How ramakanth0405/pg_s3_lab works
This repository is a collection of Semester III data-science practicals, not a single application. The main user workflow is to select a topic, run its notebook or script against example or supplied data, and inspect the resulting analysis, model, linguistic processing, or parsed data. It covers machine learning, NLP, and IoT-oriented web scraping, parsing, and cleaning. The README documents capabilities beyond the sampled source; links between unsampled exercises and particular datasets or other stages are therefore not inferred.
Main entry points: PG_S3_NLP NLP Practicals · PG_S3_ML ML Practicals · IoT_Parsing Format Parsers · PG_S3_IoT_0f_web_scrape Web Scraping
Components 26
Learning Resources
- Example Datasets
Datasets
Machine Learning
- ML Practicals
PG_S3_ML - Encoding
PG_S3_ML/PG_S3_ML_0_Encoding.ipynb - Factor Analysis
PG_S3_ML/PG_S3_ML_Factor.ipynb - K-Means
PG_S3_ML/PG_S3_ML_KMeans.ipynb - KNN
PG_S3_ML/PG_S3_ML_KNN.ipynb - PCA
PG_S3_ML/PG_S3_ML_M3_PCA.ipynb - Random Forest
PG_S3_ML/PG_S3_ML_RF.ipynb
Natural Language Processing
- NLP Practicals
PG_S3_NLP - NLP Labs
PG_S3_NLP/PG_S3_NLP_Lab - PoS Tagging
PG_S3_NLP/PG_S3_NLP_2_PoS_nltk.ipynb - N-Grams
PG_S3_NLP/PG_S3_NLP_05a_ngram.ipynb - Word Clouds
PG_S3_NLP/PG_S3_NLP_06_wordcloud.ipynb - TextBlob
PG_S3_NLP/PG_S3_NLP_4_textblob.ipynb - Naive Bayes
PG_S3_NLP/PG_S3_NLP_07_MNB.ipynb - SVM
PG_S3_NLP/PG_S3_NLP_08_SVM.ipynb
IoT Data
- Web Scraping
PG_S3_IoT/PG_S3_IoT_0f_web_scrape - Local HTML
PG_S3_IoT/PG_S3_IoT_0f_web_scrape/IoT_Webscrape_local/PG_S3_IoT_0_sample_web.html - Format Parsers
PG_S3_IoT/PG_S3_IoT_0f_web_scrape/IoT_Parsing - XML Parser
PG_S3_IoT/PG_S3_IoT_0f_web_scrape/IoT_Parsing/IoT_parsing_xml.py - JSON Parser
PG_S3_IoT/PG_S3_IoT_0f_web_scrape/IoT_Parsing/IoT_parsing_json.py - Text Parser
PG_S3_IoT/PG_S3_IoT_0f_web_scrape/IoT_Parsing/IoT_parsing_text.py - CSV Parser
PG_S3_IoT/PG_S3_IoT_0f_web_scrape/IoT_Parsing/IoT_parsing_csv.py - Data Cleaning
PG_S3_IoT/PG_S3_IoT_0f_web_scrape/IoT_Data_Cleaning
Other components
- Learner
- Web Pages
Connections 24
- Learner → ML Practicals: runs exercises
- Learner → NLP Practicals: runs exercises
- Learner → Web Scraping: runs exercises
- ML Practicals → Encoding: includes
- ML Practicals → Factor Analysis: includes
- ML Practicals → K-Means: includes
- ML Practicals → KNN: includes
- ML Practicals → PCA: includes
- ML Practicals → Random Forest: includes
- NLP Practicals → NLP Labs: includes
- NLP Practicals → PoS Tagging: includes
- NLP Practicals → N-Grams: includes
- NLP Practicals → Word Clouds: includes
- NLP Practicals → TextBlob: includes
- NLP Practicals → Naive Bayes: includes
- NLP Practicals → SVM: includes
- Web Scraping → Web Pages: requests pages
- Web Scraping → Local HTML: parses local
- Learner → Format Parsers: runs exercises
- Format Parsers → XML Parser: includes
- Format Parsers → JSON Parser: includes
- Format Parsers → Text Parser: includes
- Format Parsers → CSV Parser: includes
- Learner → Data Cleaning: runs exercise