Three Dimensional Tagcloud visualization for tourism

Three Dimensional Tagcloud visualization for tourism Metadata creation along with growth of social bookmarking emerged an approach named tagging. Often people look for location along with its route and detailed information about its surrounding. Mobile users may opt for current event that is taking place in the current location along with historical background of their surroundings and events that happen over time. They are provided with information on spatial context of location which harvests context information from freely available source and tag cloud visualization is created for this data. Firstly,…

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Rainfall prediction using Lasso and Decision Tree alogrithm on Python

Implementation Details: ———————– We are taking dataset X Train & Test is from ID to Oct-Dec (NOT ANNUAL Column) Y Train & Test is the Annual column We are taking dataset and Analysing dataset & plotted all graphs. Using Train set of X & Y we are applying ML algorithm Lasso and Decision Tree For X testset, we are arriving results and stored as resultLasso & resultDecisionTree   Python Demo

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Secret Image parts Sharing on Multiple images

Secret Image parts Sharing on Multiple images Implementation Details: ———————— Sender takes secret image, Hidimg image1 and Hiding image 2 Secret image is split into 2 parts img0, img1 The part of sercret image is taken and encoded and converted to string value then hidden in Hiding image 1, simialarly second part also done The above secret create hideimage1 and hideimage2 Then this image is sent to receiver Receiver receives receivedimage1 and receivedimage2 These images are loaded to extract secret image the image is read and string value is separated…

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k means++ Cluster algorithm for Heart Disease prediction

k means++ Cluster algorithm for Heart Disease prediction Implementation Details: ———————– Heart Disease Prediction using K-Means and K-means++ clustering and Logistics Regression 1. We are taken dataset data.csv 2. Input data.csv is split into three cluster by K-means algorithm taking centroid automatically. Whereas k-means++ arrives centroid with distance Cluster 0, Cluster1, Cluster2 3. Every cluster data is taken for getting trainset and test set Trainset contains 14 columns, whereas testset contains 13 column 4. Every cluster testset is predicted for heart disease Accuracy is arrived 5. Logistics regression is performed…

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Personality Prediction in Tweets

PERSONALITY PREDICTION FROM TWEETS Implementation Details: ———————– 1. Twitter data is collected for topic “apple” and stored as twitter.json file. The data will be added in the same file for execution of Twitterdata.py 2. Collected tweets from json file is extracted stored as tweet.csv data extracted from each tweet are tweet_id tweet_time tweet_author tweet_author_id tweet_language tweet_text polarity tweet_sentiment More 1000 tweets are collected 3. Naive Bayes and Logistics regression are applied, Plots are arrived For the taken dataset, x-train and x-test and y-train & y-test are arrived. from which te…

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Twitter sentiment analysis using five machine learning techniques

Twitter Sentiment Analysis using 5 Machine learning Techniques Implementation Details: ———————– 1. Twitter data is collected for topic “apple” and stored as twitter.json file. The data will be added in the same file for execution of Twitterdata.py 2. Collected tweets from json file is extracted stored as tweet.csv data extracted from each tweet are tweet_id tweet_time tweet_author tweet_author_id tweet_language tweet_text polarity tweet_sentiment More 1000 tweets are collected 3. 5 machine learning techniques were applied 1.Naive Bayes 2.Logistics regression 3.SVM technique 4.Random forest and 5.K-means Clustering Plots are arrived For the…

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