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Hello, my name is Kiran, and I am a transitioning professional looking to start a career in data science. I have recently completed a comprehensive data science program from INSAID to learn the necessary skills and techniques for data analysis and machine learning.

  • Prior to this, I pursued a career in optometry and worked as an optometrist for several years. Although I enjoyed helping patients with their vision, I always had a strong interest in analyzing data and using it to drive decision-making.
  • I have hands-on experience with programming language such as Python and its Library. I have completed several data science projects that involved data cleaning, exploratory data analysis, Predictive modelling and data visualization.
  • My experience in optometry has given me valuable skills such as attention to detail, critical thinking, and the ability to communicate complex ideas in simple terms. I am now eager to apply these skills to the field of data science and contribute to solving real-world problems.
  • In my free time, I enjoy reading about new developments in data science and participating in online communities to enhance my knowledge and skills. I am a quick learner, a team player, and I believe in continuous learning and improvement.
  • Please feel free to reach out to me at [email protected] to discuss any opportunities or collaborations. Thank you for taking the time to read my profile!

Kiran Bansal's Projects

exploratory-data-analysis-on-movies-dataset icon exploratory-data-analysis-on-movies-dataset

The EDA Movies Dataset repository is a project that aims to explore and analyze a dataset containing information about movies. The dataset contains various attributes such as the title, director, genre, release year, and ratings of movies.

hotel-booking-cancellation-prediction icon hotel-booking-cancellation-prediction

This is a project to predict hotel booking cancellations using multiple model. The goal of this project is to build a predictive model that can help hotel managers better understand which bookings are more likely to be cancelled, so that they can take proactive measures to mitigate cancellations and improve their business operations.

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