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Hi Khalid Siddique here 👋

  • Am a final year Mechanical Engineering student.

  • 🔭 I’m currently mentoring students to learn Data Science and ML.

  • 🌱 I’m currently learning Deep Learning.

  • 💬 Ask me about Data Science and Machine Learning.

  • 📫 Connect with me:Linkedin

Md Khalid Siddique's Projects

airbnb_newyork_2019 icon airbnb_newyork_2019

Airbnb is an online marketplace for arranging or offering lodging, primarily homestays, or tourism experiences since 2008. NYC is the most populous city in the United States and also one of the most popular tourism and business place in the world.

avocado_price_prediction icon avocado_price_prediction

In this study, we will try to see if we can predict the Avocado’s Average Price based on different features. . The features are different (Total Bags,Date,Type,Year,Region…).

ibm-hr_employee_attrition icon ibm-hr_employee_attrition

This code pattern is a high level overview of what to expect in a data science pipeline and tools that can be used along the way. It starts from framing business question to deploying the model. The pipeline is demonstrated through the employee attrition problem.

predicting-compressive-strength-of-concrete icon predicting-compressive-strength-of-concrete

Compressive strength or compression strength is the capacity of a material or structure to withstand loads tending to reduce size, as opposed to tensile strength, which withstands loads tending to elongate. compressive strength is one of the most important engineering properties of concrete. It is a standard industrial practice that the concrete is classified based on grades. This grade is nothing but the Compressive Strength of the concrete cube or cylinder. Cube or Cylinder samples are usually tested under a compression testing machine to obtain the compressive strength of concrete. The test requisites differ country to country based on the design code. The concrete compressive strength is a highly nonlinear function of age and ingredients .These ingredients include cement, blast furnace slag, fly ash, water, superplasticizer, coarse aggregate, and fine aggregate.

tips-dataset-analysis icon tips-dataset-analysis

Food servers’ tips in restaurants may be influenced by many factors, including the nature of the restaurant, size of the party, and table locations in the restaurant. Restaurant managers need to know which factors matter when they assign tables to food servers. For the sake of staff morale, they usually want to avoid either the substance or the appearance of unfair treatment of the servers, for whom tips (at least in restaurants in the United States) are a major component of pay. In one restaurant, a food server recorded the following data on all cus- tomers they served during an interval of two and a half months in early 1990. The restaurant, located in a suburban shopping mall, was part of a national chain and served a varied menu. In observance of local law, the restaurant offered to seat in a non-smoking section to patrons who requested it. Each record includes a day and time, and taken together, they show the server’s work schedule.

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