A comprehensive collection of Machine Learning and Deep Learning implementations showcasing various algorithms, techniques, and preprocessing pipelines across multiple domains.
This repository serves as a practical showcase of AI/ML implementations, featuring diverse projects ranging from classical machine learning algorithms to advanced deep learning architectures. Each notebook demonstrates end-to-end workflows including data preprocessing, model training, evaluation, and insights.
| Notebook | Description | Algorithms |
|---|---|---|
BananaClass.ipynb |
Banana image classification using deep learning | CNN |
MilkClassn.ipynb |
Milk quality classification | Classification Models |
SeedPred.ipynb |
Seed type prediction and classification | ML Classifiers |
EmpDTKNN.ipynb |
Employee data classification | Decision Tree, KNN |
TreeBayesClassifier.ipynb |
Comparison of tree-based and probabilistic models | Decision Tree, Naive Bayes |
WeatherKNN.ipynb |
Weather pattern classification | K-Nearest Neighbors |
| Notebook | Description | Architecture |
|---|---|---|
CNNnetv2.ipynb |
Convolutional Neural Network implementation (v2) | CNN |
LSTMEnsemble.ipynb |
Time series analysis using ensemble LSTM | LSTM, Ensemble Methods |
PINN.ipynb |
Physics-Informed Neural Networks | PINN |
| Notebook | Description | Domain |
|---|---|---|
LoanBoost.ipynb |
Loan approval prediction using boosting algorithms | Finance |
MedicalPred.ipynb |
Medical diagnosis prediction | Healthcare |
HangoutOP.ipynb |
Optimization and prediction models | General |
| Notebook | Description | Techniques |
|---|---|---|
NLPTextPreprocessing.ipynb |
Comprehensive text preprocessing pipeline | Tokenization, Cleaning, Feature Engineering |
| Notebook | Description | Algorithm |
|---|---|---|
QLearningAgent.ipynb |
Q-Learning agent implementation | Q-Learning |
| Notebook | Description | Method |
|---|---|---|
TimeSeriestForest.ipynb |
Time series forecasting using Random Forest | Random Forest Regressor |
- Deep Learning: TensorFlow, Keras, PyTorch
- Machine Learning: Scikit-learn, XGBoost, LightGBM
- Data Processing: Pandas, NumPy
- Visualization: Matplotlib, Seaborn, Plotly
- NLP: NLTK, SpaCy
- Time Series: Statsmodels
Python 3.8+
Jupyter Notebook / JupyterLab- Clone the repository
git clone https://github.com/yourusername/PreProsesingAI.git
cd PreProsesingAI- Install required packages
pip install -r requirements.txt- Launch Jupyter Notebook
jupyter notebookEach notebook is self-contained and includes:
- Data Loading: Import and initial exploration
- Preprocessing: Data cleaning, feature engineering, and transformation
- Model Building: Algorithm implementation and training
- Evaluation: Performance metrics and visualization
- Insights: Analysis and conclusions
Simply open any notebook and run the cells sequentially.
For beginners, we recommend following this sequence:
- Start with Basics:
EmpDTKNN.ipynb→TreeBayesClassifier.ipynb - Advance to Boosting:
LoanBoost.ipynb - Explore Deep Learning:
CNNnetv2.ipynb→LSTMEnsemble.ipynb - Dive into NLP:
NLPTextPreprocessing.ipynb - Try Reinforcement Learning:
QLearningAgent.ipynb - Advanced Topics:
PINN.ipynb
- ✅ Production-Ready Code: Clean, documented, and reproducible
- ✅ Diverse Domains: Healthcare, Finance, Weather, Agriculture, and more
- ✅ Multiple Paradigms: Supervised, Unsupervised, and Reinforcement Learning
- ✅ Best Practices: Proper data splitting, cross-validation, and model evaluation
- ✅ Visualization: Comprehensive plots and charts for better understanding
- Physics-Informed Neural Networks (PINN): Cutting-edge approach combining physics laws with neural networks
- Ensemble LSTM: Advanced time series modeling with ensemble techniques
- Multi-Algorithm Comparison: Side-by-side evaluation of different approaches
This project is licensed under the MIT License - see the LICENSE file for details.
- Thanks to the open-source community for the amazing libraries
- Inspired by various Kaggle competitions and research papers
- Special thanks to contributors and collaborators
⭐ If you find this repository helpful, please consider giving it a star! ⭐
Last Updated: October 2025