Shivam G.

Shivam G.

I am a data science enthusiast and passionate about solving real-world problems using data.

Bangalore , India

Experience: 2 Years

Shivam

Bangalore , India

I am a data science enthusiast and passionate about solving real-world problems using data.

25028.5 USD / Year

  • Immediate: Available

2 Years

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About Me

I am a data science enthusiast and passionate about solving real-world problems using data. Skilled in Machine Learning, Natural Language Processing, Text Mining, Information Retrieval using, Python, NLTK. I have skills in ...

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Portfolio Projects

Dstl Satellite Imagery Feature Detection

https://github.com/shivamgupta7

Company

Dstl Satellite Imagery Feature Detection

Description

  • Using Libraries: Pandas, NumPy, SciPy, shapely, sklearn, cv2, Tensorflow, Keras
  • Dstl provides you with 1km x 1km satellite images in both 3-band and 16-band formats. The goal is to detect and classify the types of objects found in these regions.
  • Every object class is described in the form of Polygons and MultiPolygons, which are simply a list of polygons. We provide two different formats for these shapes: GeoJson and WKT. These are both open source formats for geospatial shapes.
  • Apply U-Net architecture to solve the image segmentation problem.

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Santander Customer Transaction Prediction

https://github.com/shivamgupta7

Company

Santander Customer Transaction Prediction

Description

  • Using libraries: Pandas, NumPy, SciPy, Matplotlib, sklearn, sci-kit-learn, Model Selection
  • we can more accurately identify new ways to solve our most common challenge, binary classification problems such as: is a customer satisfied? Will a customer buy this product? Can a customer pay this loan?
  • Data cleaning, visualization and creating data pipeline for modeling.
  • Using LightGBM and Catboost Model.

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Company

Human Activity Recognition

Description

  • Using libraries: Pandas, NumPy, SciPy, Matplotlib, sklearn, sci-kit-learn, Tensorflow, Keras, Talos
  • This project is to build a model that predicts human activities such as Walking, Walking_Upstairs, Walking_Downstairs, Sitting, Standing or Laying.
  • There are a total of 561 features and doing EDA on datasets.
  • Logistic regression or Linear SVM or RBF SVM and LSTM(1 layer or 2 layers) given the best accuracy.

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