Hey there! I'm Aditya Saw, a passionate Python developer with expertise in various libraries and tools. This repository is my playground where I explore and showcase my projects, experiments, and learnings in the world of Python programming and data science.
https://theaicontentdetector.streamlit.app Implemented TF-IDF Vectorizer & ExtraTreesClassifier for text classification. Achieved an accuracy score of 99.18% on a test set of 2800 data points. Confusion matrix showed 4 misclassifications for non-AI texts and 19 misclassifications for AI texts. Precision score of approximately 99.71% indicates high accuracy in AI text detection To identify AI-generated content, such as ChatGPT, GPT-4, and Google Gemini, copy and paste your English text below.
GitHub Repository Utilizing deep learning techniques, this project aims to classify brain tumors from MRI images for early detection and treatment planning. The dataset comprises 7023 MRI images categorized into glioma, meningioma, no tumor, and pituitary classes. A Convolutional Neural Network (CNN)-based model achieves high accuracy in classification, with precision, recall, and F1-score exceeding 0.96 for all classes. The model architecture includes convolutional and pooling layers followed by dense layers, totaling over 2 million trainable parameters, facilitating comprehensive tumor detection and location identification.
Developed a classifier using machine learning techniques to detect spam emails, helping users filter unwanted messages.
Implemented a next-word predictor using Long Short-Term Memory (LSTM) neural networks, providing suggestions based on preceding words in a sentence.
Created an artificial neural network (ANN) model to predict customer churn, aiding businesses in retaining valuable customers.
Developed an artificial neural network (ANN) model to predict graduate admission probabilities based on various factors, assisting prospective students in their application process.
Implemented linear regression algorithm from scratch using Python, demonstrating understanding of fundamental machine learning concepts.
- Django: A high-level Python web framework for rapid development and clean design.
- NumPy: Fundamental package for scientific computing with Python.
- Pandas: Data analysis and manipulation library.
- Matplotlib: Plotting library for creating static, animated, and interactive visualizations.
- Plotly: Interactive graphing library.
- Seaborn: Statistical data visualization library.
- Scikit-learn: Simple and efficient tools for data mining and data analysis.
- Keras with TensorFlow backend: High-level neural networks API.
- Power BI: Business analytics tool for interactive visualizations.
- Excel: Spreadsheet application for data analysis.
- Tableau: Data visualization software for creating interactive dashboards.
- AWS: Comprehensive cloud computing platform.
- Docker: Platform for developing, shipping, and running applications in containers.
- Git and GitHub: Version control and collaboration tools.
- SQL: Standard language for managing relational databases.
- Linear Regression
- Ridge Regression
- Lasso Regression
- K Nearest Neighbors (KNN)
- Principal Component Analysis (PCA)
- Logistic Regression
- Support Vector Machines (SVM)
- Decision Tree
- Random Forest
- Gradient Boosting
- XGBoost
- Artificial Neural Networks (ANN)
- Convolutional Neural Networks (CNN)
- Recurrent Neural Networks (RNN)
- Long Short-Term Memory (LSTM)
- Gated Recurrent Unit (GRU)
- Encoder-Decoder Models
- Transformer Models
Feel free to reach out to me through GitHub or connect with me on LinkedIn. I'm always open to collaborations and discussions!
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent