DNA Mutation Prediction

DNA Mutation Prediction – Machine Learning Project

I developed a DNA Mutation Prediction Model to analyze genetic variations and predict potential mutations, aiding in biomedical research and disease diagnosis. This project leverages machine learning techniques to classify mutations and assess their potential impact.

Key Features & Insights:

Data Preprocessing & Feature Engineering: Cleaned and structured genetic data to extract meaningful patterns.

Machine Learning Model: Trained and tested classification models (e.g., Random Forest, XGBoost, Neural Networks) to predict mutation types.

Exploratory Data Analysis (EDA): Identified mutation frequency, distribution, and correlations between different genetic features.

Model Evaluation: Assessed performance using metrics like accuracy, precision, recall, and F1-score to ensure reliable predictions.

Potential Applications: Supports genetic research, precision medicine, and early disease detection by identifying high-risk mutations.

Technologies Used:

• Python (Pandas, NumPy, Scikit-Learn, TensorFlow/Keras) for data processing & machine learning .

• Data Visualization (Matplotlib, Seaborn, Power BI) to represent mutation patterns effectively.

This project highlights my expertise in biomedical data analysis, machine learning, and predictive modeling, showcasing how data science can contribute to advancements in genetics and healthcare.