📝 Practice Assignments

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 ### Please name your assignment file using the following format: Your Name_Assignment_Assignment Number. EX: INTTRVU_Assignment_1

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### Q1) Write a python Class Circle which has the following variables and functions.

#Variables:
#pi_value = 3.14 (private variable)
#radius 

#functions:
#area_calculator
#circumference_calculator


#Create an object of Class circle and calculate its area and circumference.

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#Q2) Write a function which returns the addition of all numbers in numpy array.
# Numpy array should contain 50 random numbers in the range of 10 to 100 generated using random number generator.

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# Q3) i) Create a 3 * 2 ( 3 rows , 2 columns ) matrix using numpy and transpose it.
#ii) After transposing print the first row of matrix ( index 0)
#iii) Print second element in first row
#iv) Flatten the matrix to 1D ( 1 Dimensional ) array
#v) Print maximum, minimum and average value of 1D array

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#Q4) 	Read the Housing.csv file in your notebook and perform the following operations.
#i)	Calculate average and median price per square foot area (assume area column’s unit is sq ft )
#ii)	Calculate median price per bedroom. 
#iii)	Calculate average price per sq ft for furnished, semi-furnished and unfurnished homes.
#iv)	Are homes with guestrooms AND main road location costlier than remaining houses? Analyze the data using pandas to come up with your answer.
#v)	 Convert the values in air conditioning columns to 0 and 1 using lambda function
#vi)	Create a new column with the name price_index. Assign it 1 if the price of house is higher than average price else assign it value 0
#vii)	Sort the data by area, bathrooms and bedrooms column in descending order

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# Question 1
# What is the difference between continuous and discrete random variable?

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# Question 2
# What is inter quartile range?

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# Question 3
# How we can impute missing values for numerical and categorical features.

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# Question 4
# What is the significance of p-value

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# Questions 5
# If your numerical data has outliers how you would progress with missing value imputation?

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# Question 6
# What is contingency table in chi-square test?

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# Question 7
# Company wants to understand whether older version of newer version of product is better for the market based on customer reviews
# How would you prove that older/newer version is better statistically?

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# Question 8
# What is the difference between sample and population?

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# Question 9
# How would you decide product placement in DMart store if you have purchase data of 10,000 transactions
# Which factors are important to increase the sales based on product placement?

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# Question 10
# Explain the difference between correlation and causation with example.

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#Question 11
# Use Hotel reservations data from the sessions and solve following
# Does 'market_segment_type' have any impact on booking cancellation?
# Does 'no_of_special_requests' have any impact on booking cancellation?

# clue: use hypothesis testing

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#Question 12
# Use Hotel reservations data from the sessions and solve following
# Is there any correlation between 'lead_time' and 'avg_price_per_room'?
# If 'arrival_date' is after 25th of any month is there higher or lower cancellation rate a compareto first 25 days of the month?

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#Question 13
# Use Hotel reservations data from the sessions and solve following
# Draw histogram with 20 bins for 'lead_time' and 'avg_price_per_room'?
# Use matplotlib and draw it
# then use seaborn and draw it

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# Question 14
# Write following equations/mathematical formulas in your notebook - 5 times each
# No need to submit answer of this question in your submission
# Just write DONE over here if you have completed this step

# variance
# standard deviation
# Euclidean distance
# cosine distance
# min max scalar
# Normal distribution

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# Question 1
# What are the assumptions of linear regression?

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# Question 2
# What is MAE and R Squared score? In which case you will prefer R Squared Score?

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# Question 3
# What is the impact of very high learning rate in gradient descent?

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# Question 4
# Which property of logistic function is useful for a classification model?

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# Question 5
# Is feature scaling recommended for linear regression and logistic regression? If yes, why?

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# Question 6
# What is odds ratio?

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# Questions 7
# What is a cost function? Write in simple words.

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# Question 8
# What is precision and recall?

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# Question 9
# What is decision boundary?

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# Question 10
# Can logistic regression handle non-linear decision boundary?

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# Question 11
# What is bias variance tradeoff?

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# Question 12
# What will likely happen if we increase the number of features. Overffiting or underfitting?

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# Question 13
# Which type of regularization is preferred for feature selection?

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# Question 14
# Build and evaluate a linear regression model for predicting insurance charges.
# Refer insurance.csv dataset which is kept in the same folder

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# Question 15
# Write following formulas in your notebook 5 times each
# You dont have submit the solution for this question

# Linear regression equation
# Linear regression cost function
# gradient descent formula
# Logistic regression equation
# Logistic regression cost function
# MAE, RMSE, R Squared Score
# Precision and Recall
# L1 regularization
# L2 regularization

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import pandas as pd

data = {
    'Student': ['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J'],
    'Math': [88, 72, 91, 65, 85, 77, 94, 81, 70, 83],
    'English': [75, 80, 82, 69, 86, 73, 89, 78, 74, 80],
    'Science': [90, 78, 85, 70, 88, 76, 92, 84, 77, 86],
    'Study Hours': [6, 5, 7, 3, 6, 4, 8, 5, 4, 6],
    'Attendance': [95, 82, 98, 76, 90, 80, 99, 88, 78, 92],
    'Extracurricular': ['Yes', 'No', 'Yes', 'Yes', 'No', 'No', 'Yes', 'No', 'Yes', 'Yes']
}

df = pd.DataFrame(data)
df

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Ex :
Input: 
name = "Alice"
age = 25

Output: Hello, Alice! You are 25 years old.

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Output : 
['Name', 'Age', 'Courses']
['John', 20, ['Math', 'Science']]

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# dict1 = {'a': 1, 'b': 2}
# dict2 = {'b': 3, 'c': 4}

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Output:{'a': 1, 'b': 3, 'c': 4}

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Output:{1: 1, 2: 4, 3: 9, 4: 16, 5: 25}

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# School
#     Class 1
#         Students: ['Alice', 'Bob']
#     Class 2
#         Students: ['Charlie', 'David']

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# tuple1 = (1, 2, 3)
# tuple2 = (4, 5, 6)

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# data = {
#     'Name': ['Alice', 'Bob', 'Charlie'],
#     'Age': [25, 30, 35],
#     'City': ['New York', 'Los Angeles', 'Chicago']
# }

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# data_with_nan = {
#     'Name': ['Alice', 'Bob', 'Charlie'],
#     'Age': [25, None, 35],
#     'City': ['New York', 'Los Angeles', 'Chicago']
# }

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# df1 = pd.DataFrame({'ID': [1, 2, 3], 'Name': ['Alice', 'Bob', 'Charlie']})
# df2 = pd.DataFrame({'ID': [1, 2, 4], 'Salary': [50000, 60000, 70000]})

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### City: 'New York', 'Los Angeles', 'Chicago', 'New York', 'Los Angeles'
### Month: 'Jan', 'Jan', 'Feb', 'Feb', 'Jan'
### Sales: 2000, 3000, 2500, 3500, 2800

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# data_dup = {
#     'Name': ['Alice', 'Bob', 'Alice', 'Charlie'],
#     'Age': [25, 30, 25, 35]
# }

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# array1 = np.array([1, 2, 3])
# array2 = np.array([4, 5, 6])

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# array1 = np.array([1, 2, 3])
# array2 = np.array([4, 5, 6])

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# array1 = np.array([1, 2, 3])
# array2 = np.array([4, 5, 6])

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# array = np.array([1, 2, 3, 4, 5])

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# array = np.array([1, 2, 3, 4, 5])

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# array = np.array([1, 2, 3, 4, 5])

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# matrix1 = np.array([[1, 2], [3, 4]])
# matrix2 = np.array([[5, 6], [7, 8]])

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Input : 5

Output : Positive

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Input : 4
Output : Even

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Input : 2023
Output : NO

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Input :  Race
Output : Not a Palindrome