Data science

Summary

Retail Analysis with Walmart Data using R
Analyzed the Walmart data using statistics and built a prediction model to find forecast demand.
Applied Linear Regression for the model predictions.
Reduce the time a Mercedes-Benz spends on the test bench
Performed linear dimensionality reduction using Singular Value Decomposition of the data to project it to the lower dimensions.
Predicted the optimum time required for Mercedes Benz configured vehicle testing time using Regression with Lasso model, Ridge Regression model, and Regression with Extreme Gradient Boost(xgboost).
Income Qualification
Predicted the level of income qualification needed for the families in Latin America with an accuracy of 94.5 using the RandomForestClassifier model.
Real Estate Analysis
I built a Linear Regression model to predict the total monthly expenditure for the home mortgage loan with an overall R-squared value of the linear regression model of 0.735.

Expectations

1) My job is expected to enable me to live a no-average life. It should enable me to meet my demands, yes, for every demand needs a supply, and in my case, my job should be able to supply for my demands and make me quit simple and average life.
2) It's expected to better me in different angles, my personality, and view of things inclusivemy job should generate my ideas and knowledge because learning should be part of the whole process

Employment Preferences
Expected Base Salary

**0,000 INR / year

Academic Degree
Experience

Total Professional Experience

no experience

Startup Experience

no experience

Big-Tech Companies

no experience

Enterprise Experience

no experience
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