https://github.com/geetisha/advanced-eda-and-text-mining

Advanced EDA and Text Mining
https://github.com/geetisha/advanced-eda-and-text-mining

jupyter-notebook matplotlib nltk numpy pandas python spacy textblob wordcloud

Last synced: about 2 months ago
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Advanced EDA and Text Mining

• Host: GitHub
• URL: https://github.com/geetisha/advanced-eda-and-text-mining
• Owner: geetisha
• Created: 2025-03-09T09:24:08.000Z (2 months ago)
• Default Branch: main
• Last Pushed: 2025-03-18T08:50:42.000Z (about 2 months ago)
• Last Synced: 2025-03-18T09:38:55.645Z (about 2 months ago)
• Topics: jupyter-notebook, matplotlib, nltk, numpy, pandas, python, spacy, textblob, wordcloud
• Language: Jupyter Notebook
• Homepage:
• Size: 1.08 MB
• Stars: 1
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# **Advanced EDA and Text Mining**

## **Project Overview**  

The **Advanced EDA (Exploratory Data Analysis) and Text Mining** project focuses on extracting meaningful insights from structured and unstructured data. This project leverages **Python**, **Pandas**, **Matplotlib**, and **NLP (Natural Language Processing)** techniques to analyze large datasets, uncover patterns, and perform text mining to gain deeper business insights.

## **Objectives**  

- Conduct **Advanced Exploratory Data Analysis (EDA)** to understand data distributions, correlations, and key trends.

- Perform **Text Mining and Natural Language Processing (NLP)** to analyze unstructured textual data.

- Apply **word frequency analysis, topic modeling, and sentiment analysis** to extract insights from text.

- Visualize patterns and relationships using **Matplotlib and Seaborn**.

- Derive actionable insights that help businesses optimize strategies based on data-driven findings.

## **Tools & Technologies Used**  

- **Python** for data processing and analysis  

- **Pandas & NumPy** for data manipulation  

- **Matplotlib & Seaborn** for visualization  

- **NLTK, SpaCy, or TextBlob** for text mining and NLP  

- **WordCloud** for visualizing word frequency  

- **Jupyter Notebook** for interactive analysis  

## **Key Analysis & Insights**  

- **Data Cleaning & Preprocessing:** Handling missing values, outlier detection, and feature engineering.  

- **EDA & Visualization:** Identifying trends, correlations, and statistical summaries.  

- **Text Mining:** Extracting key topics, performing named entity recognition (NER), and word frequency analysis.  

- **Sentiment Analysis:** Categorizing text data into **positive, negative, or neutral sentiments**.  

- **Topic Modeling:** Identifying underlying themes in textual data using **LDA (Latent Dirichlet Allocation)**.  

## **Results & Business Impact**  

- Provides **deeper insights** into structured and unstructured data for better decision-making.  

- Helps businesses **understand customer sentiment** and extract **key topics of discussion**.  

- Assists in **trend analysis** for better market predictions and business strategy planning.  

- Improves data-driven recommendations by uncovering hidden patterns in large datasets.  

## **Conclusion**  

The **Advanced EDA and Text Mining** project demonstrates the power of data exploration and NLP techniques in extracting meaningful insights. By combining **statistical analysis, visualization, and text mining**, this project helps businesses leverage data for informed decision-making, customer sentiment analysis, and trend forecasting.