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It is a work-in-progress for learning and practicing new Python skills.\n\n---\n\n## Topics \u0026 Libraries Explored\n\n- **Email Handling:** Sending and reading emails using `smtplib`, `imapclient`, and `pyzmail`.  \n- **Security:** Handling passwords securely with `getpass`.  \n- **GUI Automation:**  \n  - Controlling the keyboard and mouse using `pyautogui`.  \n  - Automate repetitive tasks like filling out forms, pressing buttons, and navigating applications.  \n  - Use screen color checks and screenshots to help the program stay on track.  \n  - Best practice: make programs fail quickly on invalid instructions to prevent unintended actions.  \n  - Simulate human-like actions and watch the mouse move while text appears automatically.  \n- **Data Analysis \u0026 Visualization:**  \n  - Analyze COVID-19 datasets using `pandas`.  \n  - Visualize global confirmed, recovered, and death cases with interactive Plotly charts.  \n  - Enhanced visualizations with styled pie/donut charts, annotations, and color-coded segments.  \n- **Natural Language Processing \u0026 Deep Learning:**  \n  - Build deep learning models using `TensorFlow` and `Keras`.  \n  - Text preprocessing with tokenization, padding, and GloVe embeddings.  \n  - Convolutional layers (Conv1D) to detect local patterns in text.  \n  - LSTM layers to capture long-term dependencies in sequences.  \n  - Binary classification models for tasks like fake news detection.  \n\n---\n\n## Projects Included\n\n### Fake News Detection Model (TensorFlow \u0026 NLP)\n\n- Reads a news dataset containing titles, text, and labels (FAKE/REAL).  \n- Preprocesses data by combining title and text, tokenizing, and padding sequences.  \n- Encodes labels into numerical format for model training.  \n- Uses pre-trained GloVe embeddings to represent words in real-valued vector space.  \n- Model architecture:  \n  - **Embedding Layer:** Maps words to dense vectors using GloVe embeddings.  \n  - **Conv1D + MaxPooling:** Detects local textual patterns and highlights important features.  \n  - **LSTM Layer:** Captures context and long-term dependencies in the text.  \n  - **Dense + Sigmoid:** Outputs final classification probability (Fake or Real).  \n- Trains on a subset of the dataset and validates on a test set.  \n- Makes predictions on new articles by combining title and text, tokenizing, padding, and feeding to the trained model.  \n- Saves the trained model for reuse.\n- Provides tokenized sequences and prediction probability for transparency.  \n\n### Data Analysis Projects\n\n- **COVID-19 Global Analysis with Plotly:**  \n  - Reads time-series CSV datasets for confirmed, recovered, and death cases.  \n  - Computes total active, recovered, and dead cases.  \n  - Generates interactive donut charts with custom colors, annotations, and hover information.  \n  - Produces shareable HTML chart files for visualization.  \n\n---\n\n## Tech Stack\n\n- **Programming Language:** Python 3.x  \n- **Data Analysis \u0026 Visualization:** `pandas`, `numpy`, `plotly`, `matplotlib`, `seaborn`  \n- **Machine Learning \u0026 Deep Learning:** `tensorflow`, `keras`, `scikit-learn`  \n- **Text Processing \u0026 NLP:** `keras.preprocessing.text.Tokenizer`, `pad_sequences`, GloVe embeddings  \n- **Automation \u0026 GUI Interaction:** `pyautogui`  \n- **Email Handling \u0026 Security:** `smtplib`, `imapclient`, `pyzmail`, `getpass`  \n- **File Handling \u0026 I/O:** CSV, HTML export for interactive visualizations  \n\n---\n\n## Purpose\n\nThe goal of this repository is to gradually build experience with advanced Python functionality, including:\n\n- Natural language processing and deep learning model development  \n- Automation of repetitive tasks on your computer  \n- Email interaction and handling  \n- Secure handling of sensitive information  \n- Data analysis and interactive visualization  \n\nNew projects and experiments will be added over time as skills progress.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fvlad1343%2Fpython-advanced","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fvlad1343%2Fpython-advanced","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fvlad1343%2Fpython-advanced/lists"}