Welcome to the OIBSIP (Organization of Innovative Projects - Software and Information Processing) repository! This repository contains projects undertaken as tasks, each focusing on a specific machine learning challenge.
The repository is organized as follows:
OIBSIP/
|-- README.md
|-- TASK_01_Iris_Flower_Classification/
|-- TASK_02_Car_Price_Prediction/
|-- TASK_03_Email_Spam_Detector/
Each task has its own dedicated folder with a similar structure:
TASK_N_Name/
|-- README.md/
|-- ipynb/
|-- ...
Feel free to explore each task folder for detailed documentation, source code, and any other relevant files associated with the specific project.
- Description: Train a machine learning model to classify iris flowers into three species (setosa, versicolor, and virginica) based on their measurements.
- Technologies: Python, Scikit-learn
- Folder Structure:
TASK_01_Iris_Flower_Classification/ |-- src/ |-- docs/ |-- ...
- Description: Predict the price of a car using machine learning, considering factors such as brand goodwill, car features, horsepower, and mileage.
- Technologies: Python, Machine Learning
- Folder Structure:
TASK_02_Car_Price_Prediction/ |-- src/ |-- docs/ |-- ...
- Description: Build an email spam detector using Python and machine learning to classify emails into spam and non-spam categories.
- Technologies: Python, Machine Learning
- Folder Structure:
TASK_03_Email_Spam_Detector/ |-- src/ |-- docs/ |-- ...
If you'd like to contribute to a specific task or suggest improvements, feel free to fork the repository and submit a pull request. Make sure to follow the contribution guidelines outlined in the respective task folders.
Advait Dongre Happy coding!