Recently Published
Plot
# Ordenar las palabras de mayor a menor frecuencia
top10$palabra <- factor(top10$palabra, levels = rev(top10$palabra[order(top10$Frecuencia)]))
# Gráfico estilo imagen
ggplot(top10, aes(x = palabra, y = Frecuencia, fill = palabra)) +
geom_col(width = 0.9) +
geom_label(
aes(label = Frecuencia),
color = "white", # texto blanco
size = 4,
label.size = 1, # sin borde
fontface = "bold",
show.legend = FALSE
) +
#35
theme(axis.text.x = element_text(angle = -45, vjust = 1, hjust=-0.3)) + #36
theme_light() + #37
theme(legend.position = "topleft") + #38
facet_grid(~"Palabras más Frecuentes") + #39
coord_flip() +
theme_light() +
theme(
plot.title = element_text(hjust = 0.5, face = "bold"),
legend.position = "none"
)
Survival Analysis : Understanding and Visualizing Censoring
Have you ever tried to track something, but some outcomes were still unknown? That's what we call "censoring" in data. This simple guide explains how we still learn from those "unfinished stories" when analyzing how long things take to happen, making sense of real-world data where we don't always have all the answers.
Next Word Predictor App Presentation
---
title: "Text Prediction App"
author: "Anila Mohanan"
output: ioslides_presentation
---
## Introduction
- Built as part of Coursera Data Science Capstone
- Predicts the next word in a user-entered phrase
- Uses blogs text dataset is used.
---
## How the Algorithm Works
- Text cleaned and tokenized using `tidytext`
- Created **unigram**, **bigram**, and **trigram** frequency tables
- Uses **n-gram model with backoff**:
- If trigram not found → backoff to bigram → backoff to unigram
---
## Prediction Model
- Efficient and memory-optimized using RDS storage
- Fast lookup using `dplyr` filtering and `slice_max`
- Handles unknown inputs with fallback strategy
---
## Shiny App Demo
- Built using **Shiny** and hosted on **shinyapps.io**
- User enters a phrase → App predicts the next word
- Try it live here:
**https://x1tkzd-anila-mohanan.shinyapps.io/NextWordPredictor/**
---
## Summary
- Accurate, fast predictions with minimal resources
- Real-time prediction from cleaned social and web text
- Could be extended into mobile keyboards or chat assistants