Introduction

Lung cancer is caused by an error of the DNA in cells of the lungs, which can develop into a tumor and destroy systems in the body. To predict the potential for lung cancer, the data provide conditions that cause this disease, such as smoking, which is a well-known condition, and others. The code analyses the data to graph, so it makes it easier to see if any condition affects a lot and causes lung cancer.

1.Loading the data from Kaggle

KaggleHub

KaggleHub is a Python library that provides a simple and consistent way to access and utilize resources (like Models and Datasets) hosted on the Kaggle platform, both within Kaggle Notebooks and in external environments like your Google Colab.

Usage

import kagglehub
from kagglehub import KaggleDatasetAdapter

Setting the path to the correct file name

Using the variable file_path

file_path = "survey lung cancer.csv"

Using function from KaggleHub

uses the kagglehub.dataset_load function to load a dataset from Kaggle into a pandas DataFrame.

df = kagglehub.dataset_load(
  KaggleDatasetAdapter.PANDAS,
  "mysarahmadbhat/lung-cancer",
  file_path,
)

KaggleDatasetAdapter.PANDAS specifies that the dataset should be loaded into a pandas DataFrame.

"mysarahmadbhat/lung-cancer is the identifier for the specific dataset on Kaggle.

Printing the entire DataFrame

print the records by converting the pandas DataFrame into a string

print("records:", df.to_string())

2.Creating age groups and filters the data

os module

os module allows the Python code to interact with the underlying operating system

Usage

import os

Checks the existence of directory path

Using the variable dataset_directory

dataset_directory = '/kaggle/input/lung-cancer'
if os.path.exists(dataset_directory):
    print("Files in the dataset directory:", os.listdir(dataset_directory))
else:
    print(f"Directory not found: {dataset_directory}")

If the directory exists, print("Files in the dataset directory:", os.listdir(dataset_directory))

If the directory does not exist, print(f"Directory not found: {dataset_directory}")

3.Generating bar charts and pie chart

counting the number of patients in each condition first

import kagglehub
from kagglehub import KaggleDatasetAdapter
import pandas as pd

file_path = "survey lung cancer.csv"


df = kagglehub.dataset_load(
 KaggleDatasetAdapter.PANDAS,
 "mysarahmadbhat/lung-cancer",
 file_path,
)



df_lung_cancer_filtered = df[df['LUNG_CANCER'] == 'YES'].copy()


condition_cols = ['SMOKING', 'YELLOW_FINGERS', 'ANXIETY', 'PEER_PRESSURE', 'CHRONIC DISEASE', 'FATIGUE ', 'ALLERGY ', 'WHEEZING', 'ALCOHOL CONSUMING', 'COUGHING', 'SHORTNESS OF BREATH', 'SWALLOWING DIFFICULTY', 'CHEST PAIN']



condition_counts = {}
for condition in condition_cols:

   condition_count = df_lung_cancer_filtered[df_lung_cancer_filtered[condition] == 2].shape[0]
   condition_counts[condition] = condition_count


print("Number of lung cancer patients with each condition:")
for condition, count in condition_counts.items():
   print(f"{condition}: {count}")

Making a pie chart of the percentages of number of lung cancer patients with each condition

import matplotlib.pyplot as plt
import pandas as pd

condition_counts_series = pd.Series(condition_counts)


plt.figure(figsize=(10, 10))
plt.pie(condition_counts_series, labels=condition_counts_series.index, autopct='%1.1f%%', startangle=140)
plt.title('Proportion of Lung Cancer Patients with Each Condition')
plt.axis('equal')
plt.tight_layout()
plt.show()

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