Project Description
This repository contains my submission for the Machine Learning Engineer for Beginner learning path as part of the IDCamp 2024 program by Indosat Ooredoo Hutchison in collaboration with Dicoding Indonesia. The project demonstrates my application of machine learning techniques, including clustering and classification.
Programming Language
- Jupyter Notebook
Key Features
- Data Preprocessing: Comprehensive data cleaning and preprocessing steps to prepare the dataset for machine learning models.
- Clustering: Implementation of clustering algorithms such as K-Means and Hierarchical Clustering to identify patterns and group data points.
- Classification: Use of classification techniques including Decision Trees, Random Forest, and Support Vector Machines (SVM) to predict categorical outcomes.
- Model Evaluation: Rigorous evaluation of model performance using metrics such as accuracy, precision, recall, and F1-score.
- Visualization: Detailed visualizations of data distributions, clustering results, and model performance metrics using Matplotlib and Seaborn.
Technologies Used
- Jupyter Notebook: For interactive development and documentation of the machine learning workflow.
- Pandas: For data manipulation and preprocessing.
- Scikit-learn: For implementing clustering and classification algorithms.
- Matplotlib and Seaborn: For creating informative and insightful data visualizations.
Project Highlights
- Complete Workflow: The project covers the entire machine learning pipeline from data preprocessing to model evaluation, showcasing a robust understanding of machine learning techniques.
- Reproducibility: All steps and results are documented in Jupyter Notebooks, ensuring that the work can be easily reproduced and verified.
- Practical Application: Demonstrates practical application of machine learning algorithms to solve real-world problems, reflecting a hands-on approach to learning.
Links
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Repository: GitHub - alrescha79-cmd/ML-clustering-classsification
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Dataset: Kaggle - Data Science Job Datasets