{ {;;;;;;;;;;;; ;;;;;;;; ;;;}{*1506 }{* } {*}{ itle: Data Science
Capstone: Milestone Report
}{ Name}{ : }{ Afreen Mujawar}{
}{ D}{ ate:}{ 02/01/25}{ }{
}{ Summary
}{ The goal of this project is to build a predictive text application
that suggests the next word a user is likely to type.
This milestone report demonstrates progress toward that goal by
exploring the training data, summarizing its main characteristics, and
outlining plans for the final prediction algorithm and Shiny
application.
}{ Data Overview
}{ The data used in this project comes from the HC Corpora dataset and
includes English text from three sources:
- Blogs
- News articles
- Twitter posts
Each dataset consists of plain text files where each line represents a
single text entry.}{
}{
}{ Loading the Data
}{ ```{r load-data, message=FALSE, warning=FALSE}
blogs <- readLines(“en_US.blogs.txt”, encoding = “UTF-8”, skipNul =
TRUE)
news <- readLines(“en_US.news.txt”, encoding = “UTF-8”, skipNul =
TRUE)
twitter <- readLines(“en_US.twitter.txt”, encoding = “UTF-8”, skipNul
= TRUE)}{
}{ ## }{ Data Cleaning and Sampling
}{ Due to the size of the data, a small random sample was used for
exploratory analysis.}{
}{ set.seed(123)
sample_data <- c(sample(blogs, length(blogs) * 0.01),sample(news,
length(news) * 0.01),sample(twitter, length(twitter) * 0.01))
}{
}{ The sampled text was cleaned by:
{ 'b7}}{ Converting text to lowercase
{ 'b7}Removing punctuation and numbers
{ 'b7}Removing common English stop words
} { library(tm)
corpus <- VCorpus(VectorSource(sample_data))corpus <-
tm_map(corpus, content_transformer(tolower))corpus <- tm_map(corpus,
removePunctuation)corpus <- tm_map(corpus, removeNumbers) corpus
<- tm_map(corpus, removeWords, stopwords(“english”))corpus <-
tm_map(corpus, stripWhitespace)}{
}{ Exploratory Analysis
Most Common Words
}{ tdm <- TermDocumentMatrix(corpus)word_freq <-
sort(rowSums(as.matrix(tdm)), decreasing = TRUE)
head(word_freq, 10)
}{
}{ Word Frequency Plot}{
}{ library(ggplot2)
top_words <- data.frame(word = names(word_freq)[1:20],freq =
word_freq[1:20])
ggplot(top_words, aes(x = reorder(word, freq), y = freq)) +geom_bar(stat
= “identity”) +coord_flip() +labs(title = “Top 20 Most Frequent Words”,x
= “Word”,y = “Frequency”)
}{
} { Observations
{ 'b7}} { A small number of words appear very frequently.
{ 'b7}This pattern supports the use of n-grambased prediction models.
{ 'b7}Twitter text is shorter and more informal than blog or news
text.}{
} {
} { Plans for the Prediction Algorithm and Shiny App
}{ The final application will use }{ n-gram language models}{ to predict
the next word based on previous input.
Planned steps include:
{ 'b7}} { Building unigram, bigram, and trigram models
{ 'b7}Applying backoff strategies to improve predictions
{ 'b7}Reducing model size to ensure fast performance
} { The completed model will be deployed as a }{ Shiny web application}{
, allowing users to interactively test text predictions in real time.}{
}{ Conclusion
}{ This milestone report demonstrates successful data loading, summary
analysis, and exploratory investigation of the training datasets. The
insights gained here confirm that the data is suitable for building a
predictive text model and provide a clear path forward for completing
the final application.
} {
}{
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