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https://github.com/kivanc57/feature_comparison

This project explores the relationship between features and diagnosis in cancer data. Using methods like boxplots, scatterplots, PCA, k-means clustering, and logistic regression, we analyze and visualize data to understand health indicators.
https://github.com/kivanc57/feature_comparison

boxplot clustering correlation data-science data-visualization descriptive-statistics explanatory-data-analysis pearson-correlation r scatter-plot spearman

Last synced: 1 day ago
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Host: GitHub
URL: https://github.com/kivanc57/feature_comparison
Owner: kivanc57
License: gpl-3.0
Created: 2023-07-12T18:08:21.000Z (over 1 year ago)
Default Branch: main
Last Pushed: 2024-09-13T12:27:13.000Z (2 months ago)
Last Synced: 2024-11-16T19:16:26.553Z (1 day ago)
Topics: boxplot, clustering, correlation, data-science, data-visualization, descriptive-statistics, explanatory-data-analysis, pearson-correlation, r, scatter-plot, spearman
Language: R
Homepage:
Size: 2.19 MB
Stars: 0
Watchers: 1
Forks: 0
Open Issues: 0
Metadata Files:
- Readme: README.md
- License: LICENSE

Awesome Lists containing this project

README

        # Cancer Diagnosis Analysis

## Aim and Dataset 🚀

The objective of this project is to examine the relationship between various features in a dataset, focusing on the impact of the diagnosis variable, which consists of Malignant (M) and Benign (B) categories. The study employs Exploratory Data Analysis (EDA) and Descriptive Statistics to clarify how measurement results relate to health conditions. The analysis includes boxplots, scatterplots, correlation charts, k-means clustering, and logistic regression.

**Dataset Details:**

- **Format:** CSV

- **Rows:** 569

- **Columns:** 32 (excluding the "X" column with `NaN` values)

### Key Steps:

📱 **Data Preparation** 📱

   - Load and preprocess the dataset.

   - Remove unnecessary columns and scale numerical data.

🌟 **Descriptive Statistics** 🌟

   - Generate summary statistics for the features.

⚡ **Visualization** ⚡

   - **Boxplots:** Display the distribution and outliers of features.

   - **Scatterplots:** Illustrate the relationship between features like `radius_mean` and `perimeter_mean`.

🚨 **Correlation Analysis** 🚨

   - **Pearson’s and Spearman’s Correlation:** Analyze correlations and their significance between features.

🔥 **Principal Component Analysis (PCA)** 🔥

   - Perform PCA to reduce dimensionality and visualize feature relationships.

🌱 **Clustering** 🌱

   - **K-Means Clustering:** Determine optimal cluster numbers and visualize clusters.

🔔 **Logistic Regression** 🔔

   - Compare features like `compactness_mean` and `radius_se` with the diagnosis variable to analyze their relationship.

## R Script

```R

# Load required libraries

library(tidyverse)

library(cluster)

library(Hmisc)

library(plotly)

library(ggfortify)

library(factoextra)

library(NbClust)

library(ggpubr)

library(dplyr)

library(PerformanceAnalytics)

library(ggplot2)

# Load and prepare data

data <- read.csv("/Users/admin/Desktop/Workplace/Data/cancer.csv", sep=",", row.names=1, stringsAsFactors = T)

data_rest <- data[, -32]

numerical_data <- data_rest[, -1]

numerical_data_scaled <- scale(numerical_data)

# Export data for Excel

export_data <- data[,1] %>% mutate_if(is.numeric, round, digits=3)

export_data$X <- data$X

export_data$diagnosis <- data$diagnosis

write.table(export_data, file='/Users/admin/Desktop/data.txt', sep=',', row.names = T)

# Descriptive statistics summary

summary_data <- round(apply(numerical_data, 2, summary),3)

clip <- pipe("pbcopy", "w")                       

write.table(summary_data, file=clip, sep = '\t', row.names = FALSE)                               

close(clip)

# Boxplot visualization

png("/Users/admin/Desktop/boxplot.png", width = 20, height = 10, units = 'in', res = 300)

boxplot(scale(numerical_data), col = rainbow(ncol(numerical_data)), notch = TRUE, xlab = "Features", ylab = "Values")

dev.off()

# Scatter plot visualization

png("/Users/admin/Desktop/scatter_plot.png", width = 4, height = 4, units = 'in', res = 300)

ggplot(data = data_rest, mapping = aes(x = data_rest$radius_mean, y = data_rest$perimeter_mean)) +

  geom_point(mapping = aes(color = diagnosis)) +

  labs(x = "radius_mean", y = "perimeter_mean")

dev.off()

# Correlation analysis

chart.Correlation(numerical_data, histogram=TRUE, pch="+", method = "pearson")

chart.Correlation(numerical_data, histogram=TRUE, pch="+", method = "spearman")

corr_matrix <- as.matrix(round(cor(numerical_data)), 2)

corr_matrix[corr_matrix< 0.05]=NA

# PCA

PCA_result <- prcomp(numerical_data, center = TRUE, scale. = TRUE)

summary(PCA_result)

PCA_plot <- autoplot(PCA_result, data = data_rest, colour = 'diagnosis', label.size = 3, shape = FALSE,

              loadings = TRUE, loadings.colour = 'blue',

              loadings.label = TRUE, loadings.label.size = 2)

ggplotly(PCA_plot)

# K-Means Clustering

set.seed(1)

NbClust(numerical_data, distance = "euclidean", min.nc = 2, max.nc = 10, method = "kmeans")

km.res <- kmeans(numerical_data_scaled, 2)

fviz_cluster(km.res, numerical_data, ellipse.type = "norm", repel = TRUE)

ggsave("kmeans_graph.png")

pam.res <- pam(numerical_data_scaled, 2)

fviz_cluster(pam.res, geom = "point", ellipse.type = "norm")

```

## Screenshots 🚗

### k-Mean Graph

![k-Mean](/screenshots/kmean.png?raw=true)

---

### Boxplot Graph of Each Feature

![Boxplot](/screenshots/boxplot.png?raw=true)

---

### Correlation of Graph Each Feature

![Correlation](/screenshots/correlation.png?raw=true)

---

### Logistic Regression Graph of compactness_mean and diagnosis columns

![Regression of compactness_mean/diagnosis](/screenshots/regression1.png?raw=true)

---

### Logistic Regression Graph of radius_se and diagnosis columns

![Regression of radius_se/diagnosis ](/screenshots/regression2.png?raw=true)

## Conclusions 📣

Through exploratory data analysis and statistical modeling, this project reveals significant insights into the relationships between features and their impact on the diagnosis variable. The use of correlation analysis, PCA, and clustering methods provides a comprehensive understanding of the data and highlights key patterns and relationships.

* More comprehensive report is avalable in the project folder within `report.docx`.

## Dataset 📰

* The original dataset that is used in this project is called 'Breast Cancer Wisconsin (Diagnostic) Data Set' and it can be accessed [here](https://www.kaggle.com/datasets/uciml/breast-cancer-wisconsin-data?resource=download). 

## License 📍

This project is licensed under the GNU General Public License v3.0 (GPL-3.0) - see the [LICENSE](LICENSE) file for details.

## Contact 📩

Let me know if there are any specific details you’d like to adjust or additional sections you want to include!  

* **Email**: [email protected]

* **Version**: 1.0.0

* **Date**: 23-06-2024