What does tf-idf mean?

Tf-idf stands for term frequency-inverse document frequency, and the tf-idf weight is a weight often used in information retrieval and text mining. This weight is a statistical measure used to evaluate how important a word is to a document in a collection or corpus. The importance increases proportionally to the number of times a word appears in the document but is offset by the frequency of the word in the corpus. Variations of the tf-IDF weighting scheme are often used by search engines as a central tool in scoring and ranking a document's relevance given a user query.

One of the simplest ranking functions is computed by summing the tf-IDF for each query term; many more sophisticated ranking functions are variants of this simple model.

Tf-idf can be successfully used for stop-words filtering in various subject fields including text summarization and classification.

How to Compute:

Typically,…

In this, we will analyze the earthquake data using API

Downloading Earthquake Dataset

"""
Downloading and Parsing Earthquake JSON Data
"""

import urllib.request, urllib.parse, urllib.error
import json

earthquakeURL =  "http://earthquake.usgs.gov/fdsnws/event/1/query?"
paramD = dict()
paramD["format"] = "geojson"                 # the format the data will be in
paramD["starttime"] = "2019-06-01T00:00:00"  # the minimum date/time that might be retrieved
paramD["endtime"] = "2019-06-30T23:59:59"    # the maximum date/time that might be retrieved
paramD["minmag"] = 6                         # the smallest earthquake magnitude to return
paramD["limit"] = 5                          # the maximum number of earthquakes to return
                                             # starts with the most recent

params = urllib.parse.urlencode(paramD)
print('Retrieving', earthquakeURL+params)
uh = urllib.request.urlopen(earthquakeURL+params)
data = uh.read().decode()
print('Retrieved', len(data), 'characters')

try:
    js = json.loads(data)
except:
    js = None
        
if not js or 'type' not in js :
    print('==== Failure To Retrieve ====')
    print(data)
    
# Output first Record
print("\nFirst Earthquake")
lng = js["features"][0]["geometry"]["coordinates"][0] # retrieve the first item in features array
lat = js["features"][0]["geometry"]["coordinates"][1] # look in "geometry" object
dep = js["features"]…

Shark attacks!

You have already worked extensively with the shark attack dataset. This version has a few additional columns:

• The area contains a more precise description of where the incident occurred,

• Type a broad description of why it happened (importantly, whether the attack was provoked or not),

• Injury describes the severity of the injury sustained by the victim (if available),

• Species the shark species involved in the attack (insofar it is known),

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Assignment Requirement:

Google Speech Recognition API , AI ML based Personalisation & automation of search & filters