Description:
A Computer Science student (CGPA: 3.7/4.0) with hands-on experience in training machine learning models using Python and TensorFlow offers professional data analysis and ML development services. Real-world problems are solved using structured data preprocessing, model training, and evaluation. This service is strictly focused on machine learning and data analysis—no mobile app development or Flutter integration is included.
Services Offered:
Data Analysis & Preprocessing
Data cleaning, transformation, and exploration using Pandas and NumPy. Handling missing values, outlier detection, and feature engineering. Data visualization using Matplotlib and Seaborn for actionable insights.
Machine Learning Model Development
Development and training of machine learning models using Python, TensorFlow, and Scikit-learn. Supervised and unsupervised learning including regression, classification, and clustering. Neural network design using TensorFlow and Keras.
Data Preprocessing Pipelines
Engineering of data preprocessing pipelines that increase model training efficiency and significantly reduce overall development time. Automated data transformation workflows for reproducible research.
Model Evaluation & Optimization
Model performance evaluation using appropriate metrics including accuracy, precision, recall, F1-score, and mean absolute error. Hyperparameter tuning and cross-validation for improved model generalization.
Jupyter Notebook Solutions
Interactive and well-documented Jupyter Notebooks for exploratory data analysis (EDA) and model prototyping. Code is clean, commented, and structured for reproducibility.
Technical Skills:
Python, Pandas, NumPy, TensorFlow, Keras, Scikit-learn, Matplotlib, Seaborn, Jupyter Notebook, Data Analysis, Data Preprocessing, Feature Engineering, Supervised Learning, Unsupervised Learning, Regression, Classification, Clustering, Neural Networks, Model Evaluation, Hyperparameter Tuning.
Differentiators:
At TechStep Solutions and through student projects, data preprocessing pipelines were engineered that increased model training efficiency, significantly reducing overall development time for experimental projects.
Machine learning models were developed and trained using Python and TensorFlow on real-world problems, demonstrating practical application of theoretical concepts.
Portfolio highlights include:
Development and training of ML models using Python and TensorFlow on real-world datasets. Engineered data preprocessing pipelines for improved efficiency. Model evaluation with mean absolute error and other performance metrics. Neural network architecture design including dense layers, dropout layers, and optimization.
Keywords:
Machine Learning , Data Analysis, Python ,Pandas , TensorFlow, Jupyter Notebook, data preprocessing, data visualization, Scikit-learn, NumPy, regression analysis, classification model, clustering, neural networks, freelance data scientist, model training, feature engineering.