Machine Learning
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Machine Learning Model Comparison: Supervised and Unsupervised Learning on the Titanic Dataset
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Introduction Machine Learning offers several approaches for solving prediction and pattern-discovery problems. Selecting the right algorithm is important because different algorithms can behave differently on the same dataset. In…
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Supervised Learning: Comparing 5 Machine Learning Algorithms on a Real Dataset
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A practical comparison of five supervised learning algorithms—Logistic Regression, Decision Tree, KNN, SVM, and Naive Bayes—trained on the same real-world dataset and evaluated using consistent preprocessing and classification metrics.
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What I Learned by Building Linear Regression from Scratch with NumPy
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A practical look at what I learned while building Linear Regression from scratch using NumPy, including data preparation, Gradient Descent, model evaluation, and comparison with Scikit-Learn during my internship…
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ML Fundamentals: Linear Regression from Scratch Using NumPy
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Learn ML Fundamentals by building a Linear Regression model from scratch using NumPy, Gradient Descent, MSE, and manual model evaluation.
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Data Cleaning: A Complete Beginner’s Guide
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Introduction Data cleaning is one of the most important steps in any data analysis or machine learning project. Real-world datasets are rarely perfect and often contain missing values, duplicate…