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HI I'm Hima Rani Mathews

  • 3rd yr B.tech CSE student at VIT,Chennai
  • Currently learning ReactJS and Data Science
  • Aspiring Web Developer + Data Scientist
  • Focusing on #100DaysOfCode
  • Projects that i worked on:

Languages :

C++ C Java Python R programming

Web Technologies :

HTML5 CSS3 JavaScript Bootstrap PHP NODEJS EXPRESSJS MySQL

Hosting :

Heroku Netlify Microsoft Azure Github

ML/Data Analytics/AI :

Tableau OpenCV Numpy Pandas Scikit_learn Plotly

Hima Rani Mathews's Activity Graph

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Hima Rani Mathews's Projects

100daysofcode icon 100daysofcode

#100DaysOfCode Main Focus on MERN Stack Development, HTML, CSS, JS, JQUERY, REACTJS, NODEJS, EXPRESSJS, MongoDB

breast-cancer-prediciton icon breast-cancer-prediciton

Prediction of Breast Cancer using ML models like Logistic Regression, Random Forest, Naive Bayes Classifier, KNN model and XGBoost. We have used Breast Cancer Wisconsin (Diagnostic) Data Set by UCI Machine Learning.

home-automation-system icon home-automation-system

Home Automation System : In this project we have developed three different Arduino projects, i.e., Smoke Detection System, Motion controlled LEDs and Door Lock System.

js-music-player icon js-music-player

Mini-Javascript-Project ,made using HTML,CSS and Javascript. Enjoy the music

petsworld-ecommerce_website icon petsworld-ecommerce_website

PetsWorld - an Ecommerce Website. Internet and Web Programming Project ,made using HTML, CSS, Javascript, Bootstrap, Mysql and PHP

sentiment-analysis icon sentiment-analysis

Sentiment Analysis Project is done in English and Malayalam. The objective was to find out the negative and positive comments.

the-sparks-foundation-gripnovember21 icon the-sparks-foundation-gripnovember21

The Sparks Foundation - Data & Business analytics Internship Tasks. TASKS : (1) Task3 : Exploratory Data Analysis- Retail (2) Task6: Prediction using Decision Tree Algorithm

twitter-sentiment-analysis icon twitter-sentiment-analysis

Data Analytics Project on Twitter Sentiment Analysis. Have used Logistic Regression, Random Forest, and XGBoost Models on Bag-of-Words, TF-IDF, and Word2Vec features

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