Python and Analytics for Data Science and Machine Learning

This course will cover

This is the 5th Homework Assignment and will cover Data Visualization.

Please download the homework5.pdf assignment which has 9 questions in it.

You will be using the data "homework5_bigmart.xlsx" and  "product_data.csv" in order to complete this assignment.

Please find the homework questions and data in the resource section.

Overview

New York Trip Dataset released by the NYC Taxi and Limousine Commission, which includes pickup time, geo-coordinates, number of passengers, and several other variables.

This dataset stores the trip duration across different destinations. There are two vendors which provides the Taxi service and each ride has customer id associated with it.

The Goal of the project is to find the insights such as which vendor is providing the better service in terms of duration. You can also find out which vendor is popular among customers. Which is the most common or frequent destinations among customers.

Is there any insights about which is the busiest time of the day or is there any seasonality involved in customer trip behavior.  What is the average count of passengers takes the ride in given trip across vendors or even at timeframe.

You can do descriptive statistics to find out the mean. max, min, std deviation values across vendors. This might give you some insight about if there is any similarity between them.

Dataset Dictionary:-

• id - a unique identifier for each trip

• vendor_id - a code indicating the provider associated with the trip record

• pickup_datetime - date and time when the meter was engaged (pick up customer time)

• dropoff_datetime - date and time when the meter was disengaged (drop off customer time)

• passenger_count - the number of passengers in the vehicle (driver entered value)

• pickup_longitude - the longitude where the meter was engaged

• pickup_latitude - the latitude where the meter was engaged

• dropoff_longitude - the longitude where the meter was disengaged

• dropoff_latitude - the latitude where the meter was disengaged

• store_and_fwd_flag - This flag indicates whether the trip record was held in vehicle memory before sending to the vendor because the vehicle did not have a connection to the server - Y=store and forward; N=not a store and forward trip

• trip_duration - duration of the trip in seconds

Tools and Concepts

Please feel free to use Python End to End concepts ranging from Python fundamentals to Data Science Libraries and code should be written in Jupyter Notebook.

Strategies and Hints

• Data Preprocessing Steps - Clean up the data such as fixing missing values (if any), fixing any outliers or inconsistent values, create new column based on existing columns (if required) etc.

• Pandas and Visualization - Use these libraries to showcase your findings, aggregations and insights

• Recommendations and Actionable insights - Provide your summarization in the form of recommendations, findings and actionable steps.

• At the end, There is no right or wrong answer in this project. So, feel free to be creative and see what are the important insights you can bring which can give us business or some critical value to the end users.

Dataset

nyc_taxi_trip_duration.xlsx is attached in the resources.

You can connect with your Jupyter notebook through pandas libraries.

df = pd.read_excel('nyc_taxi_trip_duration.xlsx')

Overview

This is the iconic Titanic Dataset which stores the passenger details and their survival information.

This dataset stores customer details such as demographics, fare, port of embarkation etc.

The Goal of the project is to find the insights and details such as how many people were survived and died. Find out the age buckets, which age group survived or died the most.

Is there any insights about point of embarkation such as which port has seen the highest number of survivals etc.

Any insights we can derived about the siblings or ticket class or even fare.

Which ticket class or the point of embarkation were the luckiest people to survive or vice-versa.

Dataset Dictionary:-

Variable            Definition                                                                               Key

survival              Survival                                                                         0 = No, 1 = Yes

pclass                Ticket class                                                                   1 = 1st, 2 = 2nd, 3 = 3rd

sex                      Sex

Age                     Age in years

sibsp                  # of siblings / spouses aboard the Titanic

parch                  # of parents / children aboard the Titanic

ticket                  Ticket number

fare                      Passenger fare

cabin                   Cabin number

embarked           Port of Embarkation                                              C = Cherbourg, Q = Queenstown, S = Southampton

Tools and Concepts

Please feel free to use Python End to End concepts ranging from Python fundamentals to Data Science Libraries and code should be written in Jupyter Notebook.

Strategies and Hints

• Data Preprocessing Steps - Clean up the data such as fixing missing values (if any), fixing any outliers or inconsistent values, create new column based on existing columns (if required) etc.

• Pandas and Visualization - Use these libraries to showcase your findings, aggregations and insights

• Recommendations and Actionable insights - Provide your summarization in the form of recommendations, findings and actionable steps.

• At the end, There is no right or wrong answer in this project. So, feel free to be creative and see what are the important insights you can bring which can give us business or some critical value to the end users.

Dataset

titanic_dataset.csv is attached in the resources.

You can connect with your Jupyter notebook through pandas libraries.

df = pd.read_csv('titanic_dataset.csv')