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Case Studies and Projects in Machine Learning/EDA/DL
about statistical techniques for Data Science
Statistics in Python: Jupyter Notebook Collection. You can find the details on Statistics and application to the machine learning algorithm at following link:
Having completed this project notebook, you will have hands-on experience: sampling and calculating probabilities from a normal distribution; the correct way to estimate the standard deviation of a population (the population parameter) from a sample; what a sampling distribution is and how the Central Limit Theorem applies; how to calculate critical values and confidence intervals; performing inference using such data to answer business questions, forming a hypthesis and framing the null and alternative hypothesis, and testing thsi using a t-test.
Extracted statistical relationships between house prices and many factors, applicationised the 90% R2 Random Forest model that outcompeted MLR, Lasso, PLS, KNN, and DT into production.
Aplicación Shiny para realizar inferencias estadísticas sobre medias, proporciones y varianzas. Creada originalmente por Antoine Soetewey.