Machine Learning Algorithms
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Updated
Apr 26, 2019 - Jupyter Notebook
Machine Learning Algorithms
Some scripts for dataset exploration, normalization and classification using Python, PyBrain, SciPy
This repository contains the descriptive statistics notebook.
Conducting simple linear regression on the penguins dataset
King County Real Estate Model
Age-Gender-Country-Specific Death Rates Modelling and Forecasting: A Linear Mixed-Effects Model
Generalized Additive Forecasting Mortality
Statistics on App usage data
Predicting wage in the uswage dataset (Linear Regression). Model Selection, Model Diagnostics etc.
A collection of useful implementations to perform EDA on a new dataset in order to understand preliminary patterns in the dataset and gain a high-level grasp of the dataset using plots and visualizations.
Web app for checking if a series of values is normally distributed.
Useful graphs for financial projects
Biostatistical analysis #R
RStudio project utilizing various statistical methods to replicate and diagnose the findings of Appel and Loyle from their study on post-conflict justice and foreign direct investment.
This repository is a resource for learning and applying statistics in data science. It contains code examples and explanations for many common statistical concepts, from descriptive statistics through regression and time series analysis.
Residual analysis in Linear regression is based on examination of graphical plots which are as follows :: 1. Residual plot against independent variable (x). 2. Residual plot against independent variable()y. 3. Standardize or studentized residual plot 4. Normal probability plot
Forecast the Airline Flight Demand Using ARIMA and AR
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