Machine Learning & Data Science

Practical Machine Learning Application

My journey into machine learning has been focused on solving practical problems through data-driven insights. I have successfully developed and implemented models for both classification and regression tasks.

• ODIR Classification: In this project, I built a model to classify medical images, which involved data preprocessing, feature extraction, and training a deep learning model using TensorFlow and Keras.
• OLX Car Regression: For this project, I developed a regression model to predict used car prices based on various features. This involved extensive data cleaning, feature engineering, and model evaluation using Scikit-learn.

Technical Foundation

My data science toolkit is centered around Python and its powerful ecosystem of libraries. I am proficient in using Pandas for data manipulation, Matplotlib/Seaborn for visualization, and Scikit-learn for classical machine learning algorithms. I have also gained experience with TensorFlow for building and training neural networks.

Key Competencies

• Programming: Python for data science.
• Libraries: Pandas, NumPy, Scikit-learn, Matplotlib, TensorFlow, Keras.
• Modeling: Classification, Regression, and basic Deep Learning concepts.
• Workflow: Data cleaning, feature engineering, model training, and evaluation.
• Tools: Jupyter Notebooks for exploratory data analysis and model development.