Description
Goal
The goal of this project is to expose you with a real data science problem, looking at the end-to-end pipeline.
What to do
You are asked to modify a Python Jupyter notebook, called `bikes_pgh_data.ipynb` that will:
* [Task 1] access historical bike rental data for 2019 from HealthyRidePGH and summarize the rental data
* [Task 2] create graphs to show the popularity of the different rental stations, given filter conditions
* [Task 3] create graphs to show the rebalancing issue
* [Task 4] cluster the data to group similar stations together, using a variety of clustering functions and visualize the results of the clustering.
**Your program should not take more than 15 minutes to run**
We present the details of each task next. You are provided with a skeleton `bikes_pgh_data.ipynb` Jupyter notebook file which you should use.
Task 1 (20 points)
In this task you will need to access historical bike rental data for 2021 from HealthyRidePGH and summarize it.
We will use historical rental data from HealthyRidePGH, available at [https://healthyridepgh.com/data/](https://healthyridepgh.com/data/).
In particular, we will use data for the first three quarters of 2021:
* Q1: HealthyRideRentals2021-Q1.csv
* Q2: HealthyRideRentals2021-Q2.csv
* Q3: HealthyRideRentals2021-Q3.csv
Each row in the file shows one rental transaction, indicating the bicycle ID, the source bike station (from station) and the destination bike station (to station). Worth noting:
* if there is no station ID, then this was usually a “dockless” bike, e.g., `BIKE 70000`,
* if a bike was “magically” moved from one station to a different one, that means this happened as a result of rebalancing, where HealthyRidePGH staff relocated the bike using a truck to address demand imbalance.
For this task, you will need to read in all the CSV data and create appropriate data structures (**HINT: use dataframes**) to record the following information, for every date (_year, month, day_), for every station (_stationID_):
– **fromCNT** = total number of “from” bikes at that station for that day (i.e., number of transactions with that _stationID_ in the **from** column)
– **toCNT** = total number of “to” bikes at that station for that day (i.e., number of transactions with that _stationID_ in the **to** column)
– **rebalCNT*** = total number of “rebalanced” bikes. See the next section for an explanation on how to compute this.
* **Task 1.1** Print the first 20 rows of the data structure you used to store the above data (i.e, **daily breakdown**).
You should also aggregate the above data at the month level as well, i.e., for every month, station combination record the total fromCNT, total toCNT, and total rebalCNT.
* **Task 1.2** Print the first 20 rows of the data structure you used to store the above data (i.e., **monthly breakdown**).
NOTES:
For simplicity, consider **Starttime** as the timestamp of every record. You should ignore **Stoptime** completely. For example, if a trip starts at 11:50 PM on Monday and ends at 12:10 AM on Tuesday, Just assume that the trip happened on Monday.
The daily breakdown should be a table, where every row contains the counts for a combination of day and station id. For example, it can have the form **(day, station id, fromCNT, toCNT, rebalCNT)**. Similarly, the monthly breakdown should have rows of the form **(month, station id, fromCNT, toCNT, rebalCNT)**.
How to compute the rebalancing
For every bikeID we need to figure out cases where when the data is sorted by bikeID trip start time.
Consider the following example snipet from the Q2 data:
|Trip id | Starttime | Stoptime | Bikeid | Tripduration | From station id | From station name | To station id | To station name | Usertype |
| — | — | — | — | — | — | — | — | — | — |
|66669783 | 4/1/2021 16:31 | 4/1/2021 17:41 | 70000 |4178 | 1047 |S 22nd St & E Carson St |1046 |S 25th St & E Carson St |Customer|
|67181259 | 4/6/2021 18:56 | 4/6/2021 19:08 | 70000 | 719 | 49301 |Centre Ave & N Craig St |1024 |S Negley Ave & Baum Blvd |Subscriber |
|67425370 | 4/8/2021 16:45 | 4/8/2021 16:50 | 70000 | 305 | 1024 |S Negley Ave & Baum Blvd |1026 |Penn Ave & S Whitfield St |Customer |
The first line indicates a rental from stationID = 1047 to stationID = 1046.
The second line indicates a rental from stationID = 49301 to stationID = 1024. This implies rebalancing, as somehow this bike was taken off circulation and put back in circulation in a different station (49301) than what was dropped off by a customer/subscriber earlier (1046).
The third line indicates a rental from stationID = 1024 (where it was left off before) to stationID = 1026.
So in this case you will need to increase by one the rebalancing count for 4/6/19 for stationID = 49301.
Note that you should not consider dockless stations (e.g., BIKE 70000) as stations; only consider as stations those with a proper stationID value.
Finally, note that a simple version, where you just take **rebalCNT = abs(fromCNT – toCNT)** will work as an **approximation**, since it counts as rebalancing bikes that were left at the station and not used. If you have trouble or ran out of time, this is an appropriate shortcut for partial credit (-8 points).
Task 2 (30 points)
In this task you will need to create graphs to show the popularity of the different rental stations, given filter conditions.
For this task you assume two variables containing input from the user:
* **filter_month** which corresponds to the month of interest (1-9, should have a default value of 4, i.e., April), and
* **filter_stationID** which corresponds to the stationID of interest (should have a default value of 1046).
Given the above two variables, you should create the following graphs:
* **Task 2.1** Show a bar chart for the 20 most popular bikestations when considering the number of **fromCNT** per station (for filter_month). Y axis should be the fromCNT per station, X axis should be the stationID. The first stationID corresponds to the most popular station.
* **Task 2.2** For the filter_month and for the filter_stationID show a graph that shows the distribution of bike rentals throughout the month, for that station only. Y axis should be the fromCNT for that stationID for that day, X axis would be the different days in that month (i.e., 1 – 30 for April).
* **Task 2.3** For the filter_month (e.g., April) show a graph that shows the distribution of bike rentals throughout the day, for all stations. Y axis should be the fromCNT for all stations in the filter_month, X axis would be the different hours in a day (i.e., 0 – 23).
* **Task 2.4** Update your data structure to compute the total number of rentals each bike had for each day (regardless of station). In other words, figure out how many times a bike was listed in the input data, for each different date. For the filter_month, show a graph that shows the 20 most popular bikes. Y axis should be the number of times a bike was rented, X axis should be the bikeID. The first bikeID corresponds to the most popular station.
Task 3 (20 points)
In this task you will create graphs to show the rebalancing issue.
* **Task 3.1** Show a bar chart for the 20 most popular bikestations when considering the number of **rebalCNT** per station (for filter_month). Y axis should be the rebalCNT per station, X axis should be the stationID. The first stationID corresponds to the most demanding station in terms of rebalancing.
* **Task 3.2** For the filter_month and for the filter_stationID show a graph that shows the distribution of bike rebalancing throughout the month, for that station only. Y axis should be the rebalCNT for that stationID for that day, X axis would be the different days in that month (i.e., 1 – 30 for April).
Task 4 (30 points)
In this task you will cluster the data to group similar stations together, using a variety of clustering functions and visualize the results of the clustering.
For this task, you should create a data structure where for each stationID you record the following features:
* 3 variables for the total fromCNT for each station for each of the 3 months of the third quarter (i.e., 7, 8, 9)
* 3 variables for the total rebalCNT for each station for each of the 3 months of the third quarter (i.e., 7, 8, 9)
This creates a 6-dimensional space for the different stations.
* **Task 4.1** You should perform clustering on the above 6-dimensional space using K-means (with at least 3 different values for K) and DBSCAN (with at least three different value combinations for min_samples and eps) [https://scikit-learn.org/stable/modules/clustering.html clustering](https://scikit-learn.org/stable/modules/clustering.html clustering).
* **Task 4.2** You should generate one bar chart per algorithm option (i.e., 6 different charts) showing the distribution of the number of stations per cluster. Y axis should be the number of stations in that cluster, X axis would be the clusterID. The first clusterID corresponds to the biggest cluster. Make sure each graph is properly labeled with the algorithm name and the parameters used.
* **Task 4.3** You should provide a brief explanation about your optimal choice of K. You should utilize the Elbow method on a reasonable range of K values. You should also mention (a) what is the best value of K that you found and (b) what is the best algorithm out of the two that you tried. For the second question, it is possible the results will be inconclusive.
Important notes about grading
It is absolutely imperative that your Jupyter notebook program:
* runs without any syntax or other errors (using Python 3)
* strictly adheres to the format specifications for input and output, as explained above.
Failure in any of the above will result in **severe** point loss.
Allowed Python Libraries
You are allowed to use the following Python libraries (although a fraction of these will actually be needed):
“`
argparse
collections
csv
numpy
json
glob
math
matplotlib
os
pandas
re
requests
sklearn
scipy
string
sys
time
xml
“`
If you would like to use any other libraries, you must ask permission within a maximum of two weeks after the assignment was released, using [canvas](http://cs1656.org).
About your github account
* Since we will utilize the github classroom feature to distribute the assignments it is very important that your github account can do **private** repositories. If this is not already enabled, you can do it by visiting <https://education.github.com/>
How to submit your assignment
We are going to use Gradescope to submit and grade your assignments.
To submit your assignment:
* login to Canvas for this class <https://cs1656.org>
* click on Gradescope from the menu on the left
* select “Term Project” from the list of active assignments in Gradescope
* follow the instructions to submit your assignment.
