Data Science & Machine Learning

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.

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!

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