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!