Mathew K Analytics
Welcome to this Youtube channel! I am a data scientist with a passion for helping others learn and grow in their careers. On this channel, you can expect to find a range of tutorials, resources, and insights related to the field of Data Science. Whether you are just starting out in the field or are an experienced professional looking to deepen your understanding, my content is designed to help you learn and succeed.
I have extensive expertise in areas such as Machine Learning, Predictive Analytics, Unsupervised Learning, Customer Analytics, Social Media Data Analytics, Business Intelligence and Analytics, Database Management, Statistical Programming using R, SAS, and Python, Data Visualization, and Market Research Analytics. I am always looking for new and innovative ways to apply my knowledge and skills to real-world problems, and I love sharing my insights and experience with others. Thank you for stopping by and I hope you find my content helpful and informative.
Capstone Sentiment Analysis Using Text Mining: A Real-World Data Science Project
Credit Scoring in Python Building a Complete Scorecard
MovieLens Data Analytics & Recommender Systems
1 - Fake News Detection with NLP and Transformers
4 - Insurance Claim Severity Prediction - Regression
3 - Healthcare Diabetes Prediction - Binary Classification
5 - House Price Prediction - Regression with Ames Dataset
4 - Loan Default Prediction - Risk Modeling
1 - Customer Churn Prediction - Classification Models
2 - Retail Sales Dashboard with Plotly and Streamlit
3 - COVID-19 Data Tracker using API and Time-Series Visualization
1 - Titanic Survival Analysis - EDA and ML Basics
5 Capstone Project: Comprehensive Data Mining Case Study Walkthrough
1 Customer Churn Prediction End-to-End Capstone Project
5. Can You Predict Stock Prices With Python?
3 What is EXPONENTIAL Smoothing in Time Series Forecasting?
2. How to Catch Outliers in Your Data Fast!
1 Introduction to Anomaly and Outlier Detection
6 Hands-on Project: Predicting Diabetes with Ensemble Models
4 Support Vector Machines (SVM): Fundamentals and Applications
3 Boosting Techniques: AdaBoost and XGBoost Explained
2 Bagging and Random Forests: Ensemble Methods for Advanced Machine Learning
6 Hands-on Clustering Project: Socio-Economic Segmentation Explained
5 Cluster Evaluation Metrics and Interpretation
4 Density-Based Clustering with DBSCAN
3 Hierarchical Clustering Techniques: Concepts and Examples
2 k-Means Clustering Algorithm Explained: Step-by-Step Guide
1 Introduction to Clustering in Machine Learning
6 ROC Curves, AUC, and Confusion Matrix Analysis for Classification Models
5 Evaluating Classifiers: Accuracy, Precision, Recall, and F1-Score Explained