Data Collection & Preprocessing
Gather data from diverse sources, including databases, APIs, and real-time systems.
Clean, preprocess, and format data to ensure quality and consistency for analysis and modeling.
Exploratory Data Analysis (EDA)
Perform statistical analysis and data visualization to uncover patterns, trends, and anomalies.
Use tools like Python, Power BI to present insights effectively.
Model Development & Optimization
Build, train, and evaluate machine learning models such as Linear Regression, Decision Trees, Random Forests, Gradient Boosting, Support Vector Machines, Neural Networks, and more.
Tune hyperparameters and optimize models for accuracy, precision, recall, or other relevant metrics.
Deployment of Machine Learning Models
Develop pipelines for deploying machine learning models to production environments using cloud services like AWS, Azure
Implement continuous monitoring, testing, and maintenance to ensure the reliability of deployed models.
Collaboration and Stakeholder Communication
Collaborate with cross-functional teams, including business analysts, engineers, and domain experts, to align models with business objectives.
Deliver actionable insights and present findings to both technical and non-technical stakeholders.
Tools and Technologies
Programming Languages: Python, SQL
Machine Learning Libraries: Scikit-learn, TensorFlow, PyTorch, XGBoost
Visualization Tools: Matplotlib, Seaborn, Plotly, Power BI, Tableau
Cloud Platforms: Azure ML
