Machine Learning

Introduction : Projects focused on predictive modeling, algorithmic decision-making, and pattern recognition using ML techniques like regression, neural networks, and clustering.

Projects :

Project Title : Predictive Maintenance for Industrial Equipment

Statement : Predict equipment failures in industrial machinery to reduce downtime.

Approach : Trained LSTM neural networks on sensor data from IoT devices to detect anomalies.

Tools : Python (TensorFlow/PyTorch), Pandas, AWS SageMaker.

Project Title : Crop Yield Prediction

Statement : Predict agricultural yields using deep learning.

Approach : Built a CNN to analyze satellite imagery and environmental data.

Tools : TensorFlow, OpenCV, Keras.

Project Title : Heart Disease Prediction

Statement : Diagnose heart disease using medical data.

Approach : Used random forest and XGBoost models on clinical datasets.

Tools : Scikit-learn, Python, Jupyter Notebook.

Project Title : Customer Churn Prediction for Telecom

Statement : Identify at-risk customers to reduce churn.

Approach : Analyzed customer behavior with random forest classifiers.

Tools : Python, SQL, Power BI.

Project Title : Real-Time Face and Smile Detection

Statement : Detect facial expressions for security or social media apps.

Approach : Trained a CNN on labeled facial datasets.

Tools : OpenCV, TensorFlow, Python.

Project Title : Dynamic Pricing Strategy

Project Statement: Optimize pricing based on market demand and competitor pricing.

Approach: Use reinforcement learning to adjust prices dynamically.

Tools & Technology: Python, PyTorch, Pandas, NumPy.