# Data Science Projects ## Data Science Projects ### 1. Predicting Stock Performance with Machine Learning *Project type: Group Project **University of Liverpool** * Using **Python** language and **Packages** **Aim** of the project: Maximize the return of shareholders by predicting the price of bitcoin using machine and deep learning techniques ![](https://2672152490-files.gitbook.io/~/files/v0/b/gitbook-legacy-files/o/assets%2F-McP3ju8T9EY2fcYeMRX%2F-McP56sHyIy1DH74Foti%2F-McP5h-K4rCv4po07Y-Q%2FMicrosoftTeams-image%20\(4\).png?alt=media\&token=5ccf1312-1c56-4b6f-9f7d-7d4a8f24e29f) ![](https://2672152490-files.gitbook.io/~/files/v0/b/gitbook-legacy-files/o/assets%2F-McP3ju8T9EY2fcYeMRX%2F-McP56sHyIy1DH74Foti%2F-McP5iGiBeOE8qT5bhsc%2FETH%20tRAIDING.png?alt=media\&token=e40a37dc-afbd-4615-97b0-2de11c758c38) ### 2. **Working on Credit Card Fraud Prediction using Machine Learning and Artificial Intelligence** #### *Currently working on this***..............................................................>** ## BIG-Biological Data Projects ### **1.** Rheumatoid Arthritis---Data Analysis using GWAScat > Using **R** language ```r #For GWAS analysis we need BiocManager package to be installed install.packages("BiocManager") ``` ```r BiocManager::install("ggbio") BiocManager::install("gwascat") BiocManager::install("Homo.sapiens") ``` ```r library(gwascat) objects("package:gwascat") data(ebicat38) topTraits(ebicat38) subsetByTraits(ebicat38, tr="Rheumatoid arthritis")[1:294] df_main <- data.frame(subsetByTraits(ebicat38, tr="Rheumatoid arthritis")[1:294]) getwd() write.csv(df_main,"Gwas_RA_ALL.csv", row.names = FALSE) ``` ```r #Basic Manhattan plot gwtrunc = ebicat38 requireNamespace("S4Vectors") mcols = S4Vectors::mcols mlpv = mcols(ebicat38)$PVALUE_MLOG mlpv = ifelse(mlpv > 1000, 1000, mlpv) S4Vectors::mcols(gwtrunc)$PVALUE_MLOG = mlpv library(GenomeInfoDb) seqlevelsStyle(gwtrunc) = "UCSC" gwlit = gwtrunc[ which(as.character(seqnames(gwtrunc)) %in% c("chr1")) ] library(ggbio) mlpv = mcols(gwlit)$PVALUE_MLOG mlpv = ifelse(mlpv > 550, 550, mlpv) S4Vectors::mcols(gwlit)$PVALUE_MLOG = mlpv methods:::cbind2(FALSE) autoplot(gwlit, geom="point", aes(y=PVALUE_MLOG), xlab="chr1" ) ``` After running this code you get the plot shown below ![](https://2672152490-files.gitbook.io/~/files/v0/b/gitbook-legacy-files/o/assets%2F-McP3ju8T9EY2fcYeMRX%2F-McP56sHyIy1DH74Foti%2F-McP5mEbnfulnuPB6qN8%2Fchr1%20\(1\).jpg?alt=media\&token=f328caf7-0aa8-4fc1-9bee-773611abd3e8) ### **2. Plotting of Biological Data on the World Map --------> Density-based Mapping** ```r #First install Maps package install.packages("maps") ``` ```r library(maps) library(ggplot2) ``` ```r world_map <- map_data("world") p <- ggplot() + coord_fixed() + xlab("") + ylab("") ``` ```r #Add map to base plot base_world_messy <- p + geom_polygon(data=world_map, aes(x=long, y=lat, group=group), colour="darkslategrey", fill="white") base_world_messy ``` ![](https://2672152490-files.gitbook.io/~/files/v0/b/gitbook-legacy-files/o/assets%2F-McP3ju8T9EY2fcYeMRX%2F-McP56sHyIy1DH74Foti%2F-McP5f4m7W9a97f3ysjw%2FMAPO.png?alt=media\&token=aff1b10e-14d4-4dc7-863a-6c3ef7a8d8a0) ```r #Strip the map down so it looks super clean (and beautiful!) cleanup <- theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(), panel.background = element_rect(fill = 'darkslategrey', colour = 'darkslategrey'), axis.line = element_line(colour = "white"), legend.position="none", axis.ticks=element_blank(), axis.text.x=element_blank(), axis.text.y=element_blank()) base_world <- base_world_messy + cleanup base_world ``` ![](https://2672152490-files.gitbook.io/~/files/v0/b/gitbook-legacy-files/o/assets%2F-McP3ju8T9EY2fcYeMRX%2F-McP56sHyIy1DH74Foti%2F-McP5du0B00Lh7fkx352%2FMAPOO.png?alt=media\&token=3b563f24-359f-420b-8a7c-908d635460dc) ```r # read file with cordinate data df <- read.csv("C:/Users/workc/OneDrive/Desktop/RA_map_cord.csv") ``` ```r map_data_sized <- base_world + geom_point(data=df, aes(x=long, y=lat, size=value), colour="Black", fill="Deep Pink",pch=21, alpha=I(1)) map_data_sized ``` ![](https://2672152490-files.gitbook.io/~/files/v0/b/gitbook-legacy-files/o/assets%2F-McP3ju8T9EY2fcYeMRX%2F-McP56sHyIy1DH74Foti%2F-McP5aLtGFaeXBgz7R0l%2FMAPOOO.png?alt=media\&token=93f47137-0dea-4cc3-91af-37dc66e9f2ad) Disease mapping on the world map by countries (**Improved** version from the **above map**) ![](https://2672152490-files.gitbook.io/~/files/v0/b/gitbook-legacy-files/o/assets%2F-McP3ju8T9EY2fcYeMRX%2F-McP3pEpV5z4dEbkbxRt%2F-McP4QHjdzz9cR3nI_mC%2FWmap_RA_GB.png?alt=media\&token=2fc43e9a-75e2-4c94-8dbe-c2c2801e5c14) ## Data Visualisation Gallery #### **`Data visualization outputs are included below.`** > Using **Power-BI** ![](https://2672152490-files.gitbook.io/~/files/v0/b/gitbook-legacy-files/o/assets%2F-McP3ju8T9EY2fcYeMRX%2F-McP56sHyIy1DH74Foti%2F-McP5lAn-Mo5TwHvw46o%2FCVD_DIST.svg?alt=media\&token=9c5d476a-e2ff-4746-b0a3-0075b74e97e8) > Using **Power-BI** ![](https://2672152490-files.gitbook.io/~/files/v0/b/gitbook-legacy-files/o/assets%2F-McP3ju8T9EY2fcYeMRX%2F-McP3pEpV5z4dEbkbxRt%2F-McP4VU-vR4jTNCxu6yJ%2Fg11626.png?alt=media\&token=6d070c1a-0ea6-4cbf-a8a9-b1ee4280f786) ### 1. Protein Database analysis Generated visualisations are given below: > Created using **R programming** language ![](https://2672152490-files.gitbook.io/~/files/v0/b/gitbook-legacy-files/o/assets%2F-McP3ju8T9EY2fcYeMRX%2F-McP3pEpV5z4dEbkbxRt%2F-McP4JUjlkiwnDuCzA03%2FFUll_Final.png?alt=media\&token=cad5aad5-a7ce-483d-b002-31f1f01c2120) > Creating using **R programming** language ![](https://2672152490-files.gitbook.io/~/files/v0/b/gitbook-legacy-files/o/assets%2F-McP3ju8T9EY2fcYeMRX%2F-McP3pEpV5z4dEbkbxRt%2F-McP4SJWbs2DOG5wbYPY%2FHD_main_Final.png?alt=media\&token=6e62392f-09e3-46fe-b23c-b9275de47b50) > Created using **R programming** language ![](https://2672152490-files.gitbook.io/~/files/v0/b/gitbook-legacy-files/o/assets%2F-McP3ju8T9EY2fcYeMRX%2F-McP3pEpV5z4dEbkbxRt%2F-McP4QHjdzz9cR3nI_mC%2FWmap_RA_GB.png?alt=media\&token=2fc43e9a-75e2-4c94-8dbe-c2c2801e5c14) > Created using **Power-BI** ![](https://2672152490-files.gitbook.io/~/files/v0/b/gitbook-legacy-files/o/assets%2F-McP3ju8T9EY2fcYeMRX%2F-McP56sHyIy1DH74Foti%2F-McP5p9Z8tXBd8cL4zmC%2F100_400-1.jpg?alt=media\&token=63127da1-af40-44c3-8b86-8231778ab4d3) > Created using **Power-BI** ![](https://2672152490-files.gitbook.io/~/files/v0/b/gitbook-legacy-files/o/assets%2F-McP3ju8T9EY2fcYeMRX%2F-McP56sHyIy1DH74Foti%2F-McP5niSb5_bCI8015ae%2F400_1020-1.jpg?alt=media\&token=2b2544e1-eedb-4430-8fff-dddb3ab1bd19) > Created using **Power-BI** ![](https://2672152490-files.gitbook.io/~/files/v0/b/gitbook-legacy-files/o/assets%2F-McP3ju8T9EY2fcYeMRX%2F-McP5rvzkdQYHeC8uAPT%2F-McP75FCWjno2uwBSb8p%2F10_30-1.jpg?alt=media\&token=3ef74ca9-3e33-4a0e-891b-f4bd3a507091) > Created using **Power-BI** ![](https://2672152490-files.gitbook.io/~/files/v0/b/gitbook-legacy-files/o/assets%2F-McP3ju8T9EY2fcYeMRX%2F-McP56sHyIy1DH74Foti%2F-McP5qRx8VcO0nm1UJrz%2F1_10-1.jpg?alt=media\&token=48a65933-bc39-4a63-84b4-15499a83841b)