Current pipeline:

BGE-M3
→ Dense + Sparse embeddings
→ Qdrant
→ Hybrid retrieval / RRF
→ BGE Reranker
→ Relevance Gate
→ Context Quality Filter
→ Context Builder
→ Local LLM

The LLM is DeepSeek R1 7B running through Ollama.

The main idea is to make retrieval failures explicit.

If the reranker doesn't find sufficiently relevant chunks, the system can return a "not enough information" response instead of passing weak context to the model.

GitHub:

https://github.com/Taha2hussein/mini-Rag

I'm particularly interested in how others structure the boundary between:

retrieval → reranking → context preparation → generation

What does your production RAG pipeline look like?

Ever shipped a model with 95% CV accuracy, only to watch it crash in production?

The culprit: Data leakage in preprocessing. The imputation means and scaling stds were computed on the \\\*entire\\\* dataset before train/test split. Most AutoML tools do this silently.

What I built: A lightweight, leakage-safe ML experimentation framework on scikit-learn.

Why it matters:

\\- Split happens FIRST. All preprocessing lives inside the CV loop.

\\- Decision Engine reasons about your data before training.

\\- No brute-force. SVM skipped on large data. PR-AUC used for imbalance.

\*\* PyPI:\*\* https://pypi.org/project/ml-experiment-framework/0.1.0/

Deep dive: \[https://medium.com/@taha.hussein.two/i-built-a-leakage-safe-automl-framework-from-scratch-heres-why-it-matters-61d10d979a20\\\](https://medium.com/@taha.hussein.two/i-built-a-leakage-safe-automl-framework-from-scratch-heres-why-it-matters-61d10d979a20)

Questions:

1. Do you trust your AutoML CV scores?
2. How do you prevent leakage in preprocessing?

Feedback welcome ⭐

Ever shipped a model with 95% CV accuracy, only to watch it crash in production?

The culprit: Data leakage in preprocessing. The imputation means and scaling stds were computed on the \*entire\* dataset before train/test split. Most AutoML tools do this silently.

What I built: A lightweight, leakage-safe ML experimentation framework on scikit-learn.

Why it matters:

\- Split happens FIRST. All preprocessing lives inside the CV loop.

\- Decision Engine reasons about your data before training.

\- No brute-force. SVM skipped on large data. PR-AUC used for imbalance.

** PyPI:** https://pypi.org/project/ml-experiment-framework/0.1.0/

Deep dive: [https://medium.com/@taha.hussein.two/i-built-a-leakage-safe-automl-framework-from-scratch-heres-why-it-matters-61d10d979a20\](https://medium.com/@taha.hussein.two/i-built-a-leakage-safe-automl-framework-from-scratch-heres-why-it-matters-61d10d979a20)

Questions:

1. Do you trust your AutoML CV scores?
2. How do you prevent leakage in preprocessing?

Feedback welcome ⭐

Hey everyone! 👋

I just finished a small K-Means Clustering project on the Iris dataset 🌸🤖

I covered:

• 🔹 Data cleaning & visualization
• 🔹 Feature scaling
• 🔹 Elbow Method & Silhouette Score
• 🔹 K-Means clustering
• 🔹 ARI evaluation
• 🔹 Cluster & centroid visualization

I’m currently learning ML and would really appreciate some honest feedback 🙏

What would you improve? Any mistakes in my approach or things I should add?

🔗 Kaggle:
https://www.kaggle.com/code/tahahussein2020/irics-clustering

Hey everyone! 👋

I just finished a small K-Means Clustering project on the Iris dataset 🌸🤖

I covered:

• 🔹 Data cleaning & visualization
• 🔹 Feature scaling
• 🔹 Elbow Method & Silhouette Score
• 🔹 K-Means clustering
• 🔹 ARI evaluation
• 🔹 Cluster & centroid visualization

I’m currently learning ML and would really appreciate some honest feedback 🙏

What would you improve? Any mistakes in my approach or things I should add?

🔗 Kaggle:
https://www.kaggle.com/code/tahahussein2020/irics-clustering

🚀 I just finished my Twitter Sentiment Analysis project — I’d love your honest feedback!
Hey everyone! 👋
I’ve just finished building a Twitter Sentiment Analysis project using Python and Machine Learning.
The goal was to build a complete data analysis pipeline starting from raw Twitter data and going through:
🔹 Data loading & preprocessing
🔹 Data cleaning
🔹 Exploratory Data Analysis (EDA)
🔹 Text processing & vectorization
🔹 Correlation & statistical analysis
🔹 Data visualization
🔹 Machine Learning preparation
🔹 Modular Python project structure
I tried to structure the project like a real-world data/ML project rather than putting everything into a single notebook.
🔗 Kaggle:
https://www.kaggle.com/code/tahahussein2020/twitter-sentiment-analysis
I’m especially interested in feedback from people working with Data Science, Machine Learning, NLP, or Python.
What would you improve?
Is the project structure good?
Is the data preprocessing approach reasonable?
Are the visualizations useful?
Does this look like a good portfolio project?
Any mistakes or bad practices you notice?
Please be brutally honest 😄 — I’m trying to improve my ML skills and make this project as close to a professional real-world project as possible.
Thanks in advance! 🙏

Hey everyone! 👋

I’ve just finished building a Twitter Sentiment Analysis project using Python and Machine Learning.

The goal was to build a complete data analysis pipeline starting from raw Twitter data and going through:

🔹 Data loading & preprocessing
🔹 Data cleaning
🔹 Exploratory Data Analysis (EDA)
🔹 Text processing & vectorization
🔹 Correlation & statistical analysis
🔹 Data visualization
🔹 Machine Learning preparation
🔹 Modular Python project structure

I tried to structure the project like a real-world data/ML project rather than putting everything into a single notebook.

🔗 Kaggle:
https://www.kaggle.com/code/tahahussein2020/twitter-sentiment-analysis

I’m especially interested in feedback from people working with Data Science, Machine Learning, NLP, or Python.

What would you improve?

• Is the project structure good?
• Is the data preprocessing approach reasonable?
• Are the visualizations useful?
• Does this look like a good portfolio project?
• Any mistakes or bad practices you notice?

Please be brutally honest 😄 — I’m trying to improve my ML skills and make this project as close to a professional real-world project as possible.

Thanks in advance! 🙏

Hi everyone,

I recently completed my first Machine Learning project.

I implemented Linear Regression completely from scratch without using scikit-learn in order to understand the math behind the algorithm.

The notebook includes:

• Data exploration

• Data visualization

• Gradient Descent implementation

• Model evaluation (R², MAE, RMSE)

• Prediction visualization

I'd really appreciate any feedback on:

- Code quality

- Project structure

- Notebook organization

- Best practices

- Anything I can improve

Kaggle Notebook:

https://www.kaggle.com/code/tahahussein2020/salary-prediction-using-linear-regression-scratch

Thank you!