StackOverflow: A recommandation System
About this Project
In this project, we will implement a tags recommendation system for StackOverflow questions. StackOverflow is a reknown platform for questions and answers about programmation.
In the first notebook, we will extract the required data using the StackExchange data explorer tool, and explore the data. In the second notebook, we will implement various machine learning algorithms and select the suitable inference model to deploy an API endpoint.
Features
Artificial Intelligence
Get some help! Unlock the power of Artificial Intelligence directly from your admin panel, assisting you generating content and translation!
Automatisation
Repetitive tasks? Not a problem anymore! Automatisation is the magic arrow in our stack. Integrating directly into your admin panel, you can just focus on creativity.
Data Analysis
Keep improving your results! With Data-Analysis we transform the raw data from your app into meaningful insights (and beautiful graphs) which will allow to understand your users and market.
Technologies
Front-End
Streamlit
Streamlit turns data scripts into shareable web apps in minutes.
All in pure Python. No front‑end experience required.
source: streamlit.io
Data/Ai
Plotly
Plotly's Python graphing library makes interactive, publication-quality graphs. Examples of how to make line plots, scatter plots, area charts, bar charts, error bars, box plots, histograms, heatmaps, subplots, multiple-axes, polar charts, and bubble charts.
source: plotly.com
Sickit-Learn
scikit-learn (formerly scikits.learn and also known as sklearn) is a free and open-source machine learning library for the Python programming language.
It features various classification, regression and clustering algorithms including support-vector machines, random forests, gradient boosting, k-means and DBSCAN, and is designed to interoperate with the Python numerical and scientific libraries NumPy and SciPy. Scikit-learn is a NumFOCUS fiscally sponsored project.
source: wikipedia.org
XGBoost
XGBoost is an optimized distributed gradient boosting library designed to be highly efficient, flexible and portable. It implements machine learning algorithms under the Gradient Boosting framework. XGBoost provides a parallel tree boosting (also known as GBDT, GBM) that solve many data science problems in a fast and accurate way.
source: xgboost.readthedocs.io
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Q&A
If you are still wondering...
Hikonic is a web development agency founded by Thibgl, a passionate data scientist and full-stack engineer based in Bordeaux, France. With a commitment to delivering cutting-edge solutions, Hikonic specializes in blending innovative technologies with elegant designs to create powerful digital products. Whether you're a small business owner or a large enterprise, Hikonic tailors its services to meet your unique needs and goals.
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