> For the complete documentation index, see [llms.txt](https://workcupres.gitbook.io/data-science-projects/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://workcupres.gitbook.io/data-science-projects/master.md).

# 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

![](/files/-McP5h-K4rCv4po07Y-Q)

![](/files/-McP5iGiBeOE8qT5bhsc)

### 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

![](/files/-McP5mEbnfulnuPB6qN8)

### **2. Plotting of Biological Data on the World Map -------->  Density-based Mapping**&#x20;

```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

```

![](/files/-McP5f4m7W9a97f3ysjw)

```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
```

![](/files/-McP5du0B00Lh7fkx352)

```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
 
```

![](/files/-McP5aLtGFaeXBgz7R0l)

Disease mapping on the world map by countries&#x20;

(**Improved** version from the **above map**)

![](/files/-McP4QHjdzz9cR3nI_mC)

## Data Visualisation Gallery&#x20;

#### **`Data visualization outputs are included below.`**&#x20;

> Using **Power-BI**

![](/files/-McP5lAn-Mo5TwHvw46o)

> Using **Power-BI**

![](/files/-McP4VU-vR4jTNCxu6yJ)

### 1. Protein Database analysis&#x20;

Generated visualisations are given below:

> Created using **R programming** language&#x20;

![](/files/-McP4JUjlkiwnDuCzA03)

> Creating using **R programming** language&#x20;

![](/files/-McP4SJWbs2DOG5wbYPY)

> Created using **R programming** language

![](/files/-McP4QHjdzz9cR3nI_mC)

> Created using **Power-BI**

![](/files/-McP5p9Z8tXBd8cL4zmC)

> Created using **Power-BI**

![](/files/-McP5niSb5_bCI8015ae)

> Created using **Power-BI**

![](/files/-McP75FCWjno2uwBSb8p)

> Created using **Power-BI**

![](/files/-McP5qRx8VcO0nm1UJrz)


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