akshitvjain/deeplearning-network-traffic

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20
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Jupyter Notebook
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MIT License
Created over 6 years ago
Updated almost 6 years ago

akshitvjain/deeplearning-network-traffic

akshitvjain

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Network Traffic Identification with Convolutional Neural Networks

product-sales-forecasting

Forecasted product sales using time series models such as Holt-Winters, SARIMA and causal methods, e.g. Regression. Evaluated performance of models using forecasting metrics such as, MAE, RMSE, MAPE and concluded that Linear Regression model produced the best MAPE in comparison to other models

Jupyter Notebook

restaurant-finder-featureReviews

Build a Flask web application to help users retrieve key restaurant information and feature-based reviews (generated by applying market-basket model – Apriori algorithm and NLP on user reviews).

realtime-twitter-trends-analytics

A big data project to develop a real-time data pipeline for analyzing the popularity and sentiments of trending topics on Twitter.

item-based-recommender

An item-based recommender model that computes cosine similarity for each item pairs using the item factors matrix generated by Spark MLlib’s ALS algorithm and recommends top 5 items based on the selected item.

customer-segmentation-marketing

Implement K-means and Hierarchical clustering algorithms to segment customers in order to gain insight into shopping behaviors, analyze product affinity, measure marketing effectiveness, and better allocate future marketing spend.