akshaykapoor347,akshay kapoor,github

13-reasons-why-suicide-timeseries-analysis

We extract data from google trends based on the term suicide and terms related to it from 2017-01-15 to 2017-03-30. We then apply ARIMA model on it to the to forecast the values for the next 19 days. We then compare it with the actual values of the search results to see the accuracy of the forecast. We check for the stationarity of the series and then use acf and pacf to decide p,d and q values of our arima model. We also apply auto arima and compare our forecast results with the real results. We are basically recreating the analysis performed by the John W. Ayers to see whether there was significant increase in the search results related to the suicide query.

akshaykapoor347.github.io

Portfolio Website

beautifulsoupscrapping

Use of Beautiful Soup to extract data from a web page tutorial from analytics vidhya

breast-cancer-detection

We have 30 different attributes from images extracted, Features are computed from a digitized image of a fine needle aspirate (FNA) of a breast mass. They describe characteristics of the cell nuclei present in the image. We predict the Stage of Breast Cancer B (Bengin) or M (malignant).

building-a-simple-chatbot-in-python-using-nltk

Building a Simple Chatbot from Scratch in Python (using NLTK)

classification-and-clustering-using-r-and-python

Implementation of different types of classification and clustering using R and Python on IRIS dataset.

compute-auc-roc-from-scratch-python

Computing AUC ROC from scratch in python without using any libraries

credit-card-fraud-detection

Predicting whether a transaction is fraudulent or not using machine learning.

data-science-challenge

Data science challenge for Tagup applicants

data-science-question-answer

A repo for data science related questions and answers

easy-application

345 software engineering companies that are easy to apply to

eda-on-rubber-oddbooks-and-trees-datasets

Exlporatory data analysis on Rubber, Oddbooks and Trees datasets using R

eda-scratch-mit-dataset-

Exploratory data analysis on scratch MIT dataset using R shiny and Tableau

hello-world

hello

house-price-index-advanced-regression

Advanced regression techniques for house prices dataset with use of ensemble techniques such as Stacking, averaging, Gradient Boosting, XGB, Light GBM, ElasticNet.

house-prices-king-county

Regression analysis on House prices.

hypothesis-testing-using-r

Develop an understanding of Hypothesis testing using R

image-recognition-using-convolution-neural-network-

This project is about the implementation of image recognition using convolution neural networks. We make use of TensorFlow, Theano and keras deep learning libraries to build the convolution neural network and implement image recognition.

imdbreviews

Web scrapped reviews from IMDB for Game of thrones and performed Sentiment Analysis Using NLP.

kenoanalysis

The analysis focuses on Caveman Keno game which is a variation of traditional Keno Game. It is a popular game which is played online or in gambling casinos extensively. This variation of Keno adds drawing of 3 numbers by the computer which become dinosaur eggs. If two or three of the eggs match among the 20 numbers drawn, the payouts are then multiplied. The analysis of this project is application of Hypergeometric distribution in a real-world game.

lifestory-rshiny-application

Interactive R shiny application for analysis of LifeStory data on genetics and women health. Live Application link: https://akshaykapoor347.shinyapps.io/week_1/

loan-prediction

Predicting whether a load would be provided or not based on the credit history, income and other factors.

malware-detection

Malware detection using Machine learning

marchmadness2017

Predicting the NCAA tournament using PyStan

markov_clustering

markov clustering in python

nlpclothingreview

I have worked on the analysis of reviews of an ecommerce clothing website where I have performed EDA and sentimental analysis. For sentiment analysis, I performed cleaning on it like removing the punctuation and the stop words from it, then tokenizing and like removing words which were not important like which have length less than 3. I performed analysis such as finding the most common words used in a review. (dress, size, love, like, top) Then made use of text blob to find the sentiment of the reviews and created a list of most commonly used words in positive review and a negative review. Then used a classification algorithm like naïve Bayes to train the model to rate to a review and tested it on the new data. Count vectorizer Results: 1) Reviews with 3 and 4-star rating had the longest reviews. 2) Users shopped for tops 60 percent more than bottoms 3) Got 85 percent accuracy in the naïve bayes model.

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