This repository showcases my hands-on work with datasets sourced from Kaggle. The primary focus is on building a strong end-to-end machine learning workflow — from raw data to well-performing models.
Across different projects in this repo, I work with a variety of datasets and problem types (classification, regression, etc.). For each dataset, the approach is structured and practical:
- Handling missing values
- Feature encoding (categorical → numerical)
- Feature scaling and normalization
- Outlier detection and treatment
- Data cleaning and transformation
- Understanding distributions and relationships
- Visualizing data using plots and graphs
- Identifying patterns and key insights
- Choosing appropriate models based on data type and problem
- Experimenting with multiple algorithms
- Comparing performance using evaluation metrics
- Avoiding overfitting through proper validation techniques
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Python
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Jupyter Notebook
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Libraries:
pandasfor data manipulationnumpyfor numerical computationsmatplotlibfor visualizationscikit-learnfor machine learning models and preprocessing- (and other supporting libraries as needed)
The goal of this repository is not just to build models, but to understand how different datasets require different approaches — especially in preprocessing and model selection. Each project reflects practical decision-making rather than a one-size-fits-all pipeline.
- Each notebook is self-contained and explains the workflow step by step.
- Emphasis is placed on clarity, reproducibility, and learning.
- This repository is continuously updated as I explore more datasets and techniques.
Feel free to explore, learn, or suggest improvements!