Prediction of updates


I have a set of incidents with certain text named short description (text is in bilingual) with [login to view URL] updates.

Now, I want to predict [login to view URL] updates for a new incident based on past incident data sets.


1)Text pre-processing

a) Cleaning the short description column

b) Creating two columns for each language separately

2) Prediction model from past data

a) Calculating TF-IDF value individually for each language

b) Estimation the category(as either High/Low) based on Naive Bayes classifier(F1-measure)

Few details....

Data format: CSV

Data size: 11200 rows

no. of updates class for categorizing :

1)High--if updates >= 12

2)Low--if upddates <12

Further details will be sent personally

Taidot: Data Cleansing, tiedonlouhinta, tietojenkäsittely, Machine Learning (ML), Python

Näytä lisää: tf-idf topic modeling, tf idf tutorial, tf-idf weighting python, el nino forecast 2019, el nino 2018-19, enso blog, el nino forecast california, enso index, enso forecast models, el nino tracker, el nino forecast 2018, tf idf keyword extraction python, tf-idf python github, tf idf python nltk, tf idf python sklearn, tf idf cosine similarity python, tf idf implementation, tf idf spark, tf idf python, tf idf function in r

Tietoa työnantajasta:
( 0 arvostelua ) Hamburg, Germany

Projektin tunnus: #19554436

Myönnetty käyttäjälle:


Hey, Here is my plan on how to approach this. 1. Clean the text using scipy libraries. 2. Use bag of words / ngrams / tf-idf to convert the data into machine learning suitable one. 3. Develop the model to catego Lisää

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