Scraping Fafa.kz for Almaty Delivery Market Insights

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Project Repository

Willing to start a delivery business at Almaty? Here are some insights into the market…

In this project, I:
  1. Scraped the data from the https://fa-fa.kz/search_load/gruzy-almaty/ 
  2. Cleaned Data  
  3. Introduced Data 
  4. Did an Exploratory Data Analysis 
  5. Answered 3 Business Questions  
  6. Presented Suggestions on a profitable business model

1.     Scarping Data

Scraped the data using the Beautiful Soup module, a function that iterated the scraper till the last page (see fafa_Almaty.py), and formatted it into a dictionary. Later, I wrote all the data into a csv. 

 

Picture 1.  fafa_Almaty.py

As an output, I got the fafa_Almaty.csv file containing the following information about every delivery: Destination of the delivery, Price, Product Name, Special Requests (Truck type).

 

Picture 2. fafa_Almaty.csv

2.     Cleaning Data

*For more details find the fafa_Almaty.ipynb Jupiter Notebook attached.

Cleaned the data by removing dots in numbers and the currency, normalizing Special Requests in the Jupiter Notebooks or in the csv file directly, as it was more convenient.

3.     Introduction to Data

Formatted the fafa_Almaty.csv file into a dataframe though numpy, looked through it, added column names and thought about changes that needed to be made for Data Analysis through describe(), shape, other functions.

 

Picture 3. Introduction to Data

4.      Categorizing, Preparing for Data Analysis 

Categorized Products into 9 Product groups based on similar characteristics to analyze better.

Example: 
  1. Veggies, Fruits, and Beverages all need to be in a cool place, so they all need a truck with a fridge.
  2. Building materials are mostly lengthy.
  3. Equipment is heavy.
  4. Food - no need for a fridge.
etc...

I went through products and made arrays of what products should go to what group, and then added if-else statements that check all the product names and group them based on what arrays they suit.

 

Picture 4. Grouping Products

Later, I checked if there were any uncategorized products through the unique command. Check the JN for that :)

5.     Exploratory Data Analysis 

Answering the following Business Questions…

  1. Which categories bring the most revenue?

Iterated through an array of product names and found the sum of prices, which is a Revenue for all 9 Categories, and made a Bar Plot to visualize the data. 
Here, we see that Building Materials and TNP give the most revenue. 

Figure 1. Revenues for 9 Product Groups

  1. Building - 9 035 160 TNG,
  2. TNP - 8 570 150 TNG.

Other Categories yield a lot less Revenue.

2. What trucks are most needed for the most popular Categories?

Building Materials need back and a side of the TENT trucks in most of the cases. Overall, 37 cases where they need Tent trucks (see JN).

Figure 2. Trucks for Building Materials Category


TNP needs TENT trucks in 10/21 cases, and the back of the TENT trucks most of the times, according to the prie chart below.

Figure 3. Trucks for TNP

Overall, TENT Trucks are the most popular in both Categories.

3. What are the popular destinations for the most popular Categories?

For Building Materials delivered from Almaty, the most popular destinations are Shymkent and NurSultan, accounting for 25 cases.

Figure 3. Destinations Distribution for Building Materials

For TNP delivered from Almaty, the most popular destinations are Shymkent, Horgos, Turkestan, accounting for 8 cases.

Figure 4. Destinations Distribution for TNP

Overall, for both Categories Shymkent is the most popular, and Nursultan is the next best.


6.     Conclusion & Suggestions for Business Actions

By Scraping, Cleaning, and Exploring Data from https://fa-fa.kz/search_load/gruzy-almaty/  for Almaty we reached 3 main insights:

  1. TNP and Building Categories bring the most revenue for deliveries.
  2. These two categories need TENT Trucks, especially, their back part.
  3. They are headed to Shymkent and Nursultan mostly.

đź“ŤMore insights and plots can be found in the Jupiter Notebook attached.

Actions: 

This data helps people who want to enter the market to know:

  1. What trucks to buy? - TENT truck with big space in the back.
  2. Whom they should advertise their delivery services? - People who need their equipment and TNP delivered.
  3. What workers to hire & want to teach them? - Who are willing to travel to Shymkent or NurSultan and know/learn the route.

7. Additional Comments:

Delivery trends may change with the season and consumer demand, so doing such analysis more often will yield more profitable actions.

Thank you!


Author: Ayazhan Kadessova.

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