From 4a32578fe02604879b49dee8fd07bcb4c18400e0 Mon Sep 17 00:00:00 2001 From: smortezah Date: Mon, 6 May 2024 14:56:40 +0100 Subject: [PATCH] Update README.md to enhance clarity and readability --- README.md | 82 +++++++++++++++++++++++++++---------------------------- 1 file changed, 41 insertions(+), 41 deletions(-) diff --git a/README.md b/README.md index b0f2aae1..56a42ef3 100644 --- a/README.md +++ b/README.md @@ -1,90 +1,90 @@ # :rocket: Portfolio -This repository includes my side projects on various applications of Data Science and Machine Learning. +Welcome to my repository, a collection of my exploratory projects in the diverse fields of Data Science and Machine Learning. -Check the documentaion [here](https://smortezah.github.io/portfolio/docs). +For a detailed understanding of these projects, you can refer to the comprehensive documentation available [here](https://smortezah.github.io/portfolio/docs). -Also, my articles on the Medium platform can be found [here](https://medium.com/@morihosseini/). +In addition to these projects, I regularly share my insights and learnings on the Medium platform. You can access my articles [here](https://medium.com/@morihosseini/). -**Note:** The following list is sorted alphabetically. +**Please note:** The projects listed below are organized alphabetically for your convenience. -## :rotating_light: Anomaly detection +## :rotating_light: Anomaly Detection -- [Credit card fraud detection](anomaly-detection/fraud-detection.ipynb): detecting fraudulent transactions in a dataset using neural networks +- [Credit Card Fraud Detection](anomaly-detection/fraud-detection.ipynb): Unveil fraudulent transactions using a neural network-based approach. ## :factory: Automation -- [Auto commit to GitHub](automation/auto-commit): automating the process of committing and pushing changes to GitHub +- [Automated GitHub Commits](automation/auto-commit): Simplify your workflow with an automated solution for 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 +- [Ants vs Bees Image Classification](computer-vision/ants-bees-classification/image-classification.ipynb): Harness the power of deep learning models to classify images. ## 🧩 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 +- [Understanding Hashing](data-structure/hashing.ipynb): Dive into the world of hashing, its applications, and Python implementation. +- [Sorting Algorithms](data-structure/sorting-popular.ipynb): A comprehensive guide to understanding and implementing 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 +- [Data Balancing](eda/data-balancing.ipynb): Learn techniques to balance imbalanced datasets. +- [Handling Missing Data](eda/missing-data.ipynb): Discover various methods for handling missing data in datasets. +- [Polars](eda/polars.ipynb): Leverage the [Polars](https://www.pola.rs) library for efficient 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 +- [ETL Pipeline with Airflow and Docker](etl/airflow-docker): A project showcasing the automation of data extraction, transformation, and loading into a database. -## :gear: Hyperparameter tuning +## :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 +- [KerasTuner](hypertune/kerasTuner.ipynb): Optimize your models with hyperparameter tuning using the [KerasTuner](https://keras.io/keras_tuner/) library. +- [Optuna](hypertune/optuna.ipynb): Enhance your models with hyperparameter tuning using the [Optuna](https://optuna.org/) library. ## :robot: Machine Learning -- [Best threshold for logistic regression](machine-learning/threshold-logistic-regression.ipynb): different methods to find the optimal threshold for logistic regression +- [Best Threshold for Logistic Regression](machine-learning/threshold-logistic-regression.ipynb): Explore 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 +- [Anonymization](privacy/anonymization.ipynb): Learn about data anonymization and its applications. +- [Encryption](privacy/encryption.ipynb): A guide to understanding and implementing 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 +- [Argument Parsing](python/argparse.ipynb): Master argument parsing using the `argparse` module. +- [Generators](python/generator.ipynb): A hands-on guide to understanding and using generators. +- [Lambda](python/lambda.ipynb): Get introduced to lambda functions. +- [Pattern Matching](python/match-case.ipynb): Learn pattern matching with the `match-case` statement. -## :chart_with_upwards_trend: Statistical analysis +## :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? +- [A/B Testing](stats/ab-test.ipynb): Test the effectiveness of a new feature in a web application using A/B testing. +- [Hypothesis Testing: p-values Around 0.05](stats/pvalue-around-0.05.ipynb): Understand when to reject the null hypothesis if the p-value is around 0.05. -## :bulb: Synthetic data generation +## :bulb: Synthetic Data Generation -- [Introduction](synthetic-data/intro.ipynb): generating synthetic data using Python and also, considerations for using synthetic data +- [Introduction](synthetic-data/intro.ipynb): Learn to generate synthetic data using Python and understand the 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 +- [jq](terminal/jq.ipynb): Manipulate JSON with [jq](https://jqlang.github.io/jq/). +- [Rich](terminal/rich/rich.ipynb): Format text in the terminal using the [Rich](https://github.com/Textualize/rich) library. -## :hourglass_flowing_sand: Time-series +## :hourglass_flowing_sand: Time-series Analysis -- [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 +- [Forecasting with sktime](time-series/sktime.ipynb): Forecast time-series data using the [sktime](https://github.com/sktime/sktime) library. +- [Prevent Overfitting](time-series/prevent-overfitting.ipynb): Learn techniques to prevent overfitting in time series forecasting. -## :art: Visualization +## :art: Data 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 +- [lets-plot](visualization/lets-plot/codebook.ipynb): Create stunning plots 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): Avoid common pitfalls in data visualization. +- [QR Code](visualization/qrcode.ipynb): Generate QR codes with ease. -## :spider_web: Web scraping +## :spider_web: Web Scraping -- [jobinventory](scrape/jobinventory.com/tutorial.ipynb): scraping job listings from jobinventory.com using Python +- [jobinventory](scrape/jobinventory.com/tutorial.ipynb): Scrape job listings from jobinventory.com using Python. ## :memo: XAI (Explainable AI) -- [Introduction](xai/intro.ipynb): an introduction to explainable AI and its importance +- [Introduction](xai/intro.ipynb): Understand the importance of explainable AI and its applications.