From b2f98050fa76201c4ca48f9303d7af56ad38a150 Mon Sep 17 00:00:00 2001 From: Morteza Hosseini Date: Tue, 14 May 2024 23:49:39 +0100 Subject: [PATCH] Delete README copy.md --- README copy.md | 94 -------------------------------------------------- 1 file changed, 94 deletions(-) delete mode 100644 README copy.md diff --git a/README copy.md b/README copy.md deleted file mode 100644 index 9ebd3e9e..00000000 --- a/README copy.md +++ /dev/null @@ -1,94 +0,0 @@ -# :rocket: Portfolio - -This repository includes my side projects on various applications of Data Science and Machine Learning. - -Check the documentaion [here](https://smortezah.github.io/portfolio/docs). - -Also, my articles on the Medium platform can be found [here](https://medium.com/@morihosseini/). - -**Note:** The following list is sorted alphabetically. - -## :rotating_light: Anomaly detection - -- [Credit card fraud detection](anomaly-detection/fraud-detection.ipynb): detecting fraudulent transactions in a dataset using neural networks - -## :factory: Automation - -- [Auto commit to GitHub](automation/auto-commit): automating the process of committing and pushing changes to GitHub - -## :camera: Computer Vision - -- [Ants vs bees image classification](computer-vision/ants-bees-classification/image-classification.ipynb): an app for classification of images, employing deep learning models - -## 🧩 Data Structures - -- [Hashing](data-structure/hashing.ipynb): an introduction to hashing, its applications, and Python implementation -- [Sorting](data-structure/sorting-popular.ipynb): a guide to popular sorting algorithms in Python - -## :mag: EDA (Exploratory Data Analysis) - -- [Data balancing](eda/data-balancing.ipynb): balancing imbalanced datasets using different methods -- [Handling missing data](eda/missing-data.ipynb): handling missing data in a dataset using various methods -- [Polars](eda/polars.ipynb): using [polars](https://www.pola.rs) library for data manipulation and analysis - -## :hammer_and_wrench: ETL (Extract, Transform, Load) - -- [ETL pipeline with Airflow and Docker](etl/airflow-docker): automatization of extracting data from various sources, transforming them, and loading the transformed data into a database - -## :gear: Hyperparameter tuning - -- [KerasTuner](hypertune/kerasTuner.ipynb): hyperparameter tuning using [KerasTuner](https://keras.io/keras_tuner/) library -- [Optuna](hypertune/optuna.ipynb): hyperparameter tuning with [Optuna](https://optuna.org/) library - -## :brain: LLM (Large Language Model) - -- [Tokenization](llm/tokenization.ipynb): exploring tokenization of text data - -## :robot: Machine Learning - -- [Best threshold for logistic regression](machine-learning/threshold-logistic-regression.ipynb): different methods to find the optimal threshold for logistic regression - -## :lock: Privacy - -- [Anonymization](privacy/anonymization.ipynb): an introduction to data anonymization and its applications -- [Encryption](privacy/encryption.ipynb): a beginner's guide to Python encryption - -## :snake: Python - -- [Argument parsing](python/argparse.ipynb): a guide to argument parsing using `argparse` module -- [Generators](python/generator.ipynb): a hands-on guide to generators -- [Lambda](python/lambda.ipynb): an introduction to lambda functions -- [Pattern matching](python/match-case.ipynb): a guide to pattern matching with `match-case` statement - -## :chart_with_upwards_trend: Statistical analysis - -- [A/B testing](stats/ab-test.ipynb): testing the effectiveness of a new feature in a web application by A/B testing -- [Hypothesis testing: p-values around 0.05](stats/pvalue-around-0.05.ipynb): should we reject the null hypothesis if the p-value is around 0.05? - -## :bulb: Synthetic data generation - -- [Introduction](synthetic-data/intro.ipynb): generating synthetic data using Python and also, considerations for using synthetic data - -## :desktop_computer: Terminal - -- [jq](terminal/jq.ipynb): JSON manipulating with [jq](https://jqlang.github.io/jq/) -- [Rich](terminal/rich/rich.ipynb): formatting text in the terminal using [Rich](https://github.com/Textualize/rich) library - -## :hourglass_flowing_sand: Time-series - -- [Forecasting with sktime](time-series/sktime.ipynb): time-series forecasting using [sktime](https://github.com/sktime/sktime) library -- [Prevent overfitting](time-series/prevent-overfitting.ipynb): preventing overfitting in time series forecasting using different techniques - -## :art: Visualization - -- [lets-plot](visualization/lets-plot/codebook.ipynb): plotting with [lets-plot](https://lets-plot.org/index.html), a Python port of the R's [ggplot2](https://ggplot2.tidyverse.org/) library -- [Pitfalls](visualization/pitfalls/pitfalls.ipynb): common pitfalls in data visualization and how to avoid them -- [QR code](visualization/qrcode.ipynb): generating QR codes - -## :spider_web: Web scraping - -- [jobinventory](scrape/jobinventory.com/tutorial.ipynb): scraping job listings from jobinventory.com using Python - -## :memo: XAI (Explainable AI) - -- [Introduction](xai/intro.ipynb): an introduction to explainable AI and its importance