Diagnosis of liver diseases using machine learning

Diagnosis of liver diseases using machine learning Liver Diseases account for over 2.4% of Indian deaths per annum. Liver disease is also difficult to diagnose in the early stages owing to subtle symptoms. Often the symptoms become apparent when it is too late. This paper aims to improve diagnosis of liver diseases by exploring 2 methods of identification patient parameters and genome expression. The paper also discusses the computational algorithms that can be used in the aforementioned methodology and lists demerits. It proposes methods to improve the efficiency of these…

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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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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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