Welcome to my comprehensive collection of machine learning projects! This repository showcases my journey through various ML techniques, algorithms, and real-world applications.
This repository contains implementations of various machine learning algorithms and techniques applied to real-world problems. From regression to classification, time series forecasting to gradient descent optimization - each project demonstrates different aspects of machine learning.
A deep dive into predicting future values based on historical data patterns. This project implements various forecasting techniques including:
- ARIMA/SARIMA models
- Prophet
- LSTM networks
- Exponential smoothing methods
Predicting insurance costs based on various factors using linear regression techniques:
- Feature engineering and selection
- Model evaluation metrics
- Regularization techniques
- Residual analysis
Binary classification project to detect malignant vs benign breast cancer:
- Data preprocessing and normalization
- Feature importance analysis
- Implementation of various classifiers (SVM, Random Forest, Logistic Regression)
- Model performance evaluation
Regression model to predict car prices based on various features:
- Exploratory data analysis
- Feature engineering
- Multiple regression models
- Model comparison and evaluation
Implementation of optimization algorithms from scratch:
- Batch gradient descent
- Stochastic gradient descent
- Mini-batch gradient descent
- Learning rate optimization
Time series and regression model to predict bike sharing demand:
- Temporal feature engineering
- Weather data integration
- Seasonal pattern analysis
- Ensemble methods
- Python 3.x
- Pandas & NumPy
- Scikit-learn
- TensorFlow & Keras
- Matplotlib & Seaborn
- Statsmodels
- Jupyter Notebooks
The projects use various datasets from sources such as:
- UCI Machine Learning Repository
- Kaggle
- Public government datasets
- Synthetic datasets for educational purposes
These projects represent my learning journey in machine learning. I started with basic regression models and gradually moved to more complex algorithms and techniques. Each project has helped me understand different aspects of the machine learning workflow:
- Data cleaning and preprocessing
- Exploratory data analysis
- Feature engineering
- Model selection and training
- Hyperparameter tuning
- Model evaluation and interpretation
I plan to expand this repository with:
- Deep learning projects
- Natural language processing
- Computer vision applications
- Reinforcement learning examples
Feel free to explore, fork, and contribute to any of these projects. Suggestions and improvements are always welcome!
This repository is licensed under the MIT License - see the LICENSE file for details.
"Learning means doing. Read it as a story, practice it in real life." - Syeeda Fatima