📌 Number Recognition 📖 Project Overview This project focuses on handwritten digit recognition using machine learning techniques. The model is trained on image data to accurately identify numbers from 0 to 9. This is a common application in Optical Character Recognition (OCR) systems used in various industries such as banking, postal services, and document processing.

📂 Dataset The project uses a dataset containing handwritten digits, likely sourced from popular datasets like MNIST or similar. Each image represents a single digit with pixel intensity values as input features.

⚙️ Technologies Used Python (NumPy, Pandas, Matplotlib, Seaborn), Machine Learning Frameworks (Scikit-learn, TensorFlow, Keras), Jupyter Notebook, OpenCV (if image preprocessing is needed)

📊 Key Steps Data Preprocessing – Normalization, reshaping, and augmentation of image data. Exploratory Data Analysis (EDA) – Visualizing dataset distributions and identifying patterns. Model Training – Implementing and training models like CNN (Convolutional Neural Network) or traditional classifiers. Evaluation & Optimization – Tuning hyperparameters to improve accuracy. Prediction & Testing – Testing the model on unseen handwritten digits.

🚀 How to Run Clone this repository: git clone https://github.com/ankitsingh32/Number_recognition.ipynb/blob/main/Number_recognition.ipynb?short_path=3b8903c

Install dependencies: pip install -r requirements.txt

Run the Jupyter Notebook: jupyter notebook

📈 Results & Insights Achieved 40% accuracy on the test set. CNN performed better than traditional models due to feature extraction capabilities.

📌 Future Enhancements Implementing data augmentation for better generalization. Deploying the model as a web application.