Description

100 points
Note: NO late submission will be accepted.
https://www.kaggle.com/code/goyalshalini93/car-price-prediction-linear-regression-rfe/notebook
Note that the data set you are given has different ids for the first and the last car as that in the original data set. Use the data set you are given in this homework. In general, do not assume that car id is one less than the row number.
1. [20 points] Write a Python script load.py that takes the CSV file cars.csv and loads it to your Firebase real time database. Note: you need to load the entire data set to Firebase. Every car should be stored as a JSON object in Firebase.

Execution format: python3 load.py cars.csv
2. [20 points] Search by price range: write a Python script search_price.py that takes a range of price (in two arguments) and outputs IDs of cars in the range (inclusive). Note: you need to create an index on car’s price in the Rules of your Firebase database. If no cars are in the specified price range, then please return ‘No cars found with the given range’.

Execution format (example): python3 search_price.py 15000 16000

Sample test Cases:
1. python search_price.py 15000 16000 IDs for the car price range are:
[6, 59, 112, ……….]

2. python search_price.py 150 160
No cars found with the given range

3. [30 points] Create a keyword index: write a Python script create_index.py that creates a keyword index for keywords appearing in car name. The keywords in a car name are a list of words resulting from tokenizing the car name by punctuation characters and white spaces. For example, “alfa-romero giulia” has 3 tokens: alfa, romero, and giulia, while “dodge colt (sw)” has tokens: dodge, colt, and sw. For each unique keyword in the car names, your index should store
a list of IDs of cars whose name contains the keyword. You need to store the index in Firebase which will be used in the following search task. You should store index separately from the cars data set and should not overwrite the cars data set in Firebase. Note keywords are not case sensitive. So honda and Honda are considered to be identical.

Execute format: python3 create_index.py cars.csv

Execution format (example): python3 search_car.py “honda accord”
Output should contain id’s with both honda and accord first, followed by cars whose name contains only honda or accord.
If no cars contain any given keywords in their names, output “No cars found”. See example below.
Sample test Cases:
1.python search_car.py “honda accord”
IDs of the car are:
[41, 38, 36, 34, 43, 42 …….. ]

2.python search_car.py “dodge coronet custom (sw)” IDs of the car are:
[30, 28, 122, 29, 27, 25, 201, 195, ………]

1. The screenshot of layout of your database for tasks 1 and 3 (.png /.jpg)
2. The .py solution file for each task, that is, load.py, search_price.py, create_index.py, and search_car.py.

Score: (github.com)