1.Agriculture Trend Analysis of Sichuan Province using IOT and Machine Learning.
I utilized location-specific data from Sichuan for predicting agricultural trends. My model employs decision tree, random forest, K Means, and extra tree for quality maintenance, regression, data grouping, and categorical data handling. Objectives include predicting agricultural trends via weather data analysis, implementing IoT for real-time analysis, enhancing data stream adaptation, and optimizing agriculture trend adjustments to predict ideal crops.
Technologies used: Arduino (C++) for hardware interface and Python (Google Colab) for data analysis.
2. Heart Disease Prediction Using Machine Learning Algorithms
In this project I have tested the most used Machine learning model such as: Random Forest, Decision Tree, Support Vector Machine, Naive Bayes and Logistic
Regression classifiers to show the performance of the selected classifications algorithm to best classify, or predict the heart disease cases.
3.Demola - Authentic Brands from Finland (Mirka), Vaasa, Finland.
- Multidisciplinary
- Concepting
- Solution ideas