Name: Shrawan Kumar Sahu
Type: User
Company: Robert Bosch Engineering and Business Solutions Private Limited
Bio: Excelled in implementing knowledge of Mathematics to solve real world problem with the help of Python in the field of ML and Artificial Intelligence
Location: Bengaluru, India
Blog: https://www.linkedin.com/in/shrawan-kumar-sahu/
Shrawan Kumar Sahu's Projects
A US bike-sharing provider BoomBikes has recently suffered considerable dips in their revenues due to the ongoing Corona pandemic. The company is finding it very difficult to sustain in the current market scenario. So, it has decided to come up with a mindful business plan to be able to accelerate its revenue as soon as the ongoing lockdown comes to an end, and the economy restores to a healthy state.
A Data Science Project
Google Play store Case Study
Virat Kohli Cricket Match statistical Analysis
Let's dive in the world of Data Science and Machine Learning
A Deep Learning project to Identify Hand Gestures to operate SMART TV.
To understand the global trends in investments so that investment decisions can be made effectively. Spark Funds wants to invest where most other investors are investing.
Consumer Finance company which specializes in lending various types of loans to urban customers. When the company receives a loan application, the company has to make a decision for loan approval based on the applicant’s profile.
As one of the world’s biggest manufacturers of premium cars, safety and efficiency are paramount on Daimler’s production lines. However, optimizing the speed of their testing system for many possible feature combinations is complex and time-consuming without a powerful algorithmic approach.
Basic Python conepts
To build a CNN based model which can accurately detect melanoma. Melanoma is a type of cancer that can be deadly if not detected early. It accounts for 75% of skin cancer deaths. A solution which can evaluate images and alert the dermatologists about the presence of melanoma has the potential to reduce a lot of manual effort needed in diagnosis.
Text Classification Problem using Multinomial Naive Bayes Approach
With 21 predictor variables we need to predict whether a particular customer will switch to another telecom provider or not. In telecom terminology, this is referred to as churning and not churning, respectively
LR+SVM+XBOOST+Random Forest