Advanced Computer Science Projects 2017 -2018

Hi All,

Doing an innovative project using advance technology is no big deal.
You need passion...that's the only requirement.
Anyone can be taught any technology if interested.


1. Windspeed prediction using Artificial Neural Networks (ANN)

2. Weather Reporting using R-Pi, Python and Twitter Interface

3. Student performance Prediction using Artificial Neural Networks (ANN)

4. Weather Forecasting using Artificial Neural Networks (ANN)

5. Twitter Sentiment Analysis using Python

6. Cancer prediction using Artificial Neural Networks (ANN)

7. Protein Interaction Prediction using Support Vector machine(SVM)

8. Car Number Plate detection and recognition using Matlab Image Processing Toolkit.

9. DNA cryptography for Text and Image Encryption using Matlab

Data cleaning using Python

Hi, All
Data cleaning is considered vital. See the code below.



import HTMLParser
html_parser = HTMLParser.HTMLParser()
tweet = html_parser.unescape(original_tweet)
# Actual text
#“I luv my <3 iphone & you’re awsm apple. DisplayIsAwesome, sooo happppppy 🙂 http://www.apple.com”
tweet = original_tweet.decode("utf8").encode(‘ascii’,’ignore’)
APPOSTOPHES = {“'s" : " is", "'re" : " are", ...} ## Need a huge dictionary

words = tweet.split()

reformed = [APPOSTOPHES[word] if word in APPOSTOPHES else word for word in words]

reformed = " ".join(reformed)
cleaned = “ ”.join(re.findall(‘[A-Z][^A-Z]*’, original_tweet))
    tweet = _slang_loopup(tweet)
tweet = ''.join(''.join(s)[:2] for _, s in itertools.groupby(tweet))


https://www.analyticsvidhya.com/blog/2014/11/text-data-cleaning-steps-python/







Advanced data cleaning:

Grammar checking:
Spelling correction:

NLTK in Python

Hi All,

Natural Language Toolkit (NLTK)

NLTK is a leading programming platform for building Python programs to work with human language data itms. It provides easy-to-use interfaces along with a suite of text processing libraries for classification, tokenization, stemming, tagging, parsing, and semantic reasoning, wrappers for industrial-strength NLP libraries etc

Some simple things you can do with NLTK

Tokenize and tag some text data:

>>> import nltk

>>> sentence = """At eight o'clock on Thursday morning

... Arthur didn't feel  good."""

>>> tokens = nltk.word_tokenize(sentence)

>>> tokens

['At', 'eight', "o'clock", 'on', 'Thursday', 'morning',

'Arthur', 'did', "n't", 'feel', 'very',  '.']

Range List Program in Python

Hi All,

See the program

rangelistmain  = range(10)

>>> print(rangelistmain )

[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]

for number in rangelistmain :

    # Check if number is one of

    # the numbers in the tuple.

    if number in (3, 4, 7, 9):

        # "Break" terminates a for without

        # executing the "else" clause.

        break

    else:

        # "Continue" starts the next iteration

        # of the loop. It's rather useless here,

        # as it's the last statement of the loop.

        continue

else:

    # The "else" clause is optional and is

    # executed only if the loop didn't "break".

    pass # Do nothing

if rangelistmain [1] == 2:

    print("The second item (lists are 0-based) is 2")

elif rangelistmain [1] == 3:

    print("The second item (lists are 0-based) is 3")

else:

    print("Dunno")

while rangelistmain [1] == 1:

    pass