To showcase some of my machine learning and data science projects that highlight my skills, passion for problem-solving, and continuous learning journey. This repository is a work in progress, and I’m excited to share my growth with you!
👋 Hi, I'm Abhishek Banerjee! Welcome to my GitHub profile! I’m a passionate learner diving into the world of programming, machine learning, and data science. My goal is to solve real-world problems through code and make technology accessible to everyone. Whether you're here to collaborate, learn, or explore, I’m thrilled to have you here!
🌟 About Me
- Name: Abhishek Banerjee
- Location: Delhi, India
- Profession: Enthusiastic coder, problem solver, and lifelong learner.
- Languages: English, Hindi, Bengali & little bit of Nepali
- Specialization: Machine learning, data analysis, Python programming, and leveraging AI/ML to solve real-world challenges.
- Hobbies: Exploring generative AI, reading tech blogs, experimenting with new datasets, and building creative solutions.
When I’m not coding, you’ll find me brainstorming ideas, learning new technologies, or contributing to open-source projects. I believe in continuous learning and always strive to improve my skills.
Here are some of the tools, frameworks, and languages I work with:
- Python
- JavaScript (ES6+)
- HTML5 & CSS3
- Scikit-learn
- TensorFlow & Keras
- Flask (for future deployment)
- Pandas & NumPy
- Matplotlib & Seaborn
- MySQL (basic knowledge)
- Git & GitHub
- Jupyter Notebook
- REST APIs
- VS Code
Below are some of the projects I’ve worked on. Feel free to check them out and let me know if you’d like to collaborate or contribute!
Predict house prices based on features like median income, house age, and average rooms using Linear Regression.
- Tech Used: Python, Scikit-learn, Pandas, Matplotlib
- Dataset: California Housing Dataset from
scikit-learn - Evaluation Metrics: RMSE, R² Score
Classify iris flowers into species (Setosa, Versicolor, Virginica) based on petal and sepal dimensions using Logistic Regression.
- Tech Used: Python, Scikit-learn, Pandas, Seaborn
- Dataset: Iris Dataset from
scikit-learn - Evaluation Metrics: Accuracy, Classification Report, Confusion Matrix
Group customers into clusters based on their annual income and spending score using K-Means Clustering.
- Tech Used: Python, Scikit-learn, Pandas, Matplotlib
- Dataset: Mall Customers Dataset (download from Kaggle)
- Visualization: Scatter plot of clusters
Predict whether a loan application will be approved based on features like income, credit score, and employment status using a Decision Tree Classifier.
- Tech Used: Python, Scikit-learn, Pandas, Matplotlib
- Dataset: Synthetic dataset
- Evaluation Metrics: Accuracy, Classification Report, Confusion Matrix
Classify movie reviews as positive or negative based on their text content using Logistic Regression.
- Tech Used: Python, Scikit-learn, Pandas, NLTK (TF-IDF Vectorization)
- Dataset: IMDB Movie Reviews Dataset (Stanford AI Lab)
- Evaluation Metrics: Accuracy, Classification Report, Confusion Matrix
Predict stock prices using historical stock price data and a Linear Regression model.
- Tech Used: Python, Scikit-learn, Pandas, yfinance
- Dataset: Historical stock price data from Yahoo Finance
- Evaluation Metrics: MAE, RMSE
Classify handwritten digits (0–9) using a simple Neural Network.
- Tech Used: Python, TensorFlow, Keras, Pandas, Matplotlib
- Dataset: MNIST Dataset
- Evaluation Metrics: Accuracy, Classification Report, Confusion Matrix
- Machine Learning: Building predictive models using regression, classification, clustering, and neural networks.
- Data Analysis: Cleaning, preprocessing, and analyzing datasets with Pandas and NumPy.
- Visualization: Creating insightful visualizations with Matplotlib and Seaborn.
- Problem Solving: Analytical thinker with a knack for debugging and optimizing code.
- Version Control: Proficient in Git and GitHub for collaboration and project management.
I’m always open to new opportunities, collaborations, and discussions. If you'd like to connect, feel free to reach out through the following channels:
- Email: [email protected]
- LinkedIn: linkedin.com/in/yourprofile)
- Portfolio: yourportfolio.com
- [Add achievements or milestones here, e.g., "Completed X projects," "Contributed to Y open-source projects"]
I’m currently diving into:
- Advanced Machine Learning techniques (e.g., CNNs, RNNs)
- Deploying ML models using Flask and FastAPI
- Exploring generative AI and large language models
Continuous learning is a core part of my journey, and I love sharing what I learn through blog posts and tutorials.
All my projects are open-source and available under the MIT License. Feel free to use, modify, and distribute them as you see fit!
“The best way to predict the future is to create it.” – Peter Drucker
Thank you for visiting my GitHub profile! I hope you found something interesting or useful here. Don’t hesitate to reach out—I’d love to hear from you! 😊
Let me know if you'd like to tweak anything further! 😊