Data Science
-
How to perform Feature Selection in Machine Learning – Comparing SHAP, Feature Importance and RFECV
.
Feature Selection in Machine Learning: Comparing SHAP, Feature Importance and RFECV Feature selection is an important step in machine learning because having more features does not automatically mean having…
-
Mastering Exploratory Data Analysis (EDA) with Python and Pandas: An E-Commerce Case Study
.
Data in its raw form is rarely ready for decision-making. It arrives messy, incomplete, and full of hidden anomalies. Before building predictive machine learning models or deploying dashboards, data…
-
How to Compare Machine Learning Models – Final Report
.
Introduction Machine learning provides a wide range of algorithms that can be used to solve prediction and classification problems. However, choosing the right model is an important part of…
-
Finding The Best Supervised and Unsupervised Machine Learning Models
.
Machine learning models involves choosing the right approach for a particular problem and evaluating whether the selected model performs reliably. As part of my Data Science internship at Valentius…
-
What is Supervised Machine Learning?
.
Machine learning is one of the most important areas of modern artificial intelligence (AI). It allows computers to learn patterns from data and make predictions or decisions without being…
-
What is Machine learning? Regression from scratch
.
Machine learning (ML) is a branch of artificial intelligence that enables computers to learn from data, identify patterns, and make predictions without being explicitly programmed. By utilizing algorithms rooted…
-
Comparing Ensemble Learning Benchmarks: Random Forest vs Gradient Boosting vs XGBoost Model/Algorithms
.
Random Forest primarily reduces variance by training multiple decision trees independently on different bootstrap samples and combining their predictions. This makes the model more stable and less sensitive to…
-
Machine Learning Model Comparison: Supervised and Unsupervised Learning on the Titanic Dataset
.
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…
-
What I Learned by Building Linear Regression from Scratch with NumPy
.
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…
-
ML Fundamentals: Linear Regression from Scratch Using NumPy
.
Learn ML Fundamentals by building a Linear Regression model from scratch using NumPy, Gradient Descent, MSE, and manual model evaluation.