vandhu3012

Developed machine learning models using Powerbi, Python,improving customer churn prediction and sales forecasting.

The machine learning project begins with a thorough literature review of existing ML techniques and their implementation using Python, followed by identifying a real-world problem suitable for ML application. Data is collected from online repositories or created manually, then cleaned and normalized using libraries like Pandas and NumPy. Feature engineering enhances model performance, and appropriate ML algorithms are selected based on the problem type—classification, regression, or clustering.Models are trained using frameworks like scikit-learn or TensorFlow, with hyperparameter tuning applied to optimize performance. Evaluation metrics such as accuracy, precision, recall, and F1 score assess model effectiveness. The final model is deployed via web frameworks like Flask or FastAPI for real-world integration. The software requirements include a system with at least 8GB RAM and GPU support, Python 3.x, and libraries such as Pandas, NumPy, Matplotlib, Seaborn, scikit-learn, TensorFlow, and PyTorch. Development is conducted using IDEs like Jupyter Notebook or PyCharm, with version control managed through Git and optional cloud deployment via AWS, Google Cloud, or Azure for scalability.

During my internship ,I actively contributed to project development based on the AI/ML technologies to develop innovative projects, including designing and implementing machine learning models for real-world applications and including data preprocessing, feature engineering, and model optimization, focusing on enhancing decision-making processes.