🧠 Tumor Detection using Machine Learning

This project aims to classify tumors as Malignant (M) or Benign (B) based on their characteristics using a supervised machine learning approach. The dataset contains diagnostic measurements of breast cancer tumors, and the goal is to build an accurate and interpretable classification model.

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📁 Dataset

The dataset used is Tumor_Detection.csv, which includes features such as:

• Radius, Texture, Perimeter, Area, Smoothness, etc.
• Each feature has mean, worst, and standard error statistics.
• The target variable is diagnosis:
  • M = Malignant
  • B = Benign

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📌 Project Steps

1. Data Cleaning

   • Removed irrelevant columns like id and unnamed fields.
   • Checked and confirmed absence of missing values.

2. Exploratory Data Analysis (EDA)

   • Visualized class distribution of tumor types.
   • Created a correlation heatmap to explore relationships between features.

3. Preprocessing

   • Converted categorical diagnosis labels (M/B) into numerical format (1/0).
   • Standardized feature values using StandardScaler.

4. Model Building

   • Used a Random Forest Classifier to train the model.
   • Split data into 80% training and 20% testing.

5. Evaluation

   • Achieved an accuracy of 96.49% on the test set.
   • Visualized performance with a confusion matrix.
   • Generated a detailed classification report (precision, recall, F1-score).

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🎯 Final Accuracy

Model: Random Forest Classifier
Accuracy: 96.49%

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📊 Key Visuals

• Class Distribution Plot
• Correlation Heatmap
• Confusion Matrix

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🛠️ Technologies Used

• Python
• Pandas, NumPy
• Seaborn, Matplotlib
• Scikit-learn (RandomForest, train_test_split, metrics)

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📎 How to Run

1. Clone the repository
2. Install required packages (pip install -r requirements.txt)
3. Run the Jupyter notebook: Tumor_Detection.ipynb