Welcome to ML-Journey, a collection of machine learning, data preprocessing, data visualization, and exploratory data analysis (EDA) notebooks and scripts. This repository documents a hands-on learning journey through various ML concepts, algorithms, and tools using Python.
This repository is designed to grow over time as more notebooks, datasets, and scripts are added. Below is a flexible structure that can be easily updated in the future:
Includes notebooks or scripts related to data cleaning, scaling, encoding, and transformation.
Contains notebooks focused on understanding data through visualization and descriptive statistics.
Holds ML model-building notebooks covering algorithms such as regression, classification, clustering, etc.
General-purpose Python utilities, math scripts, or helper functions.
All CSV or external datasets used across projects.
Notebooks demonstrating scraping, parsing, and automating data collection.
This repository explores concepts including:
- Data preprocessing (normalization, encoding)
- Exploratory data analysis
- Data visualization with Seaborn & Matplotlib
- Machine learning algorithms (Linear & Logistic Regression)
- Web scraping techniques
- Python scripting and math utilities (eigenvalues)
- Python
- NumPy
- Pandas
- Matplotlib
- Seaborn
- Scikit-learn
- Jupyter Notebook
Clone the repo:
git clone https://github.com/Jaidhuria/ML-journeyNavigate and open any notebook:
jupyter notebookRun each cell step-by-step to understand data workflows and ML model building.
This repository is intended as a personal learning space to practice:
- Data science techniques
- Machine learning workflow
- Visualization and interpretation
- Python coding fundamentals
If you're learning ML, feel free to explore, copy, or extend these notebooks.
While this is a learning repository, suggestions and improvements are welcome. Feel free to open issues or pull requests!
This project is open-source under the MIT License.
📌 Happy Learning and Experimenting!