Exploratory Data Analysis: Digging Through the Backlog

In the inspiring story of the Six Triple Eight, the first step of their mission was to assess and organize an overwhelming backlog of undelivered mail. These stacks, towering to the ceiling, had to be categorized and understood before any progress could be made. In the world of modern machine learning, this initial phase is akin to Exploratory Data Analysis (EDA).

For this series, we’ll replicate this process using a CSV dataset, where each row contains a category (e.g., "tech," "business") and the text associated with it. The categories function as labels, indicating where each piece of text belongs. Tools like Pandas for data manipulation, Matplotlib for visualization, WordCloud for textual insights, Tiktoken for token analysis, and NLTK for text processing will help us understand our dataset.

In this step, we will:

1. Load the data and inspect its structure.

2. Identify missing or inconsistent values that could hinder our model's performance.

3. Explore category distributions to understand the balance between labels.

4. Visualize word frequencies within text data to uncover patterns.

5. Analyze token counts using Tiktoken to measure complexity.

This EDA phase mirrors the meticulous sorting efforts of the Six Triple Eight, who had to make sense of chaos before they could bring order. By understanding our dataset in detail, we lay the foundation for building a fine-tuned LLM capable of categorising and interpreting text with precision.

Introduction

Exploratory Data Analysis (EDA) is akin to tackling a daunting backlog of data—stacked high, unorganized, and filled with untapped potential. Much like the Six Triple Eight unit tackled the overwhelming backlog of undelivered mail during World War II, EDA is our way of sifting through the chaos to uncover insights, identify trends, and prepare for the next stages of data analysis.

In this exploration, we’ll dive into a dataset of BBC news articles, unraveling its structure, addressing inconsistencies, and uncovering the stories buried within the data."

Assessing the Backlog: Dataset Overview

To begin, we must first understand the scale and structure of our dataset. The BBC news articles dataset comprises 2,234 entries distributed across five categories: business, sports, politics, tech, and entertainment. Each entry has two main features:

• category: The topic or section of the article.
• text: The full content of the article.

To get a clearer view of what we’re working with, we loaded the data into a Pandas DataFrame, performed a quick inspection, and discovered:

Cleaning the Backlog

As the Six Triple Eight tackled unsorted piles of mail, we too need to organize our dataset. The cleaning process involved several key steps:

• Removing Duplicates
  Duplicate articles cluttered the dataset. After identifying and removing these redundancies.

• Handling Missing Values
  Though our dataset was relatively clean, we ensured that any potential null values were addressed, leaving no empty entries in the final data."

Breaking Down the Categories

With the backlog cleared, we analysed the distribution of articles across categories to identify dominant themes. Here's what we found:

• Top Categories: Business and sports tied for the largest share, each containing 512 articles.

• Smaller Categories: Entertainment, politics, and tech had fewer articles but offered unique insights.

The distribution confirmed that the dataset was balanced, allowing us to focus on deeper analysis without worrying about significant category imbalance."

Zooming In: Sports Articles Under the Microscope

Much like sorting mail by its destination, we chose to focus on the sports category for a deeper dive. The goal was to analyze the textual content and extract meaningful patterns."

• Tokenization and Stopwords Removal
  Using the NLTK library, we tokenized the text into individual words and removed common stopwords (e.g., 'and,' 'the,' 'is'). This allowed us to focus on words with greater significance to the category."

• Word Frequency Analysis
  A frequency distribution was created to identify the most common terms in sports articles. Unsurprisingly, words like 'match,' 'team,' and 'game' dominated, reflecting the competitive nature of the content."

Visualizing the Findings: A Word Cloud
To capture the essence of the sports articles, we generated a word cloud. The most frequently used terms appear larger, painting a vivid picture of the category's core themes."

Key Takeaways

Just as the Six Triple Eight meticulously sorted and delivered the backlog of mail, our EDA process has unveiled a structured and insightful view of the BBC news dataset.

Code

!pip install tiktoken
!pip install matplotlib
!pip install wordcloud
!pip install nltk
!pip install pandas

import pandas as pd

df = pd.read_csv('/content/bbc.csv', on_bad_lines='skip')  


df.head()

df.info()

df.describe()

label_count = df['category'].value_counts()


len(df['text'])


df.drop_duplicates(inplace=True)

null_values = df.isnull().sum()

df.dropna(inplace=True)

import nltk
from nltk.corpus import stopwords
from nltk.tokenize import word_tokenize
from wordcloud import WordCloud
from collections import Counter
import matplotlib.pyplot as plt


nltk.download('punkt')
nltk.download('stopwords')
nltk.download('punkt_tab') 


target_label ="sport"
target_df = df[df['category'] == target_label]



target_word = [ word.lower()  for text in target_df['text']
                 for word in word_tokenize(text)
                 if word.isalnum() and word not in stopwords.words('english')
                   ]

target_word_count = Counter(target_word)


word_cloud = WordCloud().generate_from_frequencies(target_word_count)


plt.figure(figsize=(10, 5))
plt.imshow(word_cloud, interpolation='bilinear')
plt.axis('off')
plt.show()

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