Nov 1, 2021

Breast Cancer Wisconsin Dataset - Classification

Description :

This dataset provides information about the features which are computed from a digitized image of a fine needle aspirate (FNA) of a breast mass. These features describe characteristics of the cell nuclei present in the image. In the 3-dimensional space is that described in: [K. P. Bennett and O. L. Mangasarian: "Robust Linear Programming Discrimination of Two Linearly Inseparable Sets", Optimization Methods and Software 1, 1992, 23-34].

Recommended Model :

Algorithms to be used: random forest, svm, logistic regression etc

Recommended Project :

Prediction of developing Breast Cancer

Dataset link:

https://www.kaggle.com/uciml/breast-cancer-wisconsin-data

Overview of data

Detailed overview of dataset:

- Rows = 569

- Columns= 31

1) ID number 2) Diagnosis (M = malignant, B = benign)

Ten real-valued features are computed for each cell nucleus: a) radius (mean of distances from center to points on the perimeter) b) texture (standard deviation of gray-scale values) c) perimeter d) area e) smoothness (local variation in radius lengths) f) compactness (perimeter^2 / area - 1.0) g) concavity (severity of concave portions of the contour) h) concave points (number of concave portions of the contour) i) symmetry j) fractal dimension ("coastline approximation" - 1)

EDA [CODE]

import pandas as pd  

# load data 
data = pd.read_csv('data.csv') 
data.head()

# check details of the dataframe 
data.info()

# check the no.of missing values in each column 
data.isna().sum()

# statistical information about the dataset 
data.describe()


# data distribution
import seaborn as sns
import matplotlib.pyplot as plt

sns.countplot(x='diagnosis', data=data) 
plt.show()

fig = sns.FacetGrid(data, col='diagnosis', hue='diagnosis', height=4)
fig.map(sns.histplot, 'radius_mean', bins=30, kde=False)
plt.show()

fig = sns.FacetGrid(data, col='diagnosis', hue='diagnosis', height=4)
fig.map(sns.histplot, 'radius_se', bins=30, kde=False)
plt.show()

fig = sns.FacetGrid(data, col='diagnosis', hue='diagnosis', height=4)
fig.map(sns.histplot, 'radius_worst', bins=30, kde=False)
plt.show()

fig = sns.FacetGrid(data, col='diagnosis', hue='diagnosis', height=4)
fig.map(sns.histplot, 'perimeter_mean', bins=30, kde=False)
plt.show()

fig = sns.FacetGrid(data, col='diagnosis', hue='diagnosis', height=4)
fig.map(sns.histplot, 'perimeter_se', bins=30, kde=False)
plt.show()

fig = sns.FacetGrid(data, col='diagnosis', hue='diagnosis', height=4)
fig.map(sns.histplot, 'perimeter_worst', bins=30, kde=False)
plt.show()

fig = sns.FacetGrid(data, col='diagnosis', hue='diagnosis', height=4)
fig.map(sns.histplot, 'texture_mean', bins=30, kde=False)
plt.show()

fig = sns.FacetGrid(data, col='diagnosis', hue='diagnosis', height=4)
fig.map(sns.histplot, 'texture_se', bins=30, kde=False)
plt.show()

fig = sns.FacetGrid(data, col='diagnosis', hue='diagnosis', height=4)
fig.map(sns.histplot, 'texture_worst', bins=30, kde=False)
plt.show()

fig = sns.FacetGrid(data, col='diagnosis', hue='diagnosis', height=4)
fig.map(sns.histplot, 'area_mean', bins=30, kde=False)
plt.show()

fig = sns.FacetGrid(data, col='diagnosis', hue='diagnosis', height=4)
fig.map(sns.histplot, 'area_se', bins=30, kde=False)
plt.show()

fig = sns.FacetGrid(data, col='diagnosis', hue='diagnosis', height=4)
fig.map(sns.histplot, 'area_worst', bins=30, kde=False)
plt.show()

fig = sns.FacetGrid(data, col='diagnosis', hue='diagnosis', height=4)
fig.map(sns.histplot, 'smoothness_mean', bins=30, kde=False)
plt.show()

fig = sns.FacetGrid(data, col='diagnosis', hue='diagnosis', height=4)
fig.map(sns.histplot, 'smoothness_se', bins=30, kde=False)
plt.show()

fig = sns.FacetGrid(data, col='diagnosis', hue='diagnosis', height=4)
fig.map(sns.histplot, 'smoothness_worst', bins=30, kde=False)
plt.show()

fig = sns.FacetGrid(data, col='diagnosis', hue='diagnosis', height=4)
fig.map(sns.histplot, 'compactness_mean', bins=30, kde=False)
plt.show()

fig = sns.FacetGrid(data, col='diagnosis', hue='diagnosis', height=4)
fig.map(sns.histplot, 'compactness_se', bins=30, kde=False)
plt.show()

fig = sns.FacetGrid(data, col='diagnosis', hue='diagnosis', height=4)
fig.map(sns.histplot, 'compactness_worst', bins=30, kde=False)
plt.show()

fig = sns.FacetGrid(data, col='diagnosis', hue='diagnosis', height=4)
fig.map(sns.histplot, 'concavity_mean', bins=30, kde=False)
plt.show()

fig = sns.FacetGrid(data, col='diagnosis', hue='diagnosis', height=4)
fig.map(sns.histplot, 'concavity_se', bins=30, kde=False)
plt.show()

fig = sns.FacetGrid(data, col='diagnosis', hue='diagnosis', height=4)
fig.map(sns.histplot, 'concavity_worst', bins=30, kde=False)
plt.show()

fig = sns.FacetGrid(data, col='diagnosis', hue='diagnosis', height=4)
fig.map(sns.histplot, 'symmetry_mean', bins=30, kde=False)
plt.show()

fig = sns.FacetGrid(data, col='diagnosis', hue='diagnosis', height=4)
fig.map(sns.histplot, 'symmetry_se', bins=30, kde=False)
plt.show()

fig = sns.FacetGrid(data, col='diagnosis', hue='diagnosis', height=4)
fig.map(sns.histplot, 'symmetry_worst', bins=30, kde=False)
plt.show()

fig = sns.FacetGrid(data, col='diagnosis', hue='diagnosis', height=4)
fig.map(sns.histplot, 'concave points_mean', bins=30, kde=False)
plt.show()

fig = sns.FacetGrid(data, col='diagnosis', hue='diagnosis', height=4)
fig.map(sns.histplot, 'concave points_se', bins=30, kde=False)
plt.show()

fig = sns.FacetGrid(data, col='diagnosis', hue='diagnosis', height=4)
fig.map(sns.histplot, 'concave points_worst', bins=30, kde=False)
plt.show()

fig = sns.FacetGrid(data, col='diagnosis', hue='diagnosis', height=4)
fig.map(sns.histplot, 'fractal_dimension_mean', bins=30, kde=False)
plt.show()

fig = sns.FacetGrid(data, col='diagnosis', hue='diagnosis', height=4)
fig.map(sns.histplot, 'fractal_dimension_se', bins=30, kde=False)
plt.show()

fig = sns.FacetGrid(data, col='diagnosis', hue='diagnosis', height=4)
fig.map(sns.histplot, 'fractal_dimension_worst', bins=30, kde=False)
plt.show()