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-Breast Cancer Classification Project
+# Breast Cancer Classification Project
 
-📝 Description
+## 📝 Description
 The Breast Cancer Classification Project is a deep learning application designed to classify breast tumors as benign or malignant. This project utilizes a neural network built with TensorFlow/Keras and is fully implemented within a Google Colab notebook for ease of experimentation and access.
 
-🎯 Project Goals
+## 🎯 Project Goals
 The primary goal of this project is to develop a model that can accurately classify breast cancer cases based on numerical data features, aiding in early diagnosis and treatment decisions.
 
-📊 Dataset Overview
+## 📊 Dataset Overview
 Source: The dataset is the Breast Cancer dataset available in sklearn, containing key characteristics of tumors.
 Data Features: Includes numerical attributes related to tumor measurements and properties.
 Classes: Two target classes—benign (non-cancerous) and malignant (cancerous).
 Dataset Split: The dataset is divided into an 80-20 split for training and testing.
 
-🚀 Features
+## 🚀 Features
 Neural Network Model: Built with TensorFlow and Keras, featuring input, hidden, and output layers.
 Performance Visualization: Training and validation accuracy and loss are tracked and visualized to assess model performance.
 Predictive System: A simple function is included to classify new data inputs, predicting whether a tumor is benign or malignant.
 
-🔍 Project Workflow
+## 🔍 Project Workflow
 Data Preprocessing: Load the dataset, explore data distribution, and standardize features for improved model performance.
 Model Building: Define a neural network with input, hidden, and output layers to capture the patterns in the data.
 Training and Validation: Train the model on 80% of the data, validating it with a 10% validation split to monitor accuracy and loss over epochs.
 Performance Evaluation: Evaluate the model on the test data and visualize metrics like accuracy and loss.
 Prediction System: Test the model’s real-world application by classifying new data points.
 
-💻 Tech Stack
+## 💻 Tech Stack
 Python: Primary programming language.
 TensorFlow/Keras: Deep learning framework used to build and train the model.
 Pandas: For data handling and preprocessing.
 Google Colab: Provides a cloud-based environment for running the project.
 
-⚙️ How to Run
+## ⚙️ How to Run
 Open the Notebook:
 Access the Google Colab notebook by uploading it to your Colab environment.
 Execute Cells:
 Run each cell in sequence to load data, preprocess it, build, train, and evaluate the model.
 
-Test Predictions:
+## Test Predictions:
 Use the predictive system function within the notebook to classify new data points.
-🧪 Models and Performance
+###🧪 Models and Performance
 The neural network model is evaluated based on:
 
 Accuracy: Percentage of correct predictions made by the model.
 Loss: Measures the error in prediction, helping to monitor overfitting or underfitting.
-📈 Results
+
+##📈 Results
 The model achieves satisfactory accuracy in distinguishing between benign and malignant tumors. Training and validation metrics are plotted to visualize model performance over epochs.
 
-✒️ Contributor
+###✒️ Contributor
 Name: Nethmi Gamage
 GitHub: [Your GitHub Profile](https://github.com/Nethmi11)