chvrajput,Chhavi rajput,github

advance-packages-of-r

There are some which are very useful if you are a fan of R. while writing a code line by line for exploratory Data Analytics these libraries helps us to do so in one line of code.

audio-mining-in-python

In this repo I'm taking an audio file to generate its transcript by using Liv.ai API's. You can use any API or tools to generate transcript. On generated output I am going to do text mining.

building-and-applying-a-logistic-regression-spam-model-in-r

Evaluating Classification model A classification model places examples into two or more categories. The most common measure of classifier quality is accuracy and the incredible tool called the confusion matrix.

exploratory-data-analysis-with-r

Data Analysis on wine data. First we merge the data of red wine and white wine then start analysing it. We are going to use ggplot and ggthemes to plot the data, corrplot to find the correlation of variables, wine quality distribution and apply random forest model and variable importance.

facebook-messenger-bot

A demo fb messenger bot

naive-bayes-classifier

Naive Bayes Classifier on text data. It is an example of multiclass classification and on a real scenerio. This is data of ivr while playing when we are not ableto receive the call or number is switchedoff ,unavailable and Busy etc. Mostly cases I am covered in it and this is very interesting.

python-tutorial

Python Practice from Coursera- All the tutorials are created under spyder environment. From basic to advance python tutorials will covered in this repo with detailed explanation of functions, input method, strings, loops, lists etc.

quick-analysis-in-r-with-the-iris-dataset

The Iris dataset is part of the datasets library. We can access it as a data frame by loading the library, that will also load the data frame by attach(iris) and analyse the data of iris.

sentiment-analysis-with-python

Finding the positive sentiment and negative sentiment with the help of python and NLTK.

text-mining

Text mining in R, wordcloud

visualisation-with-tensorboard

So what is TensorBoard and why would we want to use it? TensorBoard is a suite of web applications for inspecting and understanding your TensorFlow runs and graphs. TensorBoard currently supports five visualizations: scalars, images, audio, histograms, and graphs. The computations you will use in TensorFlow for things such as training a massive deep neural network, can be fairly complex and confusing, TensorBoard will make this a lot easier to understand, debug, and optimize your TensorFlow programs.

webscraping

beautifulSoup in python

xgboost-with-combination-of-factor.

XGBoost (eXtreme Gradient Boosting) is an advanced implementation of gradient boosting algorithm.Xgboost is short for eXtreme Gradient Boosting package. The purpose of this Vignette is to show you how to use Xgboost to build a model and make predictions. It is an efficient and scalable implementation of gradient boosting framework by J. Friedman et al. (2000) and J. H. Friedman (2001). Two solvers are included: linear model ; tree learning algorithm. It supports various objective functions, including regression, classification and ranking. The package is made to be extendable, so that users are also allowed to define their own objective functions easily.

chvrajput Goto Github PK

Chhavi rajput's Projects

advance-packages-of-r

audio-mining-in-python

building-and-applying-a-logistic-regression-spam-model-in-r

exploratory-data-analysis-with-r

facebook-messenger-bot

naive-bayes-classifier

python-tutorial

quick-analysis-in-r-with-the-iris-dataset

sentiment-analysis-with-python

text-mining

visualisation-with-tensorboard

webscraping

xgboost-with-combination-of-factor.

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