Predictive Modeling with Machine Learning and Neural Networks course | Post Graduate Program in Artificial Intelligence and Machine Learning – The university of texas at austin – OCTOBER 2026
Wind Turbine Failure Detection
Develop a machine learning solution for Aeolus Renewables, an independent power producer operating a fleet of 1,150 onshore wind turbines, that analyzes wind turbine sensor data to detect drivetrain failures before they become critical, enabling condition-based maintenance, reducing unplanned downtime, lowering maintenance costs, and maximizing energy generation across the turbine fleet.
Neural Networks | Random Forest | Decision Tree | Boosting | Hyperparameter Tuning | Feature Importance | Data Preprocessing | Exploratory Data Analysis
neural networks course | Post Graduation in Machine learning and Artificial intelLigence – international institute information technology bangalore – october 2022.
Melanoma Detection
Develop a deep learning solution for skin cancer diagnosis that analyzes dermoscopic images of skin lesions to accurately classify nine oncological disease categories, including melanoma, enabling early detection of malignant cases, supporting clinical decision-making, reducing diagnostic effort, and improving patient outcomes through AI-assisted medical image analysis
Deep Learning | Convolutional Neural Networks (CNN) | Computer Vision | Image Classification | Medical Image Analysis | Neural Networks | Supervised Learning | Image Preprocessing | Data Augmentation | Hyperparameter Tuning | Python | Jupyter Notebook | Pandas | NumPy | Matplotlib | Seaborn | Tensor-Flow | Keras
machine learning II course | Post Graduation in Machine learning and Artificial intelLigence – international institute information technology bangalore – september 2022.
Telecom Churn Case Study
Developed a machine learning solution for a telecommunications provider to predict customer churn using customer demographics, service usage, recharge behavior, and 2G/3G network activity data. The project involved extensive data preparation, missing-value treatment, exploratory data analysis (EDA), feature selection, dimensionality reduction using Principal Component Analysis (PCA), and predictive modeling with LightGBM. By identifying customers with a high probability of churn, the solution enables targeted retention strategies, reduces customer attrition, improves marketing effectiveness, and supports data-driven customer relationship management.
Machine Learning || Supervised Learning | Feature Selection | Feature Engineering | Dimensionality Reduction | Model Evaluation | Data Preprocessing | Exploratory Data Analysis | Python | Jupyter Notebook | Pandas | NumPy | Matplotlib | Seaborn | Scikit-learn | LightGBM | PCA |
machine learning I course | Post Graduation in Machine learning and Artificial intelLigence – international institute information technology bangalore – june 2022.
Bike Rental Study
Developed a data analytics and predictive modeling solution for a bike-sharing service to understand and forecast bicycle rental demand. The project combined exploratory data analysis (EDA), feature engineering, and regression modeling to investigate how weather conditions, seasonal variations, holidays, working days, and time-related factors influence rental activity. The resulting model was used to predict future demand, providing insights that can support fleet allocation, resource planning, operational efficiency, and customer service optimization.
Machine Learning | Regression Analysis | Predictive Modeling | Supervised Learning | Feature Engineering | Model Evaluation | Data Preprocessing | Exploratory Data Analysis | Python | Jupyter Notebook | Pandas | NumPy | Matplotlib | Seaborn | Scikit-learn
statistic essentials course | Post Graduation in Machine learning and Artificial intelLigence – international institute information technology bangalore – may 2022.
Lending Club Case Study
Performed exploratory data analysis on a large consumer lending dataset from Lending Club to investigate the relationship between borrower profiles, loan attributes, and default outcomes. Identified key risk indicators including interest rates, loan grades, income levels, verification status, loan purpose, and funding characteristics to support data-driven credit risk assessment and lending decision-making.
Data Preprocessing | Exploratory Data Analysis | Python | Jupyter Notebook | Pandas | NumPy | Matplotlib | Seaborn
