🧪 Tumor Detection Project

This project applies machine learning to detect whether a tumor is Malignant (M) or Benign (B) based on medical features such as mean radius, texture, perimeter, and area. The model is built using Python and scikit-learn.

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📌 Project Overview

• Load and clean the tumor dataset (Tumor_Detection.csv).
• Explore the data with descriptive statistics and visualizations.
• Preprocess data (drop irrelevant columns, handle missing values, scale features).
• Train a Random Forest Classifier to classify tumors.
• Evaluate performance using accuracy, confusion matrix, and classification report.
• Analyze important features influencing predictions.

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📊 Steps in the Workflow

1. Data Preparation

   • Remove id and unnamed columns.
   • Handle missing values.
   • Encode target labels (M=1, B=0).

2. Exploratory Data Analysis

   • Class distribution visualization.
   • Summary statistics for numeric features.
   • Correlation heatmap.

3. Modeling

   • Train/test split.
   • Standardize features.
   • Train Random Forest Classifier.

4. Evaluation

   • Accuracy and classification report.
   • Confusion matrix heatmap.
   • Feature importances plot.

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📈 Expected Results

• Accuracy: ~95–98% (depending on random train/test split).
• Top contributing features:
  • Mean radius
  • Mean perimeter
  • Mean area

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📌 Future Improvements

• Experiment with Logistic Regression, SVM, or XGBoost.
• Perform hyperparameter tuning with GridSearchCV.
• Apply feature selection and dimensionality reduction (PCA).
• Use cross-validation for more robust results.

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🙌 Acknowledgements

Dataset adapted from the Breast Cancer Wisconsin Dataset (UCI Machine Learning Repository).

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✍️ Author

• Your Name
  📧 tripathiharsh2104@gmail.com
  🔗 LinkedIn Profile """