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Applied Machine Learning & Data Science

Machine learning results plot
Jupyter Notebook screenshot
Data visualization chart

Project Description

This collection represents my practical work in data science, primarily executed within Jupyter Notebooks. These projects cover the full data science lifecycle: from acquiring and preprocessing raw data to exploratory data analysis (EDA), feature engineering, model training, and performance evaluation.

Key Skills & Methodologies

Exploratory Data Analysis (EDA)

Utilizing Pandas and Numpy to clean, transform, and analyze datasets. Using visualization tools like Matplotlib & Seaborn to uncover hidden patterns.

Feature Engineering & Preprocessing

Skillfully selecting, transforming, and creating new features from raw data to improve model accuracy and performance.

Model Training & Evaluation

Applying various Scikit-Learn models (e.g., Regression, Classification, Clustering) and evaluating their performance using appropriate metrics.