She is an ML enthusiast and has a keen interest in Data Science and Analytics. She likes to solve various problems by thinking out of the box for possible solutions. She finds it very interesting to analyze the data and find hidden insights & patterns and to know more about the data from a different perspective.
To get the experience and knowledge she has done various projects related to Data Analysis. She has an experience in analyzing and visualizing the data using Python libraries such as pandas, numpy, matplotlib, plotly. She also has exposure to Scikit-learn library for doing machine learning tasks like predicting an airbnb price, classifying the images into various categories, segmenting or clustering the customers/users based on their purchases/likes/dislikes etc.
Here is her Resume and brief introduction letter for more information about her.
It includes all the assignments of Machine Learning Course from AndrewNg by Coursera.
It includes all the assignments of Data Science Specialization Course which contains Introduction to Data Science with Python, Charting, Plotting & Representation of data with Python, Applied ML in Python, Social Network Analysis with Python, Text Mining with Python.
This repository gives you the overview of developing a flask web application and how to recognize speech using python library SpeechRecognition. It tells you how to convert the speech into its corresponding text (or in other words gives you the overview of transcription). With the use of this flask web application, you can upload any audio/speech file to transcribe its text or you can even record your own audio from their itself to get the transcripted text. I have also done some speech analysis or audio analysis like visualizing the Energy graph of audio, Number of words spoken per minute, Number of filler words used in the audio. To know more about the same you can look at my github account or you can directly watch the demonstration video from here where I've described its functions and various features.
This repo includes the exploratory data analysis and interesting visualizations to better understand the data, to understand what the data is trying to saying & finding its hidden insights and patterns.
This repository includes the time series forecasting and analysis of a company's stock market prices using various technical indicators and machine learning algorithms like Linear Regression, Decision Trees, Random Forest, Gradient Boost etc. The metrics used to check accuracy is mean absolute error.
This repository contains the basics of predicting house prices using different machine learning algorithms like Linear Regression, Ridge Regression, Lasso Regression, Random Forest Regressor, Gradient Boosting Regressor using Randomized Search CV and Grid Search CV methods to cross validate the dataset we've. This is the basic Kaggle competition of Getting started with Advanced Regression Techniques. It has a number of features to consider for the prediction.
Many more to come...!!
You can contact to her by mailing her.