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Classifying 'Survived' and 'Not Survived' from Titanic dataset using Machine Learning algorithms such as Gradient Boosting, Random Forest, Multi Layer Perceptron, SVM and Logistic Regression.

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data-science gradient-boosting multilayer-perceptron random-forest support-vector-machine titanic-kaggle

machine-learning-algorithms's Introduction

Hi, I'm Harsh ๐Ÿ‘‹๐Ÿป ๐Ÿ‘จ๐Ÿปโ€๐Ÿ’ป

banner that says Harsh Tandon - Data Scientist, converting data into value alongside a cartoon illustration of Harsh

Iโ€™m a budding Data Scientist adept in collecting, analyzing, and transforming data into insights. My background in Information Technology and Data Analytics provides me with extensive analytical skills to wrestle with large & complex data. I am currently pursuing a Master of Science degree in Business Analytics at Santa Clara University and my passion for Data Science allows me to provide data-driven, action-oriented solutions to challenging business problems.

With deep expertise in Machine Learning, Data Mining, and Statistical Analysis, Iโ€™m also proficient in Marketing Analytics, NLP, Deep Learning, and Data Visualization. I am excited to walk the uncanny valley of research and development, and I enjoy learning new things while thriving in challenging environments.

The world today is facing a situation of data deluge, and I believe, through my passion and skills, I can unlock its significant potential and aid businesses into efficient forecasting and planning.

When Iโ€™m not studying/working ๐Ÿ“š, I love to travel โœˆ๏ธ, explore the world ๐Ÿ•๏ธ, sketch ๐ŸŽจ and workout ๐Ÿ‹๏ธโ€โ™‚๏ธ.

Languages and Tools:

Python

Jupyter

R

SQL

Spark

Spyder

Plotly

Tableau

Kubernetes


Find me around the web ๐ŸŒŽ:

Harsh Tandon | LinkedIn Harsh Tandon | Gmail Harsh Tandon | Gmail Harsh Tandon | Instagram

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