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  • šŸ‘‹ Hi, Iā€™m Rishiraj R. Dhokiya
  • šŸ‘€ Iā€™m interested in Data Science and machine learning with python
  • šŸŒ± Iā€™m currently working in the field of Data Science and ArtificialIntelligence
  • šŸ’žļø Iā€™m looking to collaborate on Github

Rishiraj's Projects

fare-price-prediction-using-rf icon fare-price-prediction-using-rf

In the last few years, the number of for-hire vehicles operating in NY has grown from 63,000 to more than 100,000. However, while the number of trips in app-based vehicles has increased from 6 million to 17 million a year, taxi trips have fallen from 11 million to 8.5 million. Hence, the NY Yellow Cab organization decided to become more data-centric. Then we have apps like Uber, OLA, Lyft, Gett, etc. how do these apps work? After all, that set price is not a random guess.

h1n1-vaccine-prediction-using-logistic-regression icon h1n1-vaccine-prediction-using-logistic-regression

Subjects receiving the same vaccine often show different levels of immune responses and some may even present adverse side effects to the vaccine. Systems vaccinology can combine omics data and machine learning techniques to obtain highly predictive signatures of vaccine immunogenicity and reactogenicity. Currently, several machine learning methods are already available to researchers with no background in bioinformatics.

heart-disease-prediction icon heart-disease-prediction

Heart disease is easier to treat when it is detected in the early stages. Machine learning techniques may aid a more efficient analysis in the prediction of the disease. Moreover, this prediction is one of the most central problems in medical, as it is one of the leading disease related to unhealthy lifestyle. So, an early prediction of this disease will be useful for a cure or averion. In this study, we experiment with the heart disease dataset to explore the machine learning algorithms and build an optimum model to predict the disease.Heart disease is easier to treat when it is detected in the early stages. Machine learning techniques may aid a more efficient analysis in the prediction of the disease. Moreover, this prediction is one of the most central problems in medical, as it is one of the leading disease related to unhealthy lifestyle. So, an early prediction of this disease will be useful for a cure or averion. In this study, we experiment with the heart disease dataset to explore the machine learning algorithms and build an optimum model to predict the disease.

market-positioning-of-mobile-using-knn icon market-positioning-of-mobile-using-knn

Breast cancer represents one of the diseases that make a high number of deaths every year. It is the most common type of all cancers and the main cause of women's deaths worldwide. Classification and data mining methods are an effective way to classify data. Especially in the medical field, where those methods are widely used in diagnosis and analysis to make decisions.

stock-close-price-prediction icon stock-close-price-prediction

The Dow Jones Industrial Average (DJIA), is a stock market index that shows how 30 large, publicly owned companies based in the United States have traded during a standard trading session in the stock market. The value of the Dow is not a weighted arithmetic mean and does not represent its component companies' market capitalization, but rather the sum of the price of one share of stock for each component company. The sum is corrected by a factor which changes whenever one of the component stocks has a stock split or stock dividend, so as to generate a consistent value for the index. It is the second-oldest U.S. market index after the Dow Jones Transportation Average, created by Wall Street Journal editor and Dow Jones & Company co-founder Charles Dow. Currently owned by S&P Dow Jones Indices, which is majority owned by S&P Global, it is the best known of the Dow Averages, of which the first (non-industrial) was originally published on February 16, 1885.

vehicle-performance-prediction icon vehicle-performance-prediction

Most players in the automotive sector are investing in ML for their marketing efforts, a much smaller group is putting in place incentives and key performance indicators (KPIs) to use more ML and automation. Closing the gap requires a stronger commitment to developing ML capability that is not just useful but also used.

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