• Understand machine learning — Know what ML is, when to use it, and how it differs from writing rules by hand
• Work with data confidently — Handle arrays, DataFrames, and mixed data types including numerical, categorical, and ordinal
• Describe any dataset statistically — Calculate and interpret mean, median, mode, standard deviation, variance, and percentiles
• Visualise data like a pro — Build histograms, normal distributions, and scatter plots to spot patterns before training any model
• Build regression models — Fit and evaluate linear, polynomial, and multiple regression models using SciPy, NumPy, and scikit-learn
• Scale features for fair comparison — Apply standardisation so every input variable competes on equal terms inside your model
• Evaluate models honestly — Split data into training and testing sets, measure R-squared, and diagnose overfitting before it ships
• Build decision trees — Grow, read, and predict with classification trees that produce transparent, explainable decisions from data

I'm a web development enthusiast who loves building with React and NextJS, always looking for ways to optimize performance and create seamless experiences.

I'm also deeply interested in SystemDesign, constantly exploring how things scale and work behind the scenes. Technology excites me, and I enjoy learning and applying new tools and frameworks to solve real-world problems.

Beyond coding, I have a creative mindset, always working on side projects that bring ideas to life.