Welcome to my GitHub repository, where I showcase my skills and interests in data science and machine learning. In this repository, you will find a collection of projects, notebooks, and resources that demonstrate my expertise in the field of data science and machine learning. Whether you are a potential employer, collaborator, or just someone interested in the work I've done, I hope you find this repository informative and inspiring.
Here are some of the data science and machine learning projects I have worked on:
-
...
- Description: A machine learning project that predicts customer churn for a telecom company using classification algorithms.
- Repository Link: ...
-
Image Classification with Convolutional Neural Networks (CNNs)
- Description: A deep learning project that classifies images using CNNs and Transfer Learning techniques.
- Repository Link: Image Classification with CNNs
-
...
- Description: ....
- Repository Link: ...
-
Natural Language Processing (NLP) for ...
- Description: An NLP project that analyzes ....
- Repository Link: ..
I also maintain Jupyter notebooks that explain and showcase various data science and machine learning concepts. These notebooks cover topics such as data preprocessing, feature engineering, model training, and evaluation. You can explore them in the Notebooks directory of this repository.
To support these projects and notebooks, I have collected and curated datasets from various sources. You can find the datasets used in these projects in the [Project name/Dataset] directory.
Here is a list of some of the key data science and machine learning skills I've included in my portfolio:
- Data Cleaning and Preprocessing
- Exploratory Data Analysis (EDA)
- Feature Engineering
- Machine Learning (Supervised and Unsupervised Learning)
- Deep Learning and Neural Networks
- Data Visualization (PowerBI, Tableau)
- Data Extraction (SQL)
- Natural Language Processing (NLP)
- Model Evaluation and Hyperparameter Tuning
- Data Pipelines and Automation
I am continuously expanding my skill set and exploring new techniques and technologies in the field of data science. Thank you so much for your attention.