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# Important Python Machine Learning and Data Science Practice Sets | Realcode4you

Practice Set 1

Practice Set 2

1. The following is the frequency distribution of the number of telephone calls received in 245 successive one minute intervals at an exchange:

A. Write a Python program without using the built-in functions for the following :

1. Mean

2. Median

3. Mode

4. Range

5. Standard deviation

6. Variance

B. Using Matplotlib, describe the above visually using a suitable graph/plot

2. The following data gives the distribution of the marks of 100 students.

A. Write a Python program to calculate the range and quartiles of the above data

B. Represent the above data with the help of a suitable plot using Matplotlib

3. Write a Python program to calculate correlation from the following data.

4. Write a python program to simulate 100000 occurrences of a random variable Y which is equal to sum of two different random variables X and Z, where X follows a normal distribution with mean = 50 and variance = 20 and Z follows a Poisson distribution with mean = 40. Draw a histogram of the simulation.

5. The table below lists the change in daily stock prices for 4 companies â€“ Microsoft, GE, Intel and GM. Write a python program to make a side by side box plot for visualization of this data.

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Practice Set 3(Using Spark)

You will be asked some basic questions about some stock market data, in this case Walmart Stock from the years 2012-2017.

Dataset: walmart_stock.csv

Questions:

• Start a simple Spark Session

• Load the Walmart Stock CSV File, have Spark infer the data types.

• What are the column names?

• What does the Schema look like?

• Print out the first 5 columns.

• Use describe() to learn about the DataFrame. Provide your inference on the same

• From the above question, There may many decimal places for mean and stddev in the describe() dataframe. Format the numbers to just show up to two decimal places. Pay careful attention to the datatypes that .describe() returns.

Hint: (http://spark.apache.org/docs/latest/api/python/pyspark.sql.html#pyspark.sql.Column.cast)

• Create a new dataframe with a column called HV Ratio that is the ratio of the High Price versus volume of stock traded for a day.

• What day had the Peak High in Price?

• What is the mean of the Close column?

• What is the max and min of the Volume column?

• How many days was the Close lower than 60 dollars?

• What percentage of the time was the High greater than 80 dollars ? Hint: Number of Days High>80)/(Total Days in the dataset)

• What is the Pearson correlation between High and Volume?

Hint:

• What is the max High per year?

• What is the average Close for each Calendar Month?

Hint: In other words, across all the years, what is the average Close price for Jan,Feb, Mar, etc... Your result will have a value for each of these months