done

Q1. what every column mean here in kashti data set? ANSWER: 1. Survived: Indicates whether the passenger survived (0 = No, 1 = Yes). 2. Pclass: Represents the passenger class (1st, 2nd, or 3rd). 3. Sex: Gender of the passenger. 4. Age: Age of the passenger. 5. SibSp: Number of siblings or spouses aboard the Titanic for each passenger. 6. Parch: Number of parents or children aboard the Titanic for each passenger. 7. Fare: The amount paid for the ticket by each passenger. 8. Embarked: Denotes the port of embarkation (C = Cherbourg, Q = Queenstown, S = Southampton). 9. Class: Similar to 'Pclass', it denotes the passenger class. 10. Who: Indicates the category of the person (child, woman, man). 11. Adult_male: Binary value indicating whether the person is an adult male (True/False). 12. Deck: Represents the deck where the passenger's cabin was located. 13. Embark_town: Another representation of the port of embarkation (Cherbourg, Queenstown, Southampton). 14. Alive: Indicates whether the passenger survived (Yes/No). 15. Alone: Binary value indicating whether the passenger was traveling alone (True/False).Q2. how many datasets are there in sns.load_dataset()? and how to fetch them? ANSWER:1. 'anscombe': Anscombe's quartet dataset. 2. 'attention': Reaction times in a psychological experiment dataset. 3. 'brain_networks': Network measures for human brain functional networks. 4. 'car_crashes': US car crash dataset. 5. 'diamonds': Diamond characteristics dataset. 6. 'dots': Lateral geniculate nucleus response to light stimuli dataset. 7. 'exercise': Results from fitness tracking dataset. 8. 'flights': Passengers on airline flights dataset. 9. 'fmri': Functional Magnetic Resonance Imaging (fMRI) dataset. 10. 'gammas': Simulated neuroimaging data. 11. 'iris': Iris flower dataset. 12. 'mpg': Miles per gallon of various car models dataset. 13. 'planets': Exoplanets discovery dataset. 14. 'tips': Restaurant tips dataset. 15. 'titanic': Titanic dataset.

day 9 assignmentName: Muhammad Daniyalwhat is nan? how much its important? and why its availiblity and not availibility is important?What is NaN?NaN stands for "Not a Number" and is a special value used in programming to represent an undefined or unrepresentable numerical value. It is commonly used in floating-point arithmetic, which is the standard way of representing numbers in computers.Why is NaN important?NaN is important because it allows programmers to handle invalid numerical operations in a controlled and predictable way. Without NaN, invalid operations would often cause programs to crash or produce unexpected results. For example, if a program tries to divide 0 by 0, the result would be NaN. This is important because there is no real number that is equal to 0 divided by 0.NaN is also important for representing values that are not numbers, such as infinity. For example, the square root of a negative number is not a real number, so it is represented by NaN.Why is the availability and non-availability of NaN important?The availability of NaN is important because it allows programmers to write more robust and reliable code. By using NaN to represent invalid or unrepresentable values, programmers can avoid crashes and unexpected results.The non-availability of NaN can also be important in some cases. For example, if a program is performing a numerical calculation that is expected to always produce a valid result, then the presence of NaN can indicate that there is a problem with the calculation.In general, NaN is a valuable tool for programmers, and its availability and non-availability can both be important depending on the specific application.

this is very good to learn data science, being a jobian and potential student of PhD, I am learning a lot from you Sir, May Allah bless you, Jazak Allah

save to excel : phool.to_excel('phool.xlsx') kashti.to_excel('kashti.xlsx')

Assignment-2: VARIABLE DESCRIPTIONS IN DATASET survived :Survival (0 = No; 1 = Yes) pclass :Passenger Class (1 = 1st; 2 = 2nd; 3 = 3rd) sex: Sex age: Passenger age in years sibsp : Number of Siblings/Spouses Aboard parch : Number of Parents/Children Aboard fare : Passenger Fare embarked : Port of Embarkation (C = Cherbourg; Q = Queenstown; S = Southampton) class: Class name (First, Second, Third) who : man, women, child adult_male: True, False deck: Deck number embarke_town : Name of Embarkation (Cherbourg, Queenstown;, Southampton alive: yes / no alone: true/falesWaiting for your reply abou the poast

Assignment-1: 17 built-in datasets in seaborn, where 9 Seaborn Datasets for Data Science + ML Beginners: 1. Anscombe 2. Attention 3. Car Crashes 4. Diamonds 5. Flights 6. Iris 7. MPG 8. Planets 9. Titanic

for accurate and meaningful data analysis , proper handling of NaN values is essential

missing data

we can fetch dataset to use this command: sns.load_dataset('datasetname')