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I recently found another disease detection dataset on the UC Irvine Machine Learning Repository and decided to have a go at this one. However this dataset is not used that often for disease detection as I have never heard of it before and never even came across it before discovering it while reading a research paper. I'd like to shed some light on this dataset and present the Mesothelioma Disease Detector Web App. This web app detects if you have Mesothelioma or not depending upon various parameters such as  Platelet Count,  Blood Lactic Dehydrogenise,  Alkaline Phosphatise,  Total Protein,  Albumin,  Glucose,  Pleural Lactic Dehydrogenise,  Pleural Protein,  Pleural Albumin,  Pleural Glucose and  C-reactive Protein. I'll explain more about these parameters later on in the article. This web app works on the Random Forest Classifier algorithm and is majorly coded in python.  Mesothelioma is a type of cancer t hat occurs in the thin layer of tissue that covers the majority of our

 My fascination for machine learning apparently never seems to fade away and here I am with another app related to disease detection in machine learning. I created this and the last web app together after discovering both those datasets and successfully deployed both of them. This app is a hepatic disease detector and detects if you have a liver disease using machine learning.  This web app predicts if you have Hepatitis, Fibrosis, Cirrhosis or no hepatic disease based upon a number of parameters such as age, Albumin,  Alkaline Phosphate,  Alanine Aminotransferase,  Aspartate Aminotransferase,  Bilirubin,  Serum Cholinesterase,  Cholestrol,  Creatinine,  Gamma-Glutamyl Transferase and  Prothrombin. I will explain the meaning of these terms as you read through the article. This web app works on the random forest classifier algorithm and is coded majorly in python.  We have deployed the app using Streamlit. It is an open source framework that allows data science teams to deploy web apps

  Among the many applications of machine learning, one is of particular interest to me. The use of disease detection in machine learning has the potential to help a large number of people in the world and the advent of machine learning and computer vision in the past few years have definitely transformed the fields of medicine, finance, biotechnology and more. The use of disease detection methods using machine learning and computer vision has a number of applications in the medical sector and its use is only expected to grow exponentially as we develop better methods and models.  he value of machine learning in healthcare is its ability to process huge datasets beyond the scope of human capability, and then reliably convert analysis of that data into clinical insights that aid physicians in planning and providing care, ultimately leading to better outcomes, lower costs of care, and increased patient satisfaction.   Many leading tech companies and universities have been doing research o

 In this article and web app, we are going to talk about data science in its true meaning- the analysis of big data, its visualization and representation and schematic analysis of that collected data. One of my first projects in Data Science was related to data analysis and specifically, exploratory data analysis using different libraries. I'm going to tell you about one such exploratory data analysis using the Pandas Profiling Report. If you've taken any courses on statistics (and by that I mean certain advanced courses that touch upon topics like probability distribution, Gaussian functions and normal distributions) you would have come across data analysis at some point or he other. Exploratory data analysis  is an approach of  analyzing   data  sets  to summarize their main characteristics, often using  statistical graphics  and other  data visualization  methods. It was first proposed by an American mathematician John Tukey (who was also known for his notable work in Fast F

Data science is spread across our lives and we are surrounded by its applications. We unknowingly use the vast number of applications of data science and machine learning such as recommender systems, house price prediction, data monitoring and data analysis etc. We are in fact surrounded by data and probably have played around with it many a times (especially if you're a math loving person). So here's another web app in yet another vital application of data science. This is a financial stock price web app. It shows the closing financial stock price values for S and P 500 companies. S and P 500 companies are 500 of the largest companies listed on stock exchanges in the US. The S and P 500 stock market index comprises of 505 common stocks issued by 500 large cap companies. The stocks of these companies trade either on the NYSE (New York Stock Exchange) or NASDAQ (National Association of Securities Dealers Automated Quotations). NYSE is an auction market whereas NASDAQ is a dealer