train_data project

How does a bike-share navigate speedy success?"

El modelo de análisis de varianza y El modelo de análisis de covarianza

Histogram and Bar Plots.BANL6310

Lesson plan for Machine Learning using the Julia programming language * Introduction to machine learning concepts * Supervised and unsupervised learning * Implementing machine learning algorithms in Julia * Cross-validation and model selection

Lesson plan for Generalized Linear Models using the Julia programming language Logistic regression and probit models Poisson regression for count data Model fitting and interpretation Evaluating model performance

Hypothesis Testing using Bootstrap, Resampling, and Confidence Intervals

Entrega de la actividad 1

Time Series Component Analysis Decomposing the time series yields the following components: Trend: The trend component is an indication of the gradual increase in the sales of used cars over time, showing a long-term upward movement in the data. Seasonality: There is a distinct repeating seasonal pattern, suggesting that used car sales fluctuate consistently each year. Cyclic Component: There are no marked cyclic variations apart from the seasonal ones; therefore, economic cycles seem not to be a contributing factor in this data set. Residual: The residual component includes random fluctuations that cannot be described by either trend or seasonality and, therefore, represents unpredictable variations in sales data. However, from the Augmented Dickey-Fuller test result, this series is stationary since the p-value falls below the threshold 0.05 of 0.01, meaning there is evidence that this is not a series with a unit root; thus, no differencing would be required in order to apply some time series models.