can you give me again a table of main steps, substeps and subsub steps with detailed description, potential tools and python ml libs at each point:
Step	Substeps
Data Collection	- Collect email data and store it in a dataframe
Data Cleaning	- Remove punctuation marks
	- Convert text to lowercase
	- Handle noise in the text (e.g., HTML tags, special characters)

	-  Visualization 
Data  Preprocessing	
Language Detection	- Detect the language of each email text
Language Translation	- If the language is not French (fr) or English (en), translate it to either English or French
Spelling Correction	- Evaluate the performance of different spelling correction techniques using NLP distances
Sentence Segmentation	- Split the text into sentences
	-  Visualization
Word Tokenization	- Split each sentence into individual words
Stemming	- Reduce each word to its base or root form (e.g., running -> run)
Lemmatization	- Convert each word to its base form (e.g., running -> run)
Stopword Analysis	- Identify and remove common stopwords (e.g., "the," "is," "and")
Cleaning and Normalization	- Perform additional cleaning and normalization steps specific to your data
Text Normalization and Enrichment	Text Normalization and Enrichment
Dependency Parsing	- Analyze the grammatical structure of sentences and identify relationships between words
Part-of-Speech (POS) Tagging	- Assign a grammatical tag to each word (e.g., noun, verb, adjective)
Named Entity Recognition	- Identify and classify named entities (e.g., person names, locations, organizations)
Machine Learning and Deep Learning	
Topic Modeling	- Apply topic modeling techniques (e.g., LDA, NMF) to extract topics from the email body or subject
Text Summarization	- Implement text summarization algorithms (e.g., extractive or abstractive methods) to generate summaries
Sentiment Analysis	- Perform sentiment analysis on the email text using pre-trained models or lexicons
Evaluate and Fine-tuning	- Assess the quality of the results obtained in each step
	- Refine the preprocessing steps and models based on the evaluation results
	

Step	Substeps	Subsubsteps	Description
Data Collection	- Collect email data and store it in a dataframe	- Retrieve email data from various sources (e.g., IMAP, Exchange) and store it in a structured format (e.g., CSV, JSON) for further analysis
Data Cleaning	- Remove punctuation marks	- Remove any punctuation marks from email text that can interfere with analysis
	- Convert text to lowercase	- Convert all email text to lowercase to standardize the text and reduce the variability in the data
	- Handle noise in the text (e.g., HTML tags, special characters)	- Remove any noise in the text such as HTML tags or special characters that are not relevant to the analysis
	- Visualization	- Visualize the distribution of the data to identify any potential outliers or anomalies
Data Preprocessing	- Language Detection	- Detect the language of each email text using language detection algorithms or libraries to prepare the text for further analysis
	- Language Translation	- Translate emails to English or French if necessary using translation libraries or services to ensure consistency in the text
	- Spelling Correction	- Implement spelling correction techniques such as autocorrection or phonetic algorithms to improve the quality of the text
	- Sentence Segmentation	- Split email text into sentences using natural language processing techniques to break down the text into smaller, more manageable pieces
	- Visualization	- Visualize the distribution of sentence lengths to identify any potential outliers or anomalies
	- Word Tokenization	- Tokenize each sentence into individual words to prepare the text for further analysis
	- Stemming	- Reduce each word to its base or root form using stemming algorithms to reduce the variability in the data
	- Lemmatization	- Convert each word to its base form using lemmatization algorithms to improve the quality of the text
	- Stopword Analysis	- Identify and remove common stopwords (e.g., "the," "is," "and") using predefined lists or frequency analysis to improve the quality of the text
	- Cleaning and Normalization	- Perform additional cleaning and normalization steps specific to your data to further improve the quality of the text
Text Normalization and Enrichment	- Dependency Parsing	- Analyze the grammatical structure of sentences and identify relationships between words using dependency parsing algorithms to extract meaningful information from the text
	- Part-of-Speech (POS) Tagging	- Assign a grammatical tag to each word (e.g., noun, verb, adjective) using POS tagging algorithms to further improve the quality of the text
	- Named Entity Recognition	- Identify and classify named entities (e.g., person names, locations, organizations) using named entity recognition algorithms to extract relevant information from the text
Machine Learning and Deep Learning	- Topic Modeling	- Apply topic modeling techniques (e.g., LDA, NMF) to extract topics from the email body or subject to identify patterns and relationships in the data
	- Text Summarization	- Implement text summarization algorithms (e.g., extractive or abstractive methods) to generate summaries to quickly understand the content of the emails
	- Sentiment Analysis	- Perform sentiment analysis on the email text using pre-trained models or lexicons to identify the sentiment of the emails (e.g., positive, negative, neutral)
Evaluate and Fine-tuning	- Assess the quality of the results obtained in each step	- Evaluate the performance of each step using appropriate metrics to identify areas for improvement
	- Refine the preprocessing steps and models based on the evaluation results	- Fine-tune the preprocessing steps and models based on the evaluation results to improve the quality of the text and the accuracy of the results