate change and irregularities are big challenging environmental issues. These irregularities in climate divisions are drastically affecting the human lives residing on the Earth. This Data Science Project concentrates on how the climate impact will highly affect global food production worldwide and how much quantification will impact climate change.  

The main aim of development for this project is to calculate the potentialities on the staple crop productions due to climate change. Through this project, all the implications related to temperatures & precipitation change. It will then be taken into account how much carbon dioxide affects the growth of plants and the uncertainties happening in the climatic conditioning. Hence, this project will largely deal with Data Visualisations. It will also compare the production in various regions at different time zones. 

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1.2 Fake News Detection

You can drive your Data Science career with this amazing Data Science Project idea for beginners – Detection of Fake News using Python language. The act of wrong or misleading journalism on a digital platform or fake news can be detected by this project. Falsifications are spreading out via social media platforms and online channels & digital media to attain any political agenda. 

With this data science project idea, you can use Python language to develop a specific model that can precisely detect whether the news is real journalism or false information.. For this, you need to build a ‘TfidfVectorizer’ classifier and then use a ‘PassiveAggressiveClassifier’ to classify the news into either a “Real” and “Fake” segmentations. There will be a dataset of the shape of 7796×4 dimensions and execute all these in the ‘JupyterLab’.

The main idea of this Data Science project is to develop a real-time machine learning model that can correctly detect social media news authenticity. ‘TF’, commonly known as ‘Term Frequency’, is the total number of times any word will appear in a single document. Whereas, ‘IDF’ or ‘Inverse Document Frequency’ is a calculative measure of the value of a word & it is based on the reputational frequency of its occurrence appearing in the various documents.  

The theory is on the ‘Common words’, if these common words happen to appear in multiple documents with a high frequency then they are considered as less important words. So, what ‘TFIDFVectorizer’ does is to analyze the collection of these documents and then accordingly create a ‘TF-IDF’ matrix to it. 

Along with this, a ‘PassiveAggressive’ classifier will remain ‘passive’ in case the ‘classification outcome’ is correct; but on the other hand, it will change aggressively if the ‘classification outcome’ is incorrect. So, you can create a machine learning model to detect social media news to be genuine or fake news using this Data Science Project idea.

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1.3 human action recognition.

This is a Data Science project on the human action recognition model. It will look at the short videos made on human beings where they are performing specific actions. This model tries to do a classification that is based on actions performed. In this Data science project, you need to use a complex neural network. This neural network is then trained on a specific dataset that contains these short videos. Then there is an accelerometer data that is associated with the dataset. The accelerometer data conversion is done first along with a ‘time-sliced’ representation. Thereafter, you have to use the ‘ Keras ’ library so that you can do training, validation, and testing of the network based on these datasets.

1.4 Forest Fire Prediction

One of the alarming & common disasters happening in today’s world is forest fires. These disasters are highly damaging to the ecosystem. To deal with such a disaster, a lot of money on infrastructure & controlling and handling is required. We can build a Data Science project using ‘k-means clustering’- it can identify any forest fires hotspots along with the severity of the fire at that particular spot.

It can be alternatively used for better resource allocation with the faster response time. Hence, using the meteorological data such as those seasons around which these kinds of fires tragedies are more likely to happen and various weather conditions that worsen them may increase these results’ accuracy levels.

1.5 Road Lane Line Detection

Another Data Science project ideas for beginners include a Live Lane-Line Detection Systems built-in Python language. In this project, a human driver receives guidance on lane detections through lines drawn on the road.

Not only this, it further refers to which direction the driver should steer their vehicle. This Data Science Project application is vital for the development of driverless cars. Hence, you can also develop an application with the powerful capability to identify a track line through the input images or via a continuous video frame.

Read: Top 4 Data Analytics Project Ideas: Beginner to Expert Level

2. Data Science Projects Ideas |Intermediate Level

2.1 recognition of speech emotion .

science projects quora

One of the popular Data Science project ideas is recognition of the speech emotion. If you want to learn the usage of different libraries, this project is perfect for you. You must have seen a lot of editor tools that can tell us how our speech emotion is appearing. This program model can be built as a Data Science project.

In this Data Science project, we will use ‘librosa’ that will perform a ‘Speech Emotion Recognition’ for us. The SER process is a trial process that can recognize human emotion. It can also recognise the speech from the affective states. As we use a combination of a tone and a pitch for expressing emotions through our voice.

The Speech Emotion Recognition model is absolutely possible. However, it can be a challenging project to perform as human emotions are very subjective. The annotation of the human audio is also quite challenging. So, here you will use the mfcc, mel & the chroma features. With this, you will also use the dataset known as ‘RAVDESS’ for the emotion recognition process. In this Data Science project, you will also learn how to develop an ‘MLPClassifier’ for this model.

2.2 Gender and Age Detection with Data Science

science projects quora

So, one of the impressive project ideas on Data Science is the ‘Gender and Age Detection with OpenCV’. With this kind of real-time project, you can easily grab your recruiter’s attention in a Data Science interview.

Talking about the project, the ‘Gender and Age Detection’ is a machine learning project based on computer visioning. Through this Data Science Project, you can learn the practical application of CNN i.e, the convolutional neural networks. Down the line, you will also use models that are trained by ‘Tal Hassner’ and ‘Gil Levi’ for ‘Adience’ dataset.

Along with this, you will also use some files such as – .pb, .prototxt, .pbtxt, & .caffemodel files. Heard about these terms? Read about these files? Understand models too? But do you know how to implement them? Well, you can learn it if you opt to develop a Data Science Project on it. 

It’s a very practical project as you will create a model that can detect any human being’s age & gender through analyses of single face detection via an image. So, with this gender classification in a man or a woman can be classified. Also, the age can be classified among the ranges of 0-2/ 4-6/ 8- 2/ 15-20/ 25-32/ 38-43/ 48-53/ 60-100. 

But due to various factors such as makeup, or brighter dim lighting, or an unusual facial expression, the recognition of the gender and the age from a single source can become challenging. Therefore, in this Data Science project, you will use a classification model instead of a regression model. A lot of practical & technical learning can be grabbed to upscale your technical skills with these kinds of projects. So, take up the challenge & work hard towards it to make an impressive Data Science Resume.

Top Data Science Skills to Learn in 2022

2.3 driver drowsiness detection in python.

An excellent Data Science project idea for intermediate levels is the ‘Keras & OpenCV Drowsiness Detection System’. Driving overnight is not only tough but a risky job too. We have heard of a lot of cases where accidents happen because the driver fell asleep while driving.

Thus, this project can help prevent numerous road accidents that happen due to such cases. This project’s main aim is to recognize whenever the driver may get drowsy & fall asleep while driving. This project uses Python language where you can build a model that can timely detect the sleepy driver behavior and raises an alert alarm through a high beeping alarm.

In this project, you can implement a ‘deep learning model’ & with its use, you can do a classification among images where a human eye is open or close. Not just this, in this model another formula line is to calculate the score.

This score is based on the time period of how long the eyes remain closed. The score is maintained throughout the driving session. If that score increases & crosses a specified threshold, this model will throw workflow automation through which the alarm will start buzzing heavily.

So, with these kinds of Data Science projects implementations, you will learn all the basics of Data Science projects. You will implement it using ‘Keras’ and ‘OpenCV’. So, why are these used? Well, you are using ‘OpenCV’ to detect face & eye movements. Whereas, with ‘Keras’, you can classify the eye’s state whether it is open or close while using techniques of the Deep neural network.

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2.4 Chatbots 

science projects quora

Chatbots are increasingly becoming popular these days. So, for a Data Science project, it is a high on-demand requirement by almost all organizations. It is an essential segment of the business nowadays. These days, chatbots are playing a very crucial role in businesses. They are helping business lines to save an enormous amount of time on their human resources. It is used to provide an improved and personalized business service simultaneously.

There are many businesses who are offering services to their customers. To provide customer service on a large scale, it requires a lot of human resources, ample time, and many efforts to handle each customer on time. On the other hand, these chatbots can provide automation for customer interaction services simply by answering a set of frequent questions commonly inquired by the customers. 

There are 2 types of chatbots available in today’s time: Domain-specific chatbot and Open-domain chatbot. The domain-specific chatbot is most often used for a particular problem solution. These are customized in a very strategic & smart manner so that they work strategically & effectively in relation to domain specifications. The second one, ‘Open-domain’ chatbots, needs a lot of training materials that are too continuously because, as per the name, it is developed to answer any kind of question.

Technically speaking, the chatbots are trained using the ‘ Deep Learning ’ techniques. They need a dataset with vocabulary listing, lists consisting of a common sentence, an intent which is behind them, and then the appropriate responses. This is one of the trending data science project ideas. 

The ‘Recurring Neural Networks’ (The RNN’s) are the common methodologies to train chatbots. These bots contain encoders that can update the states as per the input sentences alongside intent. It then passes the specified state to the Chatbot.

Thereafter, the chatbot uses the decoder to search an appropriate & subsequent response according to inputted words & also besides the intent. With this Data Science project, you can easily learn Python language implementation as the complete project is itself made in Python. You can upscale your Python technical skills to a certain extent.

Learn: How to Make a Chatbot in Python Step By Step

2.5 Handwritten Digit & Character Recognition Project

science projects quora

With this Data Science Project idea on ‘Handwritten Digit & Character Recognition with the help of CNN, you will practically learn Deep Learning concepts. So, if you are a budding Data Scientist or an enthusiast of machine learning then this is the perfect Data Science project idea for you. For this project development, you will use the ‘MNIST dataset’ of hand-written digits. This is a great project to get hands-on experience with Data Science as you will learn amazing ways that are involved in the process of project building. 

As discussed, this project is implemented through the ‘ Convolutional Neural Networks ’. After this, for a real-time prediction, you will build a creative graphical- based user interface for drawing digits on the canvas, and thereafter you will build a model that will be used for the prediction of the digits.

The project’s focus is on developing the computer’s ability & to empower the computer system so that it can recognize characters in hand-written formats by humans. It will then evaluate it further to understand it with reasonable accuracy. With this project implementation, you can learn the practical implementation of the ‘Keras’ and also ‘Tkinter’ libraries.

These are some  intermediate data science project ideas on which you can work. If you still like to test your knowledge and take on some tough projects

3.1 Credit Card Fraud Detection Project

science projects quora

After implementing easy projects, you can now move to some advanced Data Science project ideas to learn more concepts. One such idea is Credit card Fraud Detection. With this project, you will learn how to use the R with different algorithms such as Decision Tree, Artificial Neural Networks , Logistic Regression, and the Gradient Boosting Classifier.

You can also learn to use the ‘Card Transactions’ datasets to classify the credit card transaction as a fraudulent activity or a genuine transaction. You will also learn to fit all the different types of models along with the plot performance curve for all of them. This is one of the best data science project ideas one can find. 

3.2 Customer Segmentations

science projects quora

This is one of the most popular Data Science projects in the field of Data Science. Digital Marketing is an up & advanced way to target an audience for the companies through their online marketing activities for marketing purposes nowadays. So before running a marketing campaign, different customer segmentation is first done.

Customer Segmentation is among very popular applications of indeed unsupervised learning. Hereby, using clustering methods, companies can now easily identify the customers’ various segments for targeting the potential user-base. There are divisions made on customers & groups are formed according to the common characteristics such as gender, interest areas, age, and habits.

Based on these details they can effectively market each customer group. The project uses the ‘K-means clustering’ and you will learn how to perform visualizations on distributions such as gender and age. Customers annual incomes & average score values can also be analysed.

3.3 Traffic Signs Recognition

science projects quora

This project aims to develop a model to achieve high accuracy in self-driving car technologies using CNN techniques. Traffic signs and traffic rules are of utmost importance for every driver and it must be followed to avoid accidents. To follow these rules, the user must understand how the traffic signals appear to be. 

It’s a general rule that to obtain a driving license, an individual has to learn all the driving signals. But for autonomous vehicles, there are programs developed such as the ‘Traffic signs recognition’ using CNN, where you can learn how to program a model that can precisely identify various kinds of traffic signals by the input of an image.

There is a dataset called the ‘German Traffic signs recognition benchmark’. It is commonly known as the GTSRB that is used in the development of a Deep Neural Network for recognizing the class of all the traffic signs belonging to which class type. You will also learn practical knowledge of building a GUI for application interaction.

Know more: 10 Exciting Python GUI Projects & Topics For Beginners

Top Data Analytics Projects

Now that you have learned some of the best data science project topics, let’s take a look at some of the top data analytics projects ideas and data science topics that are currently trending in the market. 

1. Web Scraping

Knowing how to scrape data not only adds that boost to your portfolio, but also with the help of this, you can actually explore and use data sets that match with your interests, without the need for compiling the same. Various tools like Beautiful Soup or Scrapy are actually available with the help of which you can crawl the web for interesting data. 

2. Data Cleaning

One of the most important tasks for every data analyst is cleaning data to make it ready to analyze. Data cleaning, also called data scrubbing is basically ensuring that the data is consistent, by removing any duplicate or incorrect data and managing the holes in the data. This is one of the best data science topics that is boun dto add value to your candidature. 

3. Exploratory Data Analysis

To put it simply, data analysis is all about answering questions with data. With the help of EDA, you can explore different questions that you want to ask. 

4. Sentiment Analysis

Last but not least is sentiment analysis, which is basically a technique in natural language processing that determines whether the data is neutral, positive, or negative. They are especially useful for public review sites and social media platforms. Furthermore, with the help of sentiment analysis, you can also detect a particular emotion based on the list of words, and their corresponding emotions. This is known as a lexicon. 

Read our popular Data Science Articles

Bottom line.

In this article, we have covered top data science project ideas . We started with some beginner projects which you can solve with ease. Once you finish with these  simple data science projects, I suggest you go back, learn a few more concepts and then try the intermediate projects.

When you feel confident, you can then tackle the advanced projects. If you wish to improve your data science skills, you need to get your hands on these data science project ideas. Now go ahead and put to test all the knowledge that you’ve gathered through our data science project ideas guide to build your very own data science project!

We wish that you will drastically improve all the skills of Data Science with the project ideas we presented to you here in this blog. But in case you are new to the Data Science field & would love to learn the Data Science & build similar models for the technological advancements, we recommend you to check out the online course on  upGrad & IIIT-B’s PG Diploma programs  to learn & upskill in the Data Science world with experienced & expert professionals.

With the right set of knowledge, guidance & tools, you can learn any Data Science project. No level is difficult for learners. That’s why all these live projects are a perfect way to enhance one’s skills and fast progress in attaining mastery. At  upGrad , we offer 3 Data Science Online Certification:

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From IIIT Bangalore

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Try these Data science online certifications by upGrad as we are sure that they will help you in your Data Science career path. Therefore, don’t delay! Start your practice now!

How to make a good Data Science project?

The following points should be kept in mind before starting any Data Science project: Choose the programming language that you are comfortable with. However, the language chosen should be one of the in-demand languages such as Python, R, and Scala. Use datasets from trusted sources. You can use Kaggle datasets. Moreover, make sure that the dataset you are using does not contain errors. Find errors or outliers in your dataset and rectify them before training your model. You can use visualization tools to find the errors in your dataset.

Describe the major components that a Data Science project should have?

The following components highlight the most general architecture of a Data Science project: Problem Statement : This is the fundamental component on which the whole project is based. It defines the problem that your model is going to solve and discusses the approach that your project will follow. Dataset : This is a very crucial component for your project and should be chosen carefully. Only large enough datasets from trusted sources should be used for the project. Algorithm : This includes the algorithm you are using to analyze your data and predict the results. Popular algorithmic techniques include Regression Algorithms, Regression Trees, Naive Bayes Algorithm, and Vector Quantization. Training Models : This involves training your model against various inputs and predicting the output. This component decides the accuracy of your project. Using proper training techniques can produce better outcomes.

What are the skills required to be a Data Scientist?

The following are the essential skills and tools any Data Science enthusiast should master: 1. Statistical Skills including Probability 2. Analytical Skills to analyze and test the data. 3. Programming languages such as Python, R, Scala, and JAVA. 4.Data Visualization Tools such as Power BI, Tableau 5. Algorithms including Regression, Decision Trees, Bayes Algorithm 6. Calculus and Algebra. 7. Communication and Presentation Skills 8. Databases such as SQL 9. Cloud Computing to manage the resources Apart from these technical skills, a professional Data Scientist should also have some soft skills to provide value to the company and improve interpersonal relationships. These skills include critical and curious thinking, business orientation, smart communication skills, problem-solving, team management, and creativity.

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Easy Science Projects

Find an easy science project that you can do using common household materials. These easy projects are great for fun, home school science education, or for school science lab experiments.

Mentos and Diet Soda Fountain

Alohalika / Getty Images

All you need is a roll of Mentos candies and a bottle of diet soda to make a fountain that shoots soda into the air. This is an outdoor science project that works with any soda, but clean-up is easier if you use a diet drink.

Slime Science Project

science projects quora

MamiGibbs / Getty Images

There are many different ways to make slime. Choose from a collection of recipes to make slime using materials you have on hand. This science project is easy enough even young kids can make slime.

Easy Invisible Ink Project

science projects quora

PRG-Estudio / Getty Images

Write a secret message and reveal it using science! There are several easy invisible ink recipes you can try, using corn starch , lemon juice , and baking soda .

Easy Vinegar and Baking Soda Volcano

science projects quora

EvgeniiAnd / Getty Images

The chemical volcano is a popular science project because it is very easy and yields reliable results. The basic ingredients for this type of volcano are baking soda and vinegar, which you probably have in your kitchen.

Lava Lamp Science Project

science projects quora

fstop123 / Getty Images

The type of lava lamp you would buy at the store actually involves some fairly complex chemistry. Fortunately, there is an easy version of this science project that uses non-toxic household ingredients to make a fun and rechargeable lava lamp.

Easy Ivory Soap in the Microwave

science projects quora

Stefan Cioata / Getty Images 

Ivory Soap can be microwaved for an easy science project . This particular soap contains air bubbles that expand when the soap is heated, turning the soap into a foam right before your eyes. The composition of the soap is unchanged, so you can still use it just like bar soap.

Rubber Egg and Chicken Bones Project

science projects quora

Chris Whitehead / Getty Images

Vinegar reacts with the calcium compounds found in egg shells and chicken bones so that you can make a rubbery egg or bendable chicken bones. You can bounce the treated egg like a ball. While the project works with both fresh and cooked eggs, be sure you bounce a cooked egg because the yolk of a raw egg stays soft. The project is extremely easy and yields consistent results. It's great for first graders .

Easy Crystal Science Projects

science projects quora

Vudhikul Ocharoen / Getty Images

Growing crystals is a fun science project . While some crystals can be hard to grow, there are several you can grow quite easily, such as Easy Alum Crystals , Copper Sulfate Crystals , and Borax Crystal Snowflakes .

Easy No-Cook Smoke Bomb

science projects quora

Jess Escribano / EyeEm / Getty Images

The traditional smoke bomb recipe calls for cooking two chemicals over a stove, but there is a simple version that doesn't require any cooking. Smoke bombs require adult supervision to light, so even though this science project is extremely easy, use some care.

Easy Density Column

science projects quora

There are several common household chemicals that may be layered in a glass to form an interesting and attractive density column. The easy way to get success with the layers is to pour the new layer very slowly over the back of the spoon just above the last liquid layer. If your hands are shaky, pouring layers down the side of a sloping glass works, too.

Chemical Color Wheel

science projects quora

You can learn about how detergents work by doing the dishes, but this easy project is much more fun! Drops of food coloring in milk are pretty unspectacular, but if you add a bit of detergent you'll get swirling colors.

Bubble "Fingerprints" Project

science projects quora

You can capture the impression of bubbles by coloring them with paint and pressing them onto paper. This science project is educational, plus it produces interesting art.

Water Fireworks

science projects quora

Taya Johnston / Getty Images

Explore diffusion and miscibility using water, oil and food coloring. There's actually no fire at all in these 'fireworks', but the way the colors spread out in water is reminiscent of the pyrotechnic.

Easy Pepper and Water Project

science projects quora

Sprinkle pepper onto water, touch it, and nothing happens. Remove your finger (secretly applying a 'magic' ingredient) and try again. The pepper appears to rush away from your finger. This is a fun science project that seems like magic.

Chalk Chromatography Science Project

science projects quora

Use chalk and rubbing alcohol to separate out the pigments in food coloring or ink. This is a visually appealing science project that yields quick results.

Easy Glue Recipe

science projects quora

You can use science to make useful household products. For example, you can make non-toxic glue based on a chemical reaction between milk, vinegar, and baking soda.

Easy Cold Pack Project

science projects quora

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Make your own cold pack using two kitchen ingredients. This is an easy non-toxic way to study endothermic reactions or to chill a soft drink can if you prefer.

Watch Now: How to Make Silly Putty to Demonstrate Chemical Reactions

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Tarry Singh

Nov 2, 2017

My Most Valuable Advice to Budding Data Scientists on Quora and Coursera — Never stop learning!

I have been responding to a lot of requests ranging from:

“How do I get started in the field on Machine Learning, Deep Learning or Artificial Intelligence ”
“How to I advance from the basics that I know today… ”

or even more honest and confronting ones like this from Coursera, where I am a mentor to a few thousands of deep learning experts and enthusiasts.

“100/100 on assignments doesn’t mean you are a proficient…” asked by Noreddine Belhadj Cheikh in @Coursera

All valid questions, but how to move forward?

I have tried to answer these questions in the past 6 months from both my own learning experience as well as from a career perspective.

This is no regular bullshit and touchy-feely advice you read all over the place, this is how I’ve learned stuff.

Starting from the ground floor and racing upwards like a tiger!


Question 1 : what is the best programming language to learn for a job in 2017, my answer →>> don’t waste time sitting or letting other scare you; get started.

Start with Python and …

6–12 months, you’ve learned programming!

NOTE : I got a lot of critique on this above answer on Quora, I do realize that this rather a steep climb for most, but my hope is to get you started and then you can get accustomed to your own pace.

But, do please hurry!

Question 2 (more focused) : How do I start learning artificial intelligence? Is it possible to get research work in the field of A.I? Are there open source projects where I can contribute?

My answer: →>> if you know the basics, go deeper to learn advanced concepts.

Here’s the FSP* [fastest shortest path] I use to learn new things. This should help you.

I can guarantee you that within a year you would have experienced a metamorphosis.

*some linear algebra understanding — even high school math is good. Bit of tech / programming concepts will help you move faster.

For latest ai / deep learning tutorials feel free to follow these that I maintain daily! https://github.com/TarrySingh/Artificial-Intelligence-Deep-Learning-Machine-Learning-Tutorials

Question 3 (reality check) : Can I get a machine learning job if I finish Andrew Ng’s Deep Learning Specialization?

My answer: →>> getting job is a combination of loads of other stuff, not just math.

Getting a job — besides your own (hopefully fully loaded) learning track and plan, has also got to do with things like :

And still I think Andrew’s course is important to follow because it gives you the opportunity to learn from a very passionate person.

Hope this helps.


Question: 100/100 on assignments doesn’t mean you are a proficient, my answer: →>> don’t worry and keep going deeper, job will come eventually as well.

Great question and you make a good point!

The assignments are “easy” because you’re guided how to type a few lines of code in the graded block to complete the assignment.

Yes, that’s easy to score.

My advice to all learners/enthusiasts/proficient folks is to do the following:

Dig deeper : Yes, understand how all the data you are getting was preprocessed.

Play with data sets : How you split them into test/dev/Val/train set.

Do python the hard way / think of it like a language : Type indeed all of the code by hand to understand why you’re doing what you’re doing (I started doing that from my first assignment onwards)

Go and look for more treasures : You have to go out there and participate in real, hands on, peer reviewed even, hardcore projects.

Think of your own gem you wish to reveal/explore : Start thinking of your own projects and get yourself on GitHub.

Have tested your gem? Go build your app : here are some real ideas: Pick up one area of expertise such as NLP , go deep into a field , say chatbot to help lonely teenagers.

Doing your certification is the beginning of a journey to a place you want to be at.

Do you know where that is?

If not, find it!”

(some asked for harder programming assignments): More “hardcore” programming problem sets?

My answer: →>> don’t obsess over theoretical victories, find real-world problems to solve..

For coding and solving more difficult problems, just look around on Coursera and elsewhere. You will find enough challenges to solve. You have fastai, Udacity to play with stuff , enough research projects that require further inspection and finally do some awesome work that is awaiting.

Key lies in learning and if certification leads to self-satisfaction of “Yep, I have a cert”, then the purpose is defeated.

If you learned how and why RMSE worked it’s great but if you thought that it actually doesn’t make sense and there’s a better way, then you’re on to something.

If you think ReLU is great and you do it day in, day out. but if you think there’s a better way, like a recent paper about Swish that was recently published, then you may be on to something.

Here’s a summary

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Founder & CEO deepkapha.ai | 2.5 decades in industry tech and data | Entrepreneur, AI/ML/DL/NS Researcher

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Quora for College Full Stack Academic Project

Wikipedia is a collection of Human Knowledge whereas Quora for college is a collection of Individual Knowledge. Being on Quora for college will make you a better thinker, which will be handy in whatever else you may pursue. It’s a bit like going to school, except way better.

Fortunately, many of you humans have been enlisted to provide both questions and answers as a cover, maintaining the appearance of just another innocuous website. Just as the purpose of humanity is for the creation of the one great individual Quora is the quagmire from which springs ‘The Wonderful Lizard of Awes’.

Quora for college’s project mission is to share and grow the world’s knowledge. Quora for college is a knowledge exchange platform on the college campus, which can be shared by anyone who knows something. Sharing knowledge is a part of wisdom. It will reach the people who have a desire to acquire knowledge.

The main aim of developing the Quora-related website for college students is to quality communication between the students regarding the question and answer-based platforms. This project was developed using the Full-stack.

Our project Quora for College will be divided into different modules

The login/signup page: There will be a validation form for the login to the website and login details will be stored in the database. And it is mandatory to log in foe entering the website without login/signup you will not able to access the website. We will also provide the way to login through google means Gmail or sign in with Facebook.

Ask Question: Quora for College will provide you a better chance to add a question of your interest and wants public opinion on it. Then Quora for College is best for you.

Reply to the question: Quora for College will provide you with a better chance to showcase your knowledge. It allows you to answer the questions asked by others on subjects in which you’re knowledgeable.

Feed: Quora for College will provide you feed option in which you will be able to see questions related to the different topics.

Mern Stack was used to develop this Academic project. Mern Stack Consists of

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Top Quora Data Science Writers and Their Best Advice, Updated

Get some insight into tips and tricks, the future of the field, career advice, code snippets, and more from the top data science writers on Quora.

This post is based on Most Viewed Writers in Data Science , the 10 writers with the most answer views in the last 30 days, as retrieved on June 29, 2017.

Just so there is no confusion, please note that this post is "authored" by me, but none of the information contained herein -- from the questions to the answers -- has anything to do with me. I simply edited these informative responses together.

Data Science @ Quora

  1. Håkon Hapnes Strand , Data Scientist - 255,104 views, 173 answers

Excerpt from answer to: What is a "full-stack" data scientist?

I haven’t heard the expression being used really, but here is my take on what it means: Data scientists build predictive models. That’s the core of what they do. In addition, they need to know a little bit of: Data engineering Software engineering Business analysis A full-stack data scientist would be able to seamlessly perform the role of a data engineer, software engineer, business analyst and data scientist. If you needed someone to develop an app, the FSDS could step in and do it. If you needed someone to set up a data warehouse, or to analyze the strategic management processes of a business, the FSDS could do it.

  2. Mike West , SQL Server and Machine Learning enthusiast - 127,776 views, 45 answers

Excerpt from answer to: Is Python still relevant in data science given the rise of Scala (+Spark)?

Scala and Spark aren’t Python rivalries they are friends. I’ve been saying this for sometime now. Python is and will be the gold standard for machine learning over the next ten years. The only Python competitor is R and I’ll be honest, in the real world everyone’s using Python. You’ll see a lot of R at the college level but not in the applied space. Python simply has too much of a head start. Big Data is simply about getting any data (almost always unstructured data) into a format that can be modeled. Scala and Spark are just tools you can use to do that on very large data sets. TensorFlow wasn’t written in Scala. Don’t get caught up in one or two articles even if they are written by Andrew Ng. Do you own research.

  3. Corrin Lakeland - 117,841 views, 87 answers

Excerpt from answer to: What will data scientists be working on in 5 to 10 years from now?

This brings me to the future. Over the next five years I expect to see lots of companies that are currently claiming to be involved actually trying using it on serious projects. I expect a good chunk of those projects to fail and the whole industry to have generally matured with far more understanding of what works and what doesn’t. Look at the number of GUI tools that support machine learning now. Things like Excel add-ons that automatically cluster data. Give it five years and I expect most people to think only of them when they think about data science. In ten years I think fashion will have well and truly moved on. Data Science will be a skill that is common and expected in other disciplines and specialist data scientists will be looked at a little strangely. You will also have a situation where is is common and normal for the data that is captured by systems to be amenable to data science, as opposed to what is happening now where most data is structured in a way that requires significant manipulation.

  4. William Chen , Data Scientist at Quora - 117,834 views, 195 answers

Excerpt from answer to: Why did you choose to work in data science over quantitative finance?

The summary of all of the reasons I’m about to list was that I chose data science since I was more passionate about it . Here are 5 of the more specific reasons that led to my passion for data science. Excitement over a new, emerging, and growing career path - This decision was made sometime 2013 and 2014, when data science was even more new and uncertain than it was today. The idea of entering something where things were still developing and new appealed to me, and still does today. I try not to base my decisions based on hype - so this bullet is more about how the data science field was growing and would have a place for me rather than how it was hot. Familiarity towards data science - This is arguably the weakest reason on the list, but by the time that I had to choose what I would work on full-time, I had already had two data-science-related internships under my belt: one at Etsy (company) and one at Quora (company). I had great experiences at both of those internships, so choosing to work in data science full-time was a happy known quantity for me. Interest in working on a consumer internet product - I’ve had a longtime fascination with consumer internet products and have basically been excited to watch this whole space grow ever since I got access to dial-up. Working in data science was a unique opportunity for me to become a part of the consumer internet world I’ve been so fascinated by. Intrigue of working on a product thats new and upcoming - Consumer internet products were always interesting to me since they live in the land of uncertainty and could potentially become really big (or just fail). The intrigue of working on a product that could potentially become really important and knowing that you had a small role in it was tempting. Commitment towards knowledge-sharing - I’ve always been committed to sharing thoughts and ideas, either through being a teaching fellow for Harvard Stat 110 or writing as much as I can on Quora. Tech in general has a culture of meetups and blog posts and Quora answers and panels and invited talks. The same is not true in the secretive world of quantitative finance.

  5. Clayton Bingham , Researcher in Center for Neural Engineering at University of Southern California - 108,512 views, 8 answers

Excerpt from answer to: In Python, how can I save data from a website to CSV using BeautifulSoup?

The lazy way would be to do something like this: Once you have your data in the dataframe you can do whatever parsing/reformatting you want. Or, if you only need this once you can just do that with Excel or something. I hope this helps!

  6. Lili Jiang , Quora Data Science Manager - 88,461 views, 8 answers

Excerpt from answer to: As a data scientist, what tips would you have for a younger version of yourself?

First and foremost, is data science what you think it is? 9 out of 10 aspiring data scientists I come across equate machine learning with data science. “Data Science” is a loaded, catch-all term. Machine learning is a part of it, but at many major tech companies, product analytics is also an integral part of the data science team . Product analytics is a hidden gem. It is fun but doesn’t get talked about nearly as much. This includes: A/B test design Design metrics: Let’s take a video platform as an example. What is the best metric to optimize for that best represents user satisfaction? Should it be number of videos watched? Time spent watching videos? Percent of users that come back in a week to watch another video? Investigate why metrics change: Why is there suddenly a spike in activity in this cohort of users? Understand product mechanics: How do button X and feature Y improve the product? Should we redirect page A to B to C or go from A straight to C? Identify trends and offer strategic suggestions: Argue with data that the company should invest in ______ area to stay competitive in the field.

  7. Zeeshan Zia , PhD in Computer Vision and Machine Learning - 70,564 views, 24 answers

Excerpt from answer to: Is AI over-hyped in 2017?

Yes and no, depending upon which community you are talking about. If you are talking about the academic research community, its not over-hyped. There have been major breakthroughs in AI over the past couple of years, and the celebration is certainly justified. In my own area of object recognition, we went from ~35% accuracy (mean average precision on Pascal VOC) to above 65% in just 3–4 years. Previously, we were advancing by 1–2% per year, despite object recognition being the hottest area of computer vision with the largest fraction of papers appearing in top conferences every year. Deep learning also made major breakthroughs in reinforcement learning, which is what yielded successes in general Atari game playing, and beat world grand master in Go decades ahead of expectations! It has finally enabled speech recognition to achieve useable levels of accuracy.

  8. Jason T Widjaja , Business and analytics geek. Likes his brother. - 60,837 views, 167 answers

Excerpt from answer to: What is the risk of the hype around analytics/data science dying off, leaving lots of unemployed analysts?

Fundamentally, I do not see data science dying off anytime soon. As long as: people want to make better decisions (always), people care about what the future holds (forever), people and companies who do it well benefit (always) data points available continue to increase (forever), the tools and techniques we have continue to improve (you get the idea).. ...Analytics and data science is not going everywhere. Disclaimer: extremely biased sample size of one.

  9. Roman Trusov , Master's Information Technology & Data Science, Skolkovo Institute of Science and Technology (2018) - 57,815 views, 139 answers

Excerpt from answer to: How should a data scientist handle versioning, both for pipeline code and models?

To get the best from version control system it’s better to separate them. Keeping code in version control system just like any other code is the only logical way, because if you, as a DS, perform some heavy ETL or if your code makes decisions that can bring/cost a lot of money, there’s no way it’s going around code review. No. Way. For some things that are more typical for data scientists, though, I don’t think that storing Jupyter notebooks in version control is a good practice. You can’t see a decent diff on them, they are not “production code” and in general, when you are finished with something, you want to push at least a “camera-ready” python script. Jupyter notebooks are great for experiments and demonstrations, but outside of these cases there’s always something better.

  10. Shweta Doshi , Co-Founder GreyAtom,Data Science Immersive Learning school - 50,866 views, 123 answers

Excerpt from answer to: What is the essential knowledge and skills are required to start working as data scientist?

Essential knowledge you need to get yourself acquainted with falls under 3 categories i.e Programming, Maths and Science. As a data scientist you will be expected to take a business problem and translate it to a data question, create predictive models to answer the question and storytell about the findings. Statisticians that focus on implementing statistical approaches to data, and data managers who focus on running data science teams tend to fall in the data scientist role. Data scientists are the bridge between the programming and implementation of data science, the theory of data science, and the business implications of data.

  Related :

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Top 5 Must-Read Answers – What does a Data Scientist do on a Daily Basis?


I’m a curious person by nature. Whenever I come across a concept I haven’t heard of before, I can’t wait to dig in and find out how it works. This has come in quite handy in my own data science journey.

But before I landed my first break in data science, I was always curious about what data scientists actually did every day. Was I supposed to simply build models all the time? Or was the oft-quoted saying about spending 70-80% of our time cleaning data actually true?

I’m sure you have asked (or at least wondered) about this too. The role of a data scientist might be the “sexiest job of the 21st century”, but what does that entail on a day-to-day basis?

what does a data scientist do

I decided to research this. I wanted to expand my horizons and understand how data scientists look at their role in different domains (such as NLP). This helped me gain a broader understanding of our role and why we should always read different perspectives when it comes to data science.

So, here is a list of the top 5 answers to help you get a sense of what the typical routine of a data scientist is. Prepare to be surprised – building models isn’t the primary (and only) function in a data scientist’s day-to-day tasks!

I also encourage you to take part in a discussion on this question here . This will enrich your current understanding of what a data scientist does and your thoughts will foster a discussion among our community!

Note: I have taken the answers verbatim from Quora and added my thoughts right at the beginning of each answer. This will help you get a good perspective of what the answer covers without diluting the author’s thoughts. Enjoy!

Machine Learning is Very Process Oriented – Mike West

I like this answer because it’s crisp, to-the-point and simple. The author has even designed a flow diagram and explained his thought process in a wonderfully illustrated way. Here is his answer in full:


Machine learning engineers spend a ton of time in the first two pictures (or stages). The fun part is really in the third stage but it’s only a small part of what happens in the real world.

Some key things to keep in mind about data science in the real world:

A Percentage-wise Breakdown of a Data Scientists’ Day-to-Day Role – Vinita Silaparasetty

I really like the use of visualization by Vinita. The percentage-wise description of each data science task is helpful and insightful. Vinita has also leaned on her experience to explain the step-by-step work a data scientist does. It’s a must-read answer!

Contrary to popular belief, Data Science is not all glamour. The following survey results by CrowdFlower accurately sum up a typical day for a Data Scientist:


There is a lot of backtracking involved. Sometimes you even need to be able to predict what consequences removing/adding a variable might have.

Data Scientist Perspective from a Small-Sized Company – Justin Fister

This is a superb answer and one I can relate to. Note that machine learning, the most anticipated aspect of a data scientist’s job, only occupies 5% of the total time! Just like Vinita, he has also explained his tasks in terms of percentage. Here is Justin’s view:

The “Data Scientist” is a bit of a Myth – Tim Kiely

The author, Tim Kiely, uses a Venn diagram to explain what data science is. Just take a look at this Venn diagram below – it will blow your mind. Tim additionally talks about what data scientists are supposed to be by taking a somewhat contradictory view of the general definition. Here is Tim’s answer:

The “Data Scientist” is a bit of a myth, in my opinion. Not to say they aren’t out there but they are far rarer than is popularly understood and are more of the exception than the rule.

I liken it to the “Web Master” title of the dot-com bubble – these supposed people who could do full stack programming, front end development, marketing, everything. All of those roles/skills were always specialized and remain so today.

“Data Scientists” are supposed to be database architects, understand distributed computing, have a deep understanding of statistics AND some area of business or field expertise. That’s asking a lot when any one of those skill sets can take a career to build.


The Data Scientists I’ve worked with typically have a Ph.D. in A.I. or Machine learning and are effective communicators, which gives them the ability to direct the analysts, DevOps people, programmers and DBA’s at their disposal to solve problems with data-driven solutions. They outline the desired solution and leave it to their teams to fill in the gaps.

Machine Learning Engineer Working on NLP Tasks – Evan Pete Walsh

Let’s drill down into a particular specialization of machine learning. One of my favorites – Natural Language Processing (NLP) ! I wanted to bring out a machine learning engineer’s view here (a role every data scientist should become familiar with). Check out Evan’s full response:

Currently working on NLP, for the most part, including intent classification and entity extraction. Here’s a typical day for me:

The data scientist role is truly multi-faceted, isn’t it? A LOT of aspiring data scientists assume that they will primarily be building models all day long but that simply isn’t the case.

There are all sorts of tasks involved in a typical data science project which you’ll find yourself working on day-to-day. I quite like that because it opens up avenues to learn new concepts and apply them in the real world.

I’ll be posting some more career-related articles on Analytics Vidhya, so stay tuned and keep learning!

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Shubham Singh says: June 29, 2019 at 1:06 am

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JavaScript in Plain English

Gurdip Singh

Nov 27, 2020

My Computer Science Answer Went Viral On Quora

What are some tips for first-year (freshman year) computer science students.

Three years ago, I used to write very actively on Quora. I had just graduated from University and started working at a job that let’s say was not my dream job. I reflected on four years of journey of my graduation and realized things that I could have done differently and which could have changed the course of my career (which, by the way, was able to change later). I had my experience and my new found wisdom that I wanted to share with other people who were starting their graduation. By that time, I had already answered a few random questions and, at the same time, I was reading a lot on Quora. So, one day I was feeding myself with my daily dose of information on Quora and landed on this question.

What are some tips for first-year BTech CSE students?

I immediately started answering the question and poured my whole wisdom in the answer. I provided tips just like an elder brother would share with his younger sibling or, just like one senior would give it to his junior. I knew I was giving the best knowledge I had at that time and ended up spending more than an hour on it. After making sure, I wrote everything I wanted to write, I pressed the “Submit” button and the next thing I know, the answer was read by five thousand people in a single night. I was surprised to see that I was able to connect with so many people. The response has about 17 thousand views, which I know may not sound like a huge number, but I wasn’t even expecting that because earlier my answers used to have a few hundred views at the maximum. So, now that I have picked back up my long lost passion for writing again, I decided to share that answer here and will also try to curry the additional three years of learning at the end.

Here is the answer that I wrote:

Computer Science is a great branch and is quite interesting too if one is passionate about it. You can be a pro in a year or two if you work right from the first year and every day. So, here are a few tips which you can follow to excel in your career: Coding is what makes you different from others. You should read, write, learn, practice coding. Try to find out a new solution in your way. There always exist more than one solution to a problem. There are a number of coding websites available e.g. leetcode, codechef where you can practice and learn a lot. The good thing with CSE is that you can be at par or even ahead of those already in the industry if you work hard but in other branches until or unless you get into the industry you don’t get much practical knowledge. There are a number of online learning sites like COURSERA, EDX and lot of others where you can do an online course on almost every aspect of CSE. Browse those websites and find out what interests you and enrol in those courses and religiously attend courses on time. The problem is it is easy to enrol but difficult to be consistent. So be regular while you attend those courses. Try to find internships immediately after the second year to get practical exposure. Participate in coding events. Don’t resist yourself that you are a starter and don’t know enough. At least participate and you will realize many like you who have the same skill level as you. To motivate yourself to work more join freelancing websites and work on projects and earn. You can search on internet or quora to find more about freelancing. Start your blog to implement what you have learnt. Last but not the least maintain your GPA to average at least.

When I looked at these points again, I realised while a lot of them are still valid today, these tips are more focussed to prepare one for the job market. So, I will try to add perspective to not only prepare yourself for the industry but also to keep things in mind that can shape your career in the long run.

Understand the different career paths

Graduation in computer science comes with an overwhelming number of career options. It is essential to understand different career paths to make the best out of your time. The best way would be to research the different paths one can choose and try experimenting with different options to find out what interests you. The more involved you are in hackathons, conferences, projects and courses, the more you are going to discover what suits you.

Get involved with the tech community

The tech community is very active and continuously contributing towards lifting the fellow developers. To make the best out of college, try to get involved in the community as early as possible. You can get involved in many ways like helping others, writing articles, contributing in open source projects, answering questions on stack overflow. The more involved you are, the more you learn.

Stay up to date with the tech industry

The software industry is ever-changing and growing so much so that learning should never stop. There are newer and better technologies coming every day and, it is essential to stay up to date with them if you want to excel in this field. I try to stay updated by reading articles on Medium and Dev.io . You can read tech blogs from different tech companies to understand what are the challenges they face and how they tackle them.

I hope this article will help you get motivated to learn and make the best out of time. Also, college time is the best time, so try to enjoy as much as you can.

Happy learning!

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Towards Data Science

Shubhankar Srivastava

Jun 8, 2017

Identifying duplicate questions on Quora | Top 12% on Kaggle!

If you are a regular Quoran like me, you have most likely stumbled on duplicate questions asking the same essential question.

This is a bad user experience for both writers and seekers, as the answers get fragmented across different versions of the same question. In fact, this is a problem which is very evident on other social Q&A platforms like StackOverflow. A real-world problem out there, begging AI to solve it :)

For the past month or so i’ve spent my nights cracking my skull open at this problem, along with three thousand other kagglers! It was my second serious competition in a row, it was stressful at times, but i learnt a lot overall — Landed a Top 12% position, a discussion gold medal and a couple of kernel bronze medals :)

A bout the problem — Quora has given an (almost) real-world dataset of question pairs, with the label of is_duplicate along with every question pair. The objective was to minimize the logloss of predictions on duplicacy in the testing dataset. There were around 400K question pairs in the training set while the testing set contained around 2.5 million pairs. Yeah, 2.5 million! A large majority of those pairs were computer-generated questions to prevent cheating, but 2 and a half million, god! I was maxing out my poor 8GB machine every other hour :(

My approach — I started off with the xgboost starter by @anokas, and built upon it gradually. My feature-set involved around 70 features which is on a rather lower range, when compared to the Top Kagglers’ approaches. My features could be broadly classified into NLP-based features, word `embedding based distances and graph-based features. Let me elaborate:

N LP Features : Some very obvious text-based features are percentage of words matching between the two questions, length of the two questions, number of words, number of sentences, number of stopwords, the usual natural language stuff! I tried going ahead with tfidf scores, but that was both not very useful and computationally expensive. Instead, one of the most important features turned out to be weighted word match share, where each word’s weight is the inverse frequency of the word in the corpus(basically idf) — if i have a rare word in both the questions, they might be discussing similar topics. I also had features like cosine distance, jaccard distance, jarowinkler distance, hamming distance, and n-gram matches(shingling).

A library i used extensively for NLP tasks was Spacy, which has been developing some great functionalities lately — spacy similarity turned out to be a good feature too. Some creative ones i thought of were related to the kind of question — whether it is a “How” question or “Why” question — dependent on the first word of the sentence. Strangely, when modelling this i should’ve thought of building “last word similarity” as well, completely missed that! Named entities are a crucial key to understand the context of a question — thus common_named_entity score and common_noun_chunk score were an obvious choice. I pushed out a kernel about calculating similarity via the wordnet corpus, however wordnet fell short of spacy in terms of ease of use, speed and vocabulary size.

Word Embeddings based Distances :

When doing a NLP competition, can Word2Vec be left behind! I feel like word2vec might be the coolest computer science concept i’ve read, i always get blown away by its effectiveness. Every.Single.Time.

Anyway, i had the questions mapped to 300-dimensional vectors in a Sent2Vec format, resulting in a vector for every question. Naturally, distance-based features between vectors were built — cosine, cityblock, jacard, canberra, euclidean and braycurtis. Must mention @abhishek’s scripts for the inspiration for these features. Unfortunately, i couldn’t build the real embedding layers which i could pass in to the lstm layers in keras — my RAM just wouldn’t let me. I’m getting some serious hardware, soon!

G raph Features : In an NLP competition, these graph features played quite the spoilsport! However, it was a nice reminder of how the theories of social networks might apply in datasets like Quora’s question pairs.

Here, every question is a node in the graph and a question pair in the dataset indicates an edge between the two nodes. We can use the graph structure of the test data as well, since we’re not considering the is_duplicate label anywhere — Those 2 million edges contributed a whole lot to the graph! There were heated discussions between kagglers on whether the graph-based features are supposedly “magic features”, which should be released to level the playing field. Anyway, all of these features gave significant boosts to most models:

Transitivity Magic — Continuing on the graph structure, modelling transitivity between the questions was an obvious approach. For eg. if Q1 is similar to Q2 and Q2 is similar to Q3, it implies Q1 is similar to Q3 more often than not(as per our dataset). This was one of the features i had begun building but left it midway, a costly mistake. Many top solutions used this feature in some form or another, a simpler version was to average probabilities of duplicacy between every question and its counterpart’s neighbors.

My Whiteboarding Sessions

Some fuckups.

When a noob like me dives head-first into kaggling big-time, there are bound to be fuckups. Even though i make a conscious effort to keep my pipeline modular and version-controlled, i lost almost a weekend’s worth of work because of a nasty bug in my pipelining. Learning it the hard way, i git reverted and remodelled the pipeline on an ipython notebook . A major challenge that i faced when building features was writing memory-efficient code and not rebuilding previous features, modularity was key. This led to another fuckup — since i couldn’t handle the whole of my testing dataset in my RAM, i was breaking it into six subsets and building features iteratively. This meant i was doing a pandas concat between the older dataframe with the new feature, little did i concentrate on indexing :( Spent more than a couple days scratching my head at all NaN features. Noob mistake.

The QID Conundrum:

The training dataset had an ID for every question, thus a QID1 and QID2 in every row. However, this wasn’t the case with the testing dataset which meant “QID” couldn’t be used as a feature straightaway. A fellow kaggler released an incredibly creative observation of the decreasing average duplicate ratio(rolling mean) with increasing QID — Mostprobably an indication of Quora’s improving algorithm with time, thus reducing the number of duplicate questions with increasing ID. This inference is based on the assumption that the QID values aren’t masked and are truly representative of time of posting the question. To model QIDs in our testing dataset, i had a hashtable which mapped question texts to QIDs. Now iterating over all questions in test_df — If i encountered an existent question, the corresponding QID was assigned to it. Else we assumed that this was a new question in order of time of posting, incrementing QID by 1. This led to various features like QID difference, mean QID, min QID with the hope of modelling the decrease of duplicacy over time.

Class Imbalance:

A major part of discussions were centered around guesstimating the class split in the testing dataframe, which wasn’t clearly similar to the testing split. The mathy folks figured a narrow range of the split via a couple of constant valued submissions, here and here . There were around 34% positive duplicates in the training set, while the testing set had an estimated 16%-17% positive duplicates — Probably a result of the improved Quora algorithm or a result of the computer-generated question pairs. Anyway, a misrepresentative training dataset wasn’t going to help matters — people came up with oversampling solutions(duplicating the negative rows in training), or rescaled their predictions by an appropriate factor.

My models — XGBoost is love, XGBoost is life

Very embarassed to admit, but my submission was just a single model solution, a 2000-round xgboost. To be fair, i didn’t spend a lot of time on parameter tuning or building diverse models, left it for too late. I tried the default random forest and GBM, not to a better effect than XGB. Stacking and ensembling were in the roadmap too, but that just stayed there :(

Instead, i spent a very long weekend at learning Keras and building dense neural nets — ’twas my first time! Went through understanding various hyperparameters and ideal architectures, the theory behind activation functions, dropout layers and optimizers! Very interesting stuff! I played around with my a two-layer sigmoid neural net with my 70 features tuning it to perfection, it never came close to the xgboost though. As i mentioned earlier, i couldn’t build embedding layers or LSTMs with my hardware, that would’ve definitely helped. Soon.

Proud of my modelling pipeline though, didn’t have to fret over making a function for every model– will be helpful when i build hundreds of models to stack. Soon.

Top Solutions:

Post-competition writeups of the gold medalists just makes me feel like a complete noob! I have to really up my game, and work harder to get up there, the bronze isn’t far away! Some of the major learnings from winners’ solutions:

Conclusions :

I should manage my time better, assigning appropriate time for exploration, feature engineering, model building and stacking. Really felt the crunch in the last week as i ran short of submissions.

So yeah, that’s that. One competition ends, another begins. Kaggle is addictive!

This was originally posted on my blog . One Blog Every Week

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