Sharing 20+ Diverse Datasets📊 for Data Science and Analytics practice!
1. How much did it rain :- https://www.kaggle.com/c/how-much-did-it-rain-ii/overview
2. Inventory Demand:- https://www.kaggle.com/c/grupo-bimbo-inventory-demand
3. Property Inspection predictiion:- https://www.kaggle.com/c/liberty-mutual-group-property-inspection-prediction
4. Restaurant Revenue prediction:- https://www.kaggle.com/c/restaurant-revenue-prediction/data
5. Customer satisfcation:-https://www.kaggle.com/c/santander-customer-satisfaction
6. Iris Dataset: https://archive.ics.uci.edu/ml/datasets/iris
7. Titanic Dataset: https://www.kaggle.com/c/titanic
8. Wine Quality Dataset: https://archive.ics.uci.edu/ml/datasets/Wine+Quality
9. Heart Disease Dataset: https://archive.ics.uci.edu/ml/datasets/Heart+Disease
10. Bengaluru House Price Dataset: https://www.kaggle.com/amitabhajoy/bengaluru-house-price-data
11. Breast Cancer Dataset: https://archive.ics.uci.edu/ml/datasets/Breast+Cancer+Wisconsin+%28Diagnostic%29
12. Credit Card Fraud Detection: https://www.kaggle.com/mlg-ulb/creditcardfraud
13. Netflix Movies and TV Shows: https://www.kaggle.com/shivamb/netflix-shows
14. Trending YouTube Video Statistics: https://www.kaggle.com/datasnaek/youtube-new
15. Walmart Store Sales Forecasting: https://www.kaggle.com/c/walmart-recruiting-store-sales-forecasting
16. FIFA 19 Complete Player Dataset: https://www.kaggle.com/karangadiya/fifa19
17. World Happiness Report: https://www.kaggle.com/unsdsn/world-happiness
18. TMDB 5000 Movie Dataset: https://www.kaggle.com/tmdb/tmdb-movie-metadata
19. Students Performance in Exams: https://www.kaggle.com/spscientist/students-performance-in-exams
20. Twitter Sentiment Analysis Dataset: https://www.kaggle.com/kazanova/sentiment140
21. Digit Recognizer: https://www.kaggle.com/c/digit-recognizer
💻🔍 Don't miss out on these valuable resources for advancing your data science journey!
1. How much did it rain :- https://www.kaggle.com/c/how-much-did-it-rain-ii/overview
2. Inventory Demand:- https://www.kaggle.com/c/grupo-bimbo-inventory-demand
3. Property Inspection predictiion:- https://www.kaggle.com/c/liberty-mutual-group-property-inspection-prediction
4. Restaurant Revenue prediction:- https://www.kaggle.com/c/restaurant-revenue-prediction/data
5. Customer satisfcation:-https://www.kaggle.com/c/santander-customer-satisfaction
6. Iris Dataset: https://archive.ics.uci.edu/ml/datasets/iris
7. Titanic Dataset: https://www.kaggle.com/c/titanic
8. Wine Quality Dataset: https://archive.ics.uci.edu/ml/datasets/Wine+Quality
9. Heart Disease Dataset: https://archive.ics.uci.edu/ml/datasets/Heart+Disease
10. Bengaluru House Price Dataset: https://www.kaggle.com/amitabhajoy/bengaluru-house-price-data
11. Breast Cancer Dataset: https://archive.ics.uci.edu/ml/datasets/Breast+Cancer+Wisconsin+%28Diagnostic%29
12. Credit Card Fraud Detection: https://www.kaggle.com/mlg-ulb/creditcardfraud
13. Netflix Movies and TV Shows: https://www.kaggle.com/shivamb/netflix-shows
14. Trending YouTube Video Statistics: https://www.kaggle.com/datasnaek/youtube-new
15. Walmart Store Sales Forecasting: https://www.kaggle.com/c/walmart-recruiting-store-sales-forecasting
16. FIFA 19 Complete Player Dataset: https://www.kaggle.com/karangadiya/fifa19
17. World Happiness Report: https://www.kaggle.com/unsdsn/world-happiness
18. TMDB 5000 Movie Dataset: https://www.kaggle.com/tmdb/tmdb-movie-metadata
19. Students Performance in Exams: https://www.kaggle.com/spscientist/students-performance-in-exams
20. Twitter Sentiment Analysis Dataset: https://www.kaggle.com/kazanova/sentiment140
21. Digit Recognizer: https://www.kaggle.com/c/digit-recognizer
💻🔍 Don't miss out on these valuable resources for advancing your data science journey!
👍16
1. What is the primary difference between R square and adjusted R square?
In linear regression, you use both these values for model validation. However, there is a clear distinction between the two. R square accounts for the variation of all independent variables on the dependent variable. In other words, it considers each independent variable for explaining the variation. In the case of Adjusted R square, it accounts for the significant variables alone for indicating the percentage of variation in the model. By significant, we refer to the P values less than 0.05.
2. What is the curse of dimensionality?
Curse of Dimensionality refers to a set of problems that arise when working with high-dimensional data. The dimension of a dataset corresponds to the number of attributes/features that exist in a dataset. A dataset with a large number of attributes, generally of the order of a hundred or more, is referred to as high dimensional data. Some of the difficulties that come with high dimensional data manifest during analyzing or visualizing the data to identify patterns, and some manifest while training machine learning models. The difficulties related to training machine learning models due to high dimensional data are referred to as the ‘Curse of Dimensionality’.
3. What are some Stopping Criteria for k-Means Clustering?
a. Convergence. No further changes, points stay in the same cluster.
b. The maximum number of iterations. When the maximum number of iterations has been reached, the algorithm will be stopped. This is done to limit the runtime of the algorithm.
c. Variance did not improve by at least x * initial variance
4. What are hard margin and soft Margin SVMs?
Hard margin SVMs work only if the data is linearly separable and these types of SVMs are quite sensitive to the outliers. But our main objective is to find a good balance between keeping the margins as large as possible and limiting the margin violation i.e. instances that end up in the middle of margin or even on the wrong side, and this method is called soft margin SVM.
In linear regression, you use both these values for model validation. However, there is a clear distinction between the two. R square accounts for the variation of all independent variables on the dependent variable. In other words, it considers each independent variable for explaining the variation. In the case of Adjusted R square, it accounts for the significant variables alone for indicating the percentage of variation in the model. By significant, we refer to the P values less than 0.05.
2. What is the curse of dimensionality?
Curse of Dimensionality refers to a set of problems that arise when working with high-dimensional data. The dimension of a dataset corresponds to the number of attributes/features that exist in a dataset. A dataset with a large number of attributes, generally of the order of a hundred or more, is referred to as high dimensional data. Some of the difficulties that come with high dimensional data manifest during analyzing or visualizing the data to identify patterns, and some manifest while training machine learning models. The difficulties related to training machine learning models due to high dimensional data are referred to as the ‘Curse of Dimensionality’.
3. What are some Stopping Criteria for k-Means Clustering?
a. Convergence. No further changes, points stay in the same cluster.
b. The maximum number of iterations. When the maximum number of iterations has been reached, the algorithm will be stopped. This is done to limit the runtime of the algorithm.
c. Variance did not improve by at least x * initial variance
4. What are hard margin and soft Margin SVMs?
Hard margin SVMs work only if the data is linearly separable and these types of SVMs are quite sensitive to the outliers. But our main objective is to find a good balance between keeping the margins as large as possible and limiting the margin violation i.e. instances that end up in the middle of margin or even on the wrong side, and this method is called soft margin SVM.
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1. What is the Difference Between a Shallow Copy and Deep Copy in python?
Deepcopy creates a different object and populates it with the child objects of the original object. Therefore, changes in the original object are not reflected in the copy. copy.deepcopy() creates a Deep Copy. Shallow copy creates a different object and populates it with the references of the child objects within the original object. Therefore, changes in the original object are reflected in the copy. copy.copy creates a Shallow Copy.
2. How can you remove duplicate values in a range of cells?
1. To delete duplicate values in a column, select the highlighted cells, and press the delete button. After deleting the values, go to the ‘Conditional Formatting’ option present in the Home tab. Choose ‘Clear Rules’ to remove the rules from the sheet.
2. You can also delete duplicate values by selecting the ‘Remove Duplicates’ option under Data Tools present in the Data tab.
3. Define shelves and sets in Tableau?
Shelves: Every worksheet in Tableau will have shelves such as columns, rows, marks, filters, pages, and more. By placing filters on shelves we can build our own visualization structure. We can control the marks by including or excluding data.
Sets: The sets are used to compute a condition on which the dataset will be prepared. Data will be grouped together based on a condition. Fields which is responsible for grouping are known assets. For example – students having grades of more than 70%.
4. Given a table Employee having columns empName and empId, what will be the result of the SQL query below?
select empName from Employee order by 2 asc;
“Order by 2” is valid when there are at least 2 columns used in SELECT statement. Here this query will throw error because only one column is used in the SELECT statement.
ENJOY LEARNING 👍👍
Deepcopy creates a different object and populates it with the child objects of the original object. Therefore, changes in the original object are not reflected in the copy. copy.deepcopy() creates a Deep Copy. Shallow copy creates a different object and populates it with the references of the child objects within the original object. Therefore, changes in the original object are reflected in the copy. copy.copy creates a Shallow Copy.
2. How can you remove duplicate values in a range of cells?
1. To delete duplicate values in a column, select the highlighted cells, and press the delete button. After deleting the values, go to the ‘Conditional Formatting’ option present in the Home tab. Choose ‘Clear Rules’ to remove the rules from the sheet.
2. You can also delete duplicate values by selecting the ‘Remove Duplicates’ option under Data Tools present in the Data tab.
3. Define shelves and sets in Tableau?
Shelves: Every worksheet in Tableau will have shelves such as columns, rows, marks, filters, pages, and more. By placing filters on shelves we can build our own visualization structure. We can control the marks by including or excluding data.
Sets: The sets are used to compute a condition on which the dataset will be prepared. Data will be grouped together based on a condition. Fields which is responsible for grouping are known assets. For example – students having grades of more than 70%.
4. Given a table Employee having columns empName and empId, what will be the result of the SQL query below?
select empName from Employee order by 2 asc;
“Order by 2” is valid when there are at least 2 columns used in SELECT statement. Here this query will throw error because only one column is used in the SELECT statement.
ENJOY LEARNING 👍👍
👍13
Amazing Hackthon Solved Data Science/ML Project Collection
⭐️ 167
https://github.com/analyticsindiamagazine/MachineHack/tree/master/Hackathon_Solutions
𝗘𝗡𝗝𝗢𝗬 𝗟𝗘𝗔𝗥𝗡𝗜𝗡𝗚 👍👍
⭐️ 167
https://github.com/analyticsindiamagazine/MachineHack/tree/master/Hackathon_Solutions
𝗘𝗡𝗝𝗢𝗬 𝗟𝗘𝗔𝗥𝗡𝗜𝗡𝗚 👍👍
❤7👍7
FREE DATASET BUILDING YOUR PORTFOLIO ⭐
1. Supermarket Sales - https://lnkd.in/e86UpCMv
2.Credit Card Fraud Detection - https://lnkd.in/eFTsZDCW
3. FIFA 22 complete player dataset - https://lnkd.in/eDScdUUM
4. Walmart Store Sales Forecasting - https://lnkd.in/eVT6h-CT
5. Netflix Movies and TV Shows - https://lnkd.in/eZ3cduwK
6.LinkedIn Data Analyst jobs listings - https://lnkd.in/ezqxcmrE
7. Top 50 Fast-Food Chains in USA - https://lnkd.in/esBjf5u4
8. Amazon and Best Buy Electronics - https://lnkd.in/e4fBZvJ3
9. Forecasting Book Sales - https://lnkd.in/eXHN2XsQ
10. Real / Fake Job Posting Prediction - https://lnkd.in/e5SDDW9G
1. Supermarket Sales - https://lnkd.in/e86UpCMv
2.Credit Card Fraud Detection - https://lnkd.in/eFTsZDCW
3. FIFA 22 complete player dataset - https://lnkd.in/eDScdUUM
4. Walmart Store Sales Forecasting - https://lnkd.in/eVT6h-CT
5. Netflix Movies and TV Shows - https://lnkd.in/eZ3cduwK
6.LinkedIn Data Analyst jobs listings - https://lnkd.in/ezqxcmrE
7. Top 50 Fast-Food Chains in USA - https://lnkd.in/esBjf5u4
8. Amazon and Best Buy Electronics - https://lnkd.in/e4fBZvJ3
9. Forecasting Book Sales - https://lnkd.in/eXHN2XsQ
10. Real / Fake Job Posting Prediction - https://lnkd.in/e5SDDW9G
👍13😁1
Harvard University offers a ton of FREE online courses.
From Computer Science to Artificial Intelligence.
Here are 10 FREE courses you don't want to miss
1. Introduction to Computer Science
An introduction to the intellectual enterprises of computer science and the art of programming.
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2. Web Programming with Python and JavaScript
This course takes you deeply into the design and implementation of web apps with Python, JavaScript, and SQL using frameworks like Django, React, and Bootstrap.
Check here 👇
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3. Introduction to Programming with Scratch
A gentle introduction to programming that prepares you for subsequent courses in coding.
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An introduction to programming using Python, a popular language for general-purpose programming, data science, web programming, and more.
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This is CS50’s introduction to technology for students who don’t (yet!) consider themselves computer persons.
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Learn to use machine learning in Python in this introductory course on artificial intelligence.
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10. Introduction to Data Science with Python
Join Harvard University instructor Pavlos Protopapas in this online course to learn how to use Python to harness and analyze data.
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From Computer Science to Artificial Intelligence.
Here are 10 FREE courses you don't want to miss
1. Introduction to Computer Science
An introduction to the intellectual enterprises of computer science and the art of programming.
Check here 👇
https://pll.harvard.edu/course/cs50-introduction-computer-science?delta=0
2. Web Programming with Python and JavaScript
This course takes you deeply into the design and implementation of web apps with Python, JavaScript, and SQL using frameworks like Django, React, and Bootstrap.
Check here 👇
https://pll.harvard.edu/course/cs50s-web-programming-python-and-javanoscript?delta=0
3. Introduction to Programming with Scratch
A gentle introduction to programming that prepares you for subsequent courses in coding.
Check here 👇
https://pll.harvard.edu/course/cs50s-introduction-programming-scratch?delta=0
4. Introduction to Programming with Python
An introduction to programming using Python, a popular language for general-purpose programming, data science, web programming, and more.
Check here 👇
https://edx.org/course/cs50s-introduction-to-programming-with-python
5. Understanding Technology
This is CS50’s introduction to technology for students who don’t (yet!) consider themselves computer persons.
Check here 👇
https://pll.harvard.edu/course/cs50s-understanding-technology-0?delta=0
6. Introduction to Artificial Intelligence with Python
Learn to use machine learning in Python in this introductory course on artificial intelligence.
Check here 👇
https://pll.harvard.edu/course/cs50s-introduction-artificial-intelligence-python?delta=0
7. Introduction to Game Development
Learn about the development of 2D and 3D interactive games in this hands-on course, as you explore the design of games such as Super Mario Bros., Pokémon, Angry Birds, and more.
Check here 👇
https://pll.harvard.edu/course/cs50s-introduction-game-development?delta=0
8. CS50's Computer Science for Business Professionals
This is CS50’s introduction to computer science for business professionals.
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9. Mobile App Development with React Native
Learn about mobile app development with React Native, a popular framework maintained by Facebook that enables cross-platform native apps using JavaScript without Java or Swift.
Check here 👇
https://pll.harvard.edu/course/cs50s-mobile-app-development-react-native?delta=0
10. Introduction to Data Science with Python
Join Harvard University instructor Pavlos Protopapas in this online course to learn how to use Python to harness and analyze data.
Check here 👇
https://pll.harvard.edu/course/introduction-data-science-python?delta=0
Harvard University
CS50: Introduction to Computer Science | Harvard University
An introduction to the intellectual enterprises of computer science and the art of programming.
👍10❤6👏2
1. What is the Impact of Outliers on Logistic Regression?
The estimates of the Logistic Regression are sensitive to unusual observations such as outliers, high leverage, and influential observations. Therefore, to solve the problem of outliers, a sigmoid function is used in Logistic Regression.
2. What is the difference between vanilla RNNs and LSTMs?
The main difference between vanilla RNNs and LSTMs is that LSTMs are able to better remember long-term dependencies, while vanilla RNNs tend to forget them. This is due to the fact that LSTMs have a special type of memory cell that can retain information for longer periods of time, while vanilla RNNs only have a single layer of memory cells.
3. What is Masked Language Model in NLP?
Masked language models help learners to understand deep representations in downstream tasks by taking an output from the corrupt input. This model is often used to predict the words to be used in a sentence.
4. Why is the KNN Algorithm known as Lazy Learner?
When the KNN algorithm gets the training data, it does not learn and make a model, it just stores the data. Instead of finding any discriminative function with the help of the training data, it follows instance-based learning and also uses the training data when it actually needs to do some prediction on the unseen datasets. As a result, KNN does not immediately learn a model rather delays the learning thereby being referred to as Lazy Learner.
The estimates of the Logistic Regression are sensitive to unusual observations such as outliers, high leverage, and influential observations. Therefore, to solve the problem of outliers, a sigmoid function is used in Logistic Regression.
2. What is the difference between vanilla RNNs and LSTMs?
The main difference between vanilla RNNs and LSTMs is that LSTMs are able to better remember long-term dependencies, while vanilla RNNs tend to forget them. This is due to the fact that LSTMs have a special type of memory cell that can retain information for longer periods of time, while vanilla RNNs only have a single layer of memory cells.
3. What is Masked Language Model in NLP?
Masked language models help learners to understand deep representations in downstream tasks by taking an output from the corrupt input. This model is often used to predict the words to be used in a sentence.
4. Why is the KNN Algorithm known as Lazy Learner?
When the KNN algorithm gets the training data, it does not learn and make a model, it just stores the data. Instead of finding any discriminative function with the help of the training data, it follows instance-based learning and also uses the training data when it actually needs to do some prediction on the unseen datasets. As a result, KNN does not immediately learn a model rather delays the learning thereby being referred to as Lazy Learner.
👍16
Industry Data Science vs Academia Data Science
Comparing Data Science in academia and Data Science in industry is like comparing tennis with table tennis: they sound similar but in the end, they are completely different!
5 big differences between Data Science in academia and in industry 👇:
1️⃣ Model vs Data: Academia focuses on models, industry focuses on data. In academia, it’s all about trying to find the best model architecture to optimise a defined metric. In industry, loading and processing the data accounts for around 80% of the job.
2️⃣ Novelty vs Efficiency: The end goal of academia is often to publish a paper and to do so, you will need to find and implement a novel approach. Industry is all about efficiency: reusing existing models as much as possible and applying them to your use case.
3️⃣ Complex vs Simple: More often than not, academia requires complex solutions. I know that this isn’t always the case but unfortunately, complex papers get a higher chance of being accepted at top conferences. In industry, it’s all about simplicity: trying to find the simplest solution that solves a specific problem.
4️⃣ Theory vs Engineering: To succeed in academia, you need to have strong theoretical and maths skills. To succeed in industry, you need to develop strong engineering skills. It is great to be able to train a model in a notebook but if you cannot deploy your model in production, it will be completely useless.
5️⃣ Knowledge impact vs $ impact: In academia, it’s all about creating new work and expanding human knowledge. In industry, it is all about using data to drive value and increase revenue.
Comparing Data Science in academia and Data Science in industry is like comparing tennis with table tennis: they sound similar but in the end, they are completely different!
5 big differences between Data Science in academia and in industry 👇:
1️⃣ Model vs Data: Academia focuses on models, industry focuses on data. In academia, it’s all about trying to find the best model architecture to optimise a defined metric. In industry, loading and processing the data accounts for around 80% of the job.
2️⃣ Novelty vs Efficiency: The end goal of academia is often to publish a paper and to do so, you will need to find and implement a novel approach. Industry is all about efficiency: reusing existing models as much as possible and applying them to your use case.
3️⃣ Complex vs Simple: More often than not, academia requires complex solutions. I know that this isn’t always the case but unfortunately, complex papers get a higher chance of being accepted at top conferences. In industry, it’s all about simplicity: trying to find the simplest solution that solves a specific problem.
4️⃣ Theory vs Engineering: To succeed in academia, you need to have strong theoretical and maths skills. To succeed in industry, you need to develop strong engineering skills. It is great to be able to train a model in a notebook but if you cannot deploy your model in production, it will be completely useless.
5️⃣ Knowledge impact vs $ impact: In academia, it’s all about creating new work and expanding human knowledge. In industry, it is all about using data to drive value and increase revenue.
👍18👏4❤2
Here are some incredible platforms where you can download datasets for your project:
Our World in Data https://ourworldindata.org/
World Health Organization (https://www.who.int/data/gho
Statcounter (https://gs.statcounter.com/
Food and Agriculture Organization of the UN (FAO) (https://www.fao.org/home/en
World Bank (https://data.worldbank.org/)
Our World in Data https://ourworldindata.org/
World Health Organization (https://www.who.int/data/gho
Statcounter (https://gs.statcounter.com/
Food and Agriculture Organization of the UN (FAO) (https://www.fao.org/home/en
World Bank (https://data.worldbank.org/)
❤7👍1
8 AI Tools Just for Fun:
1. Tattoo Artist
https://tattoosai.com
2. Talk to Books
https://books.google.com/talktobooks/
3. Vintage Headshots
https://myheritage.com/ai-time-machine
4. Hello to Past
https://hellohistory.ai
5. Fake yourself
https://fakeyou.com
6. Unreal Meal
https://unrealmeal.ai
7. Reface AI
https://hey.reface.ai
8. Voice Changer
https://voicemod.net
1. Tattoo Artist
https://tattoosai.com
2. Talk to Books
https://books.google.com/talktobooks/
3. Vintage Headshots
https://myheritage.com/ai-time-machine
4. Hello to Past
https://hellohistory.ai
5. Fake yourself
https://fakeyou.com
6. Unreal Meal
https://unrealmeal.ai
7. Reface AI
https://hey.reface.ai
8. Voice Changer
https://voicemod.net
Tattoosai
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1. Can you explain how the memory cell in an LSTM is implemented computationally?
The memory cell in an LSTM is implemented as a forget gate, an input gate, and an output gate. The forget gate controls how much information from the previous cell state is forgotten. The input gate controls how much new information from the current input is allowed into the cell state. The output gate controls how much information from the cell state is allowed to pass out to the next cell state.
2. What is CTE in SQL?
A CTE (Common Table Expression) is a one-time result set that only exists for the duration of the query. It allows us to refer to data within a single SELECT, INSERT, UPDATE, DELETE, CREATE VIEW, or MERGE statement's execution scope. It is temporary because its result cannot be stored anywhere and will be lost as soon as a query's execution is completed.
3. List the advantages NumPy Arrays have over Python lists?
Python’s lists, even though hugely efficient containers capable of a number of functions, have several limitations when compared to NumPy arrays. It is not possible to perform vectorised operations which includes element-wise addition and multiplication. They also require that Python store the type information of every element since they support objects of different types. This means a type dispatching code must be executed each time an operation on an element is done.
4. What’s the F1 score? How would you use it?
The F1 score is a measure of a model’s performance. It is a weighted average of the precision and recall of a model, with results tending to 1 being the best, and those tending to 0 being the worst.
5. Name an example where ensemble techniques might be useful?
Ensemble techniques use a combination of learning algorithms to optimize better predictive performance. They typically reduce overfitting in models and make the model more robust (unlikely to be influenced by small changes in the training data). You could list some examples of ensemble methods (bagging, boosting, the “bucket of models” method) and demonstrate how they could increase predictive power.
The memory cell in an LSTM is implemented as a forget gate, an input gate, and an output gate. The forget gate controls how much information from the previous cell state is forgotten. The input gate controls how much new information from the current input is allowed into the cell state. The output gate controls how much information from the cell state is allowed to pass out to the next cell state.
2. What is CTE in SQL?
A CTE (Common Table Expression) is a one-time result set that only exists for the duration of the query. It allows us to refer to data within a single SELECT, INSERT, UPDATE, DELETE, CREATE VIEW, or MERGE statement's execution scope. It is temporary because its result cannot be stored anywhere and will be lost as soon as a query's execution is completed.
3. List the advantages NumPy Arrays have over Python lists?
Python’s lists, even though hugely efficient containers capable of a number of functions, have several limitations when compared to NumPy arrays. It is not possible to perform vectorised operations which includes element-wise addition and multiplication. They also require that Python store the type information of every element since they support objects of different types. This means a type dispatching code must be executed each time an operation on an element is done.
4. What’s the F1 score? How would you use it?
The F1 score is a measure of a model’s performance. It is a weighted average of the precision and recall of a model, with results tending to 1 being the best, and those tending to 0 being the worst.
5. Name an example where ensemble techniques might be useful?
Ensemble techniques use a combination of learning algorithms to optimize better predictive performance. They typically reduce overfitting in models and make the model more robust (unlikely to be influenced by small changes in the training data). You could list some examples of ensemble methods (bagging, boosting, the “bucket of models” method) and demonstrate how they could increase predictive power.
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