🚀 Portfolio

Welcome to my repository, a collection of my exploratory projects in the diverse fields of Data Science and Machine Learning.

For a detailed understanding of these projects, you can refer to the comprehensive documentation available here.

In addition to these projects, I regularly share my insights and learnings on the Medium platform. You can access my articles here.

Please note: The projects listed below are organized alphabetically for your convenience.

🚨 Anomaly Detection

• Credit Card Fraud Detection: Unveil fraudulent transactions using a neural network-based approach.

🏭 Automation

• Automated GitHub Commits: Simplify your workflow with an automated solution for committing and pushing changes to GitHub.

📷 Computer Vision

• Ants vs Bees Image Classification: Harness the power of deep learning models to classify images.

🧩 Data Structures

• Understanding Hashing: Dive into the world of hashing, its applications, and Python implementation.
• Sorting Algorithms: A comprehensive guide to understanding and implementing popular sorting algorithms in Python.

🎨 Data Visualization

• lets-plot: Create stunning plots with lets-plot, a Python port of the R's ggplot2 library.
• Pitfalls: Avoid common pitfalls in data visualization.
• QR Code: Generate QR codes with ease.

🔍 EDA (Exploratory Data Analysis)

• Data Balancing: Learn techniques to balance imbalanced datasets.
• Handling Missing Data: Discover various methods for handling missing data in datasets.
• Polars: Leverage the Polars library for efficient data manipulation and analysis.

🛠️ ETL (Extract, Transform, Load)

• ETL Pipeline with Airflow and Docker: A project showcasing the automation of data extraction, transformation, and loading into a database.

⚙️ Hyperparameter Tuning

• KerasTuner: Optimize your models with hyperparameter tuning using the KerasTuner library.
• Optuna: Enhance your models with hyperparameter tuning using the Optuna library.

🧠 LLM (Large Language Model)

• Tokenization: Explore the tokenization of text data.

🤖 Machine Learning

• Best Threshold for Logistic Regression: Explore different methods to find the optimal threshold for logistic regression.

🔒 Privacy

• Anonymization: Learn about data anonymization and its applications.
• Encryption: A guide to understanding and implementing Python encryption.

🐍 Python

• Argument Parsing: Master argument parsing using the argparse module.
• Calendars: Explore the calendar module in Python.
• Generators: A hands-on guide to understanding and using generators.
• Lambda: Get introduced to lambda functions.
• Pattern Matching: Learn pattern matching with the match-case statement.

📈 Statistical Analysis

• A/B Testing: Test the effectiveness of a new feature in a web application using A/B testing.
• Hypothesis Testing: p-values Around 0.05: Understand when to reject the null hypothesis if the p-value is around 0.05.

💡 Synthetic Data Generation

• Introduction: Learn to generate synthetic data using Python and understand the considerations for using synthetic data.

🖥️ Terminal

• jq: Manipulate JSON with jq.
• Rich: Format text in the terminal using the Rich library.

⏳ Time-series Analysis

• Forecasting with sktime: Forecast time-series data using the sktime library.
• Prevent Overfitting: Learn techniques to prevent overfitting in time series forecasting.

🕸️ Web Scraping

• jobinventory: Scrape job listings from jobinventory.com using Python.

📝 XAI (Explainable AI)

• Introduction: Understand the importance of explainable AI and its applications.