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  • 👋 Hi, I’m @Isha03Shukla
  • 👀 I’m interested in Machine Learning, Java, C and Python.
  • 💞️ I’m looking to collaborate on Data Science.
  • 📫 How to reach me [email protected]

Isha Shukla's Projects

-online-retail-orders-analysis icon -online-retail-orders-analysis

You are hired by a chain of online retail stores “Reliant retail limited”. They provide you with “orders” database and seek answers to the following queries as the results from these queries will help the company in making data-driven decisions that will impact the overall growth of the online retail store.

awesome-datascience icon awesome-datascience

:memo: An awesome Data Science repository to learn and apply for real world problems.

breast-cancer-prediction---comparing-models icon breast-cancer-prediction---comparing-models

Features are computed from a digitized image of a fine need aspirate (FNA) of a breast mass. The features describe the characteristics of the cell nuclei present in the image. Attribute information: 1) ID number 2) Diagnosis (M = malignant, B = benign) 3-32) Ten real-valued features are computed for each cell nucleus: a) radius (mean of distances from center to points on the perimeter) b) texture (standard deviation of gray-scale values) c) perimeter d) area e) smoothness (local variation in radius lengths) f) compactness (perimeter^2 / area - 1.0) g) concavity (severity of concave portions of the contour) h) concave points (number of concave portions of the contour) i) symmetry j) fractal dimension ("coastline approximation" - 1) The mean, standard error and "worst" or largest (mean of the three largest values) of these features were computed for each image, resulting in 30 features. For instance, field 3 is Mean Radius, field 13 is Radius SE, field 23 is Worst Radius. All feature values are recoded with four significant digits. Missing attribute values: none Class distribution: 357 benign, 212 malignant

drug-seizues-annually-since-1970s icon drug-seizues-annually-since-1970s

THE USE OF DRUGS AND GETTING CAUGHT IS AN OLD ONE. Catch this new dataset to get some knowledge of the world's greatest drug seizues. The dataset has 7 columns all categorized by their region. Try dataset to salute the world's various drug enforcement departments.

email-spam-classification icon email-spam-classification

Email Spam Classification using Scikit-learn to build a SVM classifier that can detect or classify emails as Spam or not spam(Ham).

hacktoberfest-2021 icon hacktoberfest-2021

Make this Hacktoberfest a learning period and contribute to Great Open Source Projects.

hacktoberfest-2022 icon hacktoberfest-2022

This repo will consist merge from the all participants of hacktoberfest powered by DigitalOcean.

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