WQD7004 PROGRAMMING FOR DATA SCIENCE Session 2024/2025 Semester I Title: Analyzing Customer Spending Behaviour: The role of demographics, purchase type, and loyalty pattern in consumer

Detecting Online Payment Fraud Using Machine Learning Models Lecturer: Dr. Ang Group 9 | Matric | Full Name | |----------|----------------| | 23121328 | Mohammed Iqram | | 24052516 | LI JUNMING | | 22106713 | LI YUEXIN | | 23111676 | LIU YICONG | | 23108677 | ZHAO ZITONG |

Here are some useful R commands.

Saving to here as Im paranoid that I dont want to lose anything

Group Assignment for WQD7004

This homework used STL decomposition to extract the trend, seasonal, and residual components of the time series. The data was tidied with `mutate()` and visualized using `filter()`. The trend model was estimated using `TSLM()` and forecasts were generated with the `forecast()` function, which were then visualized with `autoplot()`. Basic forecasting models such as **MEAN(), NAIVE(),** and **RW()** were applied and visualized. Missing dates were handled by updating the time series index with `update_tsibble()`. Fitted values and residuals were calculated using `augment()` and visualized with `ggplot()` and ACF plots. Additionally, a **log() transformation** was applied, and seasonally adjusted series were generated for improved forecasting accuracy.

This homework used the **tidyverse** and **ggplot2** packages to tidy and manipulate data. The data was cleaned and transformed for analysis by converting it into tibble and tsibble formats. Time series data was extracted and visualized, with **seasonal data** being specifically plotted using `gg_season()` and `gg_subseries()`. The **autocorrelation function (ACF)** was also applied for deeper analysis. Daily data was updated into the **tsibble()** format to ensure correct indexing and consistency. The combined data was visualized using various plotting functions from **ggplot2**, enabling a comprehensive exploration of trends, seasonality, and autocorrelation in the time series.

Documento para ilustrar a optenção de probabilidades em ambientes multidecrementais.

Abstract: Program 1033, which transfers surplus military equipment to local police departments, has faced scrutiny amid ongoing concerns over police brutality and the presence of militarized police at protests. Some scholars worry that such militarization has a “chilling effect,” deterring not just rioting but also peaceful protests, calling into question the constitutionality of militarization within the context of the freedom of assembly. Still others have found that militarization actually increases the frequency of protests, suggesting that police militarization is ineffective at deterring violent protests. This study examines the impact of police militarization under Program 1033 on protest behavior, focusing on its role in deterring or inciting peaceful and violent protests. I use a time series panel regression on a state-by-state level from 2014 to 2023 to investigate this question. Even while accounting for lagged time effects and various social and economic variables, the data confirms that increased militarization has a statistically significant positive correlation with both peaceful and violent protests. This conclusion sheds light on the ineffectiveness of police militarization as it pertains to quelling violence, and it calls for policymakers to reassess the purpose and effectiveness of such policies on protecting communities.