ML | Cancer cell classification using Scikit-learn Last Updated : 13 Apr, 2025 Comments Improve Suggest changes Like Article Like Report Machine learning is used in solving real-world problems including medical diagnostics. One such application is classifying cancer cells based on their features and determining whether they are 'malignant' or 'benign'. In this article, we will use Scikit-learn to build a classifier for cancer cell detection.Overview of the DatasetThe Breast Cancer Wisconsin (Diagnostic) dataset consists of:569 instances (tumor samples)30 attributes (features), including radius, texture, perimeter, and area of tumorsTwo classification labels:0 (Malignant) : Cancerous1 (Benign) : Non-cancerousWe will use these features to train and evaluate our machine learning model.Implementing Cancer cell classification in PythonBelow is the step-by-step implementation:1. Importing Necessary Modules and DatasetWe will use numpy, matplotlib and scikit learn for this. Python from sklearn.datasets import load_breast_cancer from sklearn.model_selection import train_test_split from sklearn.naive_bayes import GaussianNB from sklearn.metrics import accuracy_score import pandas as pd import matplotlib.pyplot as plt 2. Loading the Dataset into a VariableFor this project, we will use the Breast Cancer Wisconsin (Diagnostic) dataset which is available in Scikit-learn’s datasets module. We use the load_breast_cancer() function to load the dataset. Python data = load_breast_cancer() 3. Exploring the DatasetBefore training the model let's examine the dataset. This helps us understand how the data is structured and labeled. We will use pandas module to create a dataframe to simplify this process. We will use df.sample() function to fetch some random records from the data. Python df=pd.DataFrame(data.data,columns=data.feature_names) df.sample(5) Output:DatasetTo explore the data types of the columns in our dataset we will use the df.info() function. It will help us to understand the categorical and numerical columns in our dataset. Python df.info() Output:Dataset InfoTo investigate the numerical columns we will use the df.describe() function. This function provides key summary statistics such as the mean, standard deviation, minimum and maximum values for each numerical column. It helps us understand the distribution and scale of the data which is crucial for preprocessing and model performance. Python df.describe() Output:Described DatasetWe must also analyze data.target to understand the distribution of malignant and benign cases as class imbalance can affect model performance. Python df2=pd.DataFrame(data.target,columns=['target']) df2.sample(5) Output:Data DistributionPlotting an pie chart will help us understand the distribution of the target values. Python class_counts=df2["target"].value_counts() plt.pie(class_counts, labels=class_counts.index, autopct='%1.2f%%', colors=['red', 'green']) Output:Pie ChartUsually this type of dataset is considered imbalanced. A common threshold is when the minority class constitutes less than 30% of the total samples. However in this case its almost 38% which is acceptable. Incases of imbalances we can use techniques like oversampling, undersampling or class weighting.4. Splitting the Data into Training and Testing SetsTo evaluate our classifier we split the dataset into training and test sets using train_test_split(). Here 33% of the data is used for testing while the remaining 67% is used for training. Python X_train, X_test, y_train, y_test = train_test_split(data.data, data.target, test_size=0.33, random_state=42) 5. Building and Training the ModelWe use Naive Bayes algorithm which is effective for binary classification tasks. The fit() function trains the model on the training dataset. Python model = GaussianNB() model.fit(X_train, y_train) Output:Model Training6. Making PredictionsNow we use our trained model to predict the classification of cancer cells in the test set. The output is an array of 0s and 1s representing predicted tumor classifications. Python y_pred = model.predict(X_test) print(y_pred[:10]) Output:[1, 0, 0, 1, 1, 0, 0, 0, 1, 1]7. Evaluating Model AccuracyTo measure how well our model performs we will compare its predictions with the actual labels to calculate its accuracy. We will use accuracy_score from the sklearn.metrics library. Python accuracy = accuracy_score(y_test, y_pred) print(f"Model Accuracy: {accuracy * 100:.2f}%") Output:Model Accuracy: 94.15%This means our Naive Bayes classifier is 94.15% accurate in predicting whether a tumor is malignant or benign meaning our model is working fine and can be used for medical diagnostics. Comment More infoAdvertise with us Next Article ML | Kaggle Breast Cancer Wisconsin Diagnosis using KNN and Cross Validation R Rahul_Roy Follow Improve Article Tags : Technical Scripter Machine Learning AI-ML-DS Python scikit-module Machine Learning Projects AI-ML-DS With Python +2 More Practice Tags : Machine Learning Similar Reads 100+ Machine Learning Projects with Source Code [2025] This article provides over 100 Machine Learning projects and ideas to provide hands-on experience for both beginners and professionals. Whether you're a student enhancing your resume or a professional advancing your career these projects offer practical insights into the world of Machine Learning an 5 min read Classification ProjectsWine Quality Prediction - Machine LearningHere we will predict the quality of wine on the basis of given features. We use the wine quality dataset available on Internet for free. 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