Label Encoder() -for more accuracy#label = np.array(lab_enc.fit_transform(train_data[‘std_score’]))Since score is a continuous value,hence we used linear regression model and the error percentage will be calculated by Mean Squared Error, Root Mean Square Error and Cohen’s kappa score and in case of SVR Model after features scaling fit the data in the model and its score will be calculated where gamma is set to “scale” and epsilon= “0.2" where in features both the bag of words and various other features stated above such as lemma, noun, pronoun, misspell etc. Result Analysis: Grading predicted for the student of a given essay : Conclusion: In our discussion, we talked about efforts on breaking the barriers by attempting to featurize characteristics for good writing in general, such as sentence coherence.
We attempted to build a regressorfor this task, with limited success.
Upon examining the data, we realized that in this project we don’t have any missing data but have categorical data that needs to be converted in integer values to make the model work.
And to do that we used one-hot encoding and Label Encoder().
If you want an amusing way to whiling away a rainy afternoon, take a piece of literary prose you consider sublimely masterful and run the Microsoft Word™ grammar checker on it, accepting all the suggested changes.” ( The popular website Top Ten Reviews does a nice job reviewing the four most popular grammar checkers, although their top choice, Grammarly, did happen to advertise rather prominently on their site.
In the review site’s testing, Grammarly caught 10 of 14 “grammar” errors.Of this majority, some comment on writing content; some on essay structure; some on the quality and relevance of evidence; some on the proper use of citations; some on grammar and usage; some on mechanics (punctuation, capitalization, spelling, etc.); and some attend to matters of writing style. Wikipedia has a nice article, Grammar Checker, which explains the programming limitations of grammar checkers, but suffice it to say for non-techies: grammar checking software is a whole lot harder to program than is spelling.My take is that we should encourage students to spell check and revise accordingly, but skip the grammar check and proofread instead. Pullum, Professor of General Linguistics at the University of Edinburgh, agrees with greater reservations: “For the most part, accepting the advice of a computer grammar checker on your prose will make it much worse, sometimes hilariously incoherent.Additionally, the statistical correlation between some features (for instances where they were available) were appeared to be not related. In addition to this, sampling was necessary for machine learning algorithms.Library Used: import pandas as pd import nltk nltk.download(‘stopwords’) nltk.download(‘words’) nltk.download(‘punkt’) from nltk.corpus import stopwords from nltk.corpus import words import re import numpy as np from sklearn.feature_import Tfidf Vectorizer from sklearn import preprocessing from sklearn import utils from sklearn import metrics from spellchecker import Spell Checker from sklearn.linear_model import Linear Regression, Ridge, Lasso from import SVR from porter import Porter Stemmer from spellchecker import Spell Checker from sklearn.decomposition import Truncated SVD import string nltk.download(‘averaged_perceptron_tagger’) from import Word Net Lemmatizer from nltk.corpus import wordnet import re, collections from collections import defaultdict from sklearn.feature_import Count Vectorizer from sklearn.metrics import mean_squared_error, r2_score from sklearn.model_selection import train_test_split from import SVRRegression Method: We had the data in 8 esssay set format we had analysed the data in following ways :# Fitting Logistic Regression to the Training set#Creating the Bag of Words model# Text to Features #lab_enc = preprocessing.Our solution for this problem will be provided under the link stated below:https://github.com/ishitatiwari72/Automated-Essay-Grading Forsk Technologies link : https://medium.com/@yogendrasinsinwar Dataset: Hewlett is appealing to data scientists and machine learning specialists to help solve an important social problem.We need fast, effective and affordable solutions for automated grading of student-written essays.After this , Bag Of Words were extracted from each essay by Count Vectroizer Method but this will create memory issue hence Tfid Vectroizer Method will be used with ngrams of (1,3) and the max features will be taken of about 1500 and there pickel file will be saved in order to use later while doing prediction these were refers to “Latent Semantic Analysis”.Secondly, “Natural Language Tool Kit” refers to nltk will be downloaded with stopwords, missspell dictionary and all the necessary library stated in pdf file.Conscientious teachers still mark up and comment on student essays.Despite recent trends toward holistic grading and the views of some kind-hearted souls who believe that “red marking” student writing irreparably crushes self-esteem, the vast majority of teachers do respond to student writing. So, naturally, teachers look for short-cuts that will save energy and time, but ones which will still give students what they need as developing writers. Whereas spelling checkers, either as a stand-alone software or as a tool embedded in word processing programs such as Microsoft Word®, do a reasonable job of finding spelling errors (other than troublesome homonyms), grammar checkers simply cannot replicate that effectiveness.