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hybrid-k-means-pso

An advanced version of K-Means using Particle swarm optimization for clustering of high dimensional data sets, which converges faster to the optimal solution.

insight-dataengineering-challenge

netflix-movie-recommender-system

Developed a recommendation system in Python using Netflix prize dataset and MovieLens data set using collaborative filtering technique to recommend movies to a user, based on their preferences.

simple-visualize-dataset

Simple way to Visualize Data using Python packages Pandas, Sklearn, Numpy and matplotlib

spam-mail-filtering

A text classifier in Python using classification algorithms of machine learning (Support vector machines, Naïve Bayes classifier) to detect if a given mail or message is spam or ham (not spam). Spam dataset was derived from Kaggle, UCI repository etc.

stance-detection-oftweets

A simple python script to extract data from Twitter API on a particular target and classify the tweets as positive or negative or neutral based on their sentiment.

stock_prediction

Predict the closing price of the S&P 500 data using a special type of recurrent neural network called an LSTM network.

youtube-us-trending-videos-data.

Context YouTube (the world-famous video sharing website) maintains a list of the top trending videos on the platform. According to Variety magazine, “To determine the year’s top-trending videos, YouTube uses a combination of factors including measuring users interactions (number of views, shares, comments and likes). Note that they’re not the most-viewed videos overall for the calendar year”. Top performers on the YouTube trending list are music videos (such as the famously virile “Gangam Style”), celebrity and/or reality TV performances, and the random dude-with-a-camera viral videos that YouTube is well-known for. This dataset is a daily record of the top trending YouTube videos in US. Content Data includes the video title, channel title, publish time, tags, views, likes and dislikes, description, and comment count. Acknowledgements This dataset was submitted by "Mitchell J" on kaggle. The data was retrieved through Youtube API. What is done so far? Did an Exploratory Data Analysis on the data to derive few meaningful trends through visualizations. To Do's and what else can be done? Sentiment analysis on the description to see if it effects views,likes..etc. Analysis of what factors will help a newly uploaded video to trend. Analyzing how to convert views into likes and comments. (All these without taking in the actual content of the video) ~ Hemanth Kumar Gudur