This repository contains a collection of machine learning projects and experiments developed for learning and practicing machine learning algorithms.
The goal of this repository is to explore different datasets and implement classification and prediction models using Python and Jupyter Notebook.
Each project focuses on a different real-world dataset and demonstrates the workflow of
- Data loading
- Data preprocessing
- Feature analysis
- Model training
- Model evaluation
This repository serves as a personal learning archive while studying machine learning concepts and model-building techniques.
Machine-Learning-Models
│
├── DDoS_Attack
│ ├── DDos_Attack.ipynb
│ └── realtime_ddos_traffic_dataset.csv
│
├── SPAM_url
│ ├── spamURL.ipynb
│ └── url_spam_classification.csv
│
├── australia
│ ├── AUS_Rain.ipynb
│ └── weatherAUS.csv
│
├── diabetes
│ ├── diabetes.ipynb
│ └── diabetes.csv
│
├── sonar
│ ├── RM.ipynb
│ └── sonar_data.csv
│
├── vehicle
│ ├── Emission.ipynb
│ └── vehicle_emission_dataset.csv
│
└── README.md
This project focuses on detecting Distributed Denial of Service (DDoS) attacks using machine learning.
realtime_ddos_traffic_dataset.csv
The dataset contains network traffic features used to identify malicious traffic patterns.
DDos_Attack.ipynb
The notebook demonstrates:
- Network traffic analysis
- Feature preprocessing
- Machine learning model training
- Detection of malicious traffic
- Network intrusion detection
- Cybersecurity monitoring
- Real-time attack prevention
This project detects whether a URL is spam or legitimate.
url_spam_classification.csv
Contains features extracted from URLs such as:
- Length of URL
- Presence of suspicious characters
- Domain features
spamURL.ipynb
The notebook includes:
- Feature extraction from URLs
- Classification model training
- Spam detection evaluation
- Email filtering
- Phishing detection
- Website security
This project predicts rainfall in Australia based on historical weather data.
weatherAUS.csv
Contains meteorological data such as:
- Temperature
- Humidity
- Wind speed
- Rainfall records
AUS_Rain.ipynb
The notebook demonstrates:
- Weather data preprocessing
- Feature engineering
- Rain prediction model training
- Weather forecasting
- Agricultural planning
- Climate analysis
This project predicts whether a patient is likely to have diabetes based on medical attributes.
diabetes.csv
The dataset includes medical parameters such as:
- Glucose level
- Blood pressure
- Insulin
- BMI
- Age
diabetes.ipynb
The notebook includes:
- Medical data preprocessing
- Feature analysis
- Binary classification model training
- Healthcare diagnostics
- Medical decision support
- Preventive health analysis
This project predicts whether an object detected by sonar is a mine or a rock.
sonar_data.csv
The dataset consists of sonar signal returns from different objects.
RM.ipynb
The notebook demonstrates:
- Signal feature analysis
- Classification of sonar signals
- Model evaluation
- Naval defense systems
- Underwater object detection
- Marine robotics
This project predicts vehicle emission levels based on engine and vehicle parameters.
vehicle_emission_dataset.csv
The dataset contains information such as:
- Engine size
- Fuel type
- Vehicle weight
- Fuel consumption
Emission.ipynb
The notebook demonstrates:
- Environmental data analysis
- Emission prediction models
- Performance evaluation
- Environmental monitoring
- Vehicle regulation compliance
- Emission control research
Across the projects, a common ML workflow is followed:
Dataset Collection
↓
Data Cleaning
↓
Feature Selection
↓
Model Training
↓
Model Evaluation
↓
Prediction
During experimentation, several machine learning algorithms may be used such as:
- Logistic Regression
- Decision Trees
- Random Forest
- K-Nearest Neighbors
- Support Vector Machines
- Naive Bayes
These algorithms are tested to understand their performance across different datasets.
| Technology | Purpose |
|---|---|
| Python | Programming language |
| Jupyter Notebook | Experiment environment |
| Pandas | Data manipulation |
| NumPy | Numerical computation |
| Scikit-learn | Machine learning models |
| Matplotlib | Data visualization |
| Seaborn | Statistical visualization |
This repository was created as part of a machine learning learning journey to understand:
- Data preprocessing techniques
- Feature engineering
- Classification algorithms
- Model evaluation metrics
- Real-world datasets
It acts as a practice environment for building and testing machine learning models.
Planned improvements include:
- Adding deep learning models
- Model deployment using Flask or FastAPI
- Hyperparameter tuning
- Performance benchmarking
- Adding more datasets
- Building end-to-end ML pipelines
Abinesh N
GitHub https://github.com/Abineshabee