This repository contains two machine learning projects that demonstrate my expertise in developing, tuning, and evaluating machine learning models from scratch. Each notebook showcases a different approach to solving real-world data-driven problems, from simple models to more advanced techniques.
This notebook focuses on building a machine learning pipeline from scratch, walking through every step from data preprocessing to model evaluation.
In this part, I demonstrate:
- Data Preprocessing: Handling missing values, feature scaling, and encoding categorical data.
- Exploratory Data Analysis (EDA): Uncovering insights using visualizations and descriptive statistics.
- Supervised Learning Models: Comparing performance of algorithms like Decision Trees, Random Forests, and SVM.
- Hyperparameter Tuning: Using GridSearchCV to find the optimal parameters.
- Model Evaluation: Assessing model performance with metrics such as accuracy, precision, recall, and F1 score.
- Full machine learning workflow using Scikit-learn.
- Extensive Feature Engineering and preprocessing techniques.
- Cross-Validation and hyperparameter tuning for performance optimization.
- Data Visualization with Seaborn and Matplotlib.
- Clone the repository:
git clone https://github.com/YourGithubUsername/YourRepoName.git
- Open the notebook:
jupyter notebook Scratch_ML.ipynb
- Run the cells in sequence to replicate the results.
This notebook builds upon Part 1 by introducing more advanced techniques and tools to further improve the model performance and handle more complex data structures.
The ML Complete Project goes deeper into the advanced stages of machine learning, covering:
- Advanced Feature Engineering: Identifying and creating new features to improve model performance.
- Handling Complex Data: Techniques for managing outliers, imbalanced data, and large datasets.
- Deep Learning Integration: Applying neural networks to the problem for better accuracy.
- Model Stacking: Combining multiple models to build more robust predictions.
- Model Stacking and ensembling techniques.
- Data Augmentation to handle imbalanced datasets.
- Advanced Visualization for in-depth data exploration.
- Clone the repository:
- Open the notebook:
jupyter notebook ML_complete.ipynb
- Run the cells in sequence to experiment with advanced ML techniques.
- Python: Core programming language used.
- Pandas & NumPy: Data manipulation and numerical operations.
- Scikit-learn: Model building, evaluation, and tuning.
- Matplotlib ,Seaborn & Pyplot: Data visualization and EDA.