https://github.com/vlad1343/python-advanced

A collection of projects exploring advanced Python concepts, including email handling, GUI automation, data analysis, visualizations, and deep learning for NLP tasks. Designed for learning, practicing, and automating real-world tasks.
https://github.com/vlad1343/python-advanced

email-handler email-sender gui machine-learning machine-learning-algorithms nlp nlp-machine-learning pandas pandas-python python python3

Last synced: 3 months ago
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A collection of projects exploring advanced Python concepts, including email handling, GUI automation, data analysis, visualizations, and deep learning for NLP tasks. Designed for learning, practicing, and automating real-world tasks.

• Host: GitHub
• URL: https://github.com/vlad1343/python-advanced
• Owner: Vlad1343
• Created: 2025-09-07T07:32:07.000Z (10 months ago)
• Default Branch: main
• Last Pushed: 2025-09-30T10:28:38.000Z (9 months ago)
• Last Synced: 2025-09-30T12:28:06.718Z (9 months ago)
• Topics: email-handler, email-sender, gui, machine-learning, machine-learning-algorithms, nlp, nlp-machine-learning, pandas, pandas-python, python, python3
• Language: HTML
• Homepage:
• Size: 13.4 MB
• Stars: 0
• Watchers: 0
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

          
# Python-Advanced

This repository contains experiments and projects exploring advanced Python concepts and libraries. It is a work-in-progress for learning and practicing new Python skills.

---

## Topics & Libraries Explored

- **Email Handling:** Sending and reading emails using `smtplib`, `imapclient`, and `pyzmail`.  

- **Security:** Handling passwords securely with `getpass`.  

- **GUI Automation:**  

  - Controlling the keyboard and mouse using `pyautogui`.  

  - Automate repetitive tasks like filling out forms, pressing buttons, and navigating applications.  

  - Use screen color checks and screenshots to help the program stay on track.  

  - Best practice: make programs fail quickly on invalid instructions to prevent unintended actions.  

  - Simulate human-like actions and watch the mouse move while text appears automatically.  

- **Data Analysis & Visualization:**  

  - Analyze COVID-19 datasets using `pandas`.  

  - Visualize global confirmed, recovered, and death cases with interactive Plotly charts.  

  - Enhanced visualizations with styled pie/donut charts, annotations, and color-coded segments.  

- **Natural Language Processing & Deep Learning:**  

  - Build deep learning models using `TensorFlow` and `Keras`.  

  - Text preprocessing with tokenization, padding, and GloVe embeddings.  

  - Convolutional layers (Conv1D) to detect local patterns in text.  

  - LSTM layers to capture long-term dependencies in sequences.  

  - Binary classification models for tasks like fake news detection.  

---

## Projects Included

### Fake News Detection Model (TensorFlow & NLP)

- Reads a news dataset containing titles, text, and labels (FAKE/REAL).  

- Preprocesses data by combining title and text, tokenizing, and padding sequences.  

- Encodes labels into numerical format for model training.  

- Uses pre-trained GloVe embeddings to represent words in real-valued vector space.  

- Model architecture:  

  - **Embedding Layer:** Maps words to dense vectors using GloVe embeddings.  

  - **Conv1D + MaxPooling:** Detects local textual patterns and highlights important features.  

  - **LSTM Layer:** Captures context and long-term dependencies in the text.  

  - **Dense + Sigmoid:** Outputs final classification probability (Fake or Real).  

- Trains on a subset of the dataset and validates on a test set.  

- Makes predictions on new articles by combining title and text, tokenizing, padding, and feeding to the trained model.  

- Saves the trained model for reuse.

- Provides tokenized sequences and prediction probability for transparency.  

### Data Analysis Projects

- **COVID-19 Global Analysis with Plotly:**  

  - Reads time-series CSV datasets for confirmed, recovered, and death cases.  

  - Computes total active, recovered, and dead cases.  

  - Generates interactive donut charts with custom colors, annotations, and hover information.  

  - Produces shareable HTML chart files for visualization.  

---

## Tech Stack

- **Programming Language:** Python 3.x  

- **Data Analysis & Visualization:** `pandas`, `numpy`, `plotly`, `matplotlib`, `seaborn`  

- **Machine Learning & Deep Learning:** `tensorflow`, `keras`, `scikit-learn`  

- **Text Processing & NLP:** `keras.preprocessing.text.Tokenizer`, `pad_sequences`, GloVe embeddings  

- **Automation & GUI Interaction:** `pyautogui`  

- **Email Handling & Security:** `smtplib`, `imapclient`, `pyzmail`, `getpass`  

- **File Handling & I/O:** CSV, HTML export for interactive visualizations  

---

## Purpose

The goal of this repository is to gradually build experience with advanced Python functionality, including:

- Natural language processing and deep learning model development  

- Automation of repetitive tasks on your computer  

- Email interaction and handling  

- Secure handling of sensitive information  

- Data analysis and interactive visualization  

New projects and experiments will be added over time as skills progress.