Mrinal Walia

h2o.ai

Data Scientist

June 22, 2026 – Present

text-2-everything-py

September 3, 2025 – Present

SDK for text-2-everything

Face-Classification-into-Blood-No-Blood

September 17, 2020 – September 22, 2020

Competition

AI-for-Finance-Stocks-real-time-analysis-

April 28, 2020 – October 3, 2023

1. First we fetch data of stocks in realtime from nse India website, perform basis data visualizations using python to analyze the stock. 2. Then we use machine learning LSTM technique to predict the future stock price and at last create an interactive web-app using Streamlit in python.

Detecting-Natural-Diasters-with-Keras-and-Deep-Learning-Kaggle

November 15, 2019 – November 15, 2019

In this project we detect natural disaster in video and photo.

Facial-Expression-Recognition

September 22, 2019 – February 15, 2023

• First we recognize the emotion of the person using Opencv and Keras by training our model on Data provided from Kaggle, program is trained for 30 epochs and we have got an accuracy of 71%. • After detecting the emotion out of 7 labels, we as user for his favourite artist and then recommend songs from Spotify using its API and movies from IMDB depending upon its mood.

Document-Scanner-Using-OpenCV-Python

July 4, 2019 – August 13, 2019

• In this project, you will learn how to extract email and phone number from a business card or any document and save the output in a JSON file. • Initially we need to resize the images so OpenCV can handle it and then the following steps are applied-detecting the edges, finding contours, applying perspective transform to get top-down view, using pytesseract to extract text and then finally using regex expressions to identify only email and phone number.

Deep-Learning-with-OpenCV

April 21, 2019 – May 7, 2019

Deep Learning is a fast growing domain of Machine Learning and if you’re working in the field of computer vision/image processing already (or getting up to speed), it’s a crucial area to explore. With OpenCV 3.3, we can utilize pre-trained networks with popular deep learning frameworks. The fact that they are pre-trained implies that we don’t need to spend many hours training the network — rather we can complete a forward pass and utilize the output to make a decision within our application. OpenCV does not (and does not intend to be) to be a tool for training networks — there are already great frameworks available for that purpose. Since a network (such as a CNN) can be used as a classifier, it makes logical sense that OpenCV has a Deep Learning module that we can leverage easily within the OpenCV ecosystem. Popular network architectures compatible with OpenCV 3.3 include: GoogleLeNet (used in this blog post) AlexNet SqueezeNet VGGNet (and associated flavors) ResNet

fuel-consumption-and-Carbon-dioxide-emission-of-cars

March 28, 2019 – March 28, 2019

In this notebook, we learn how to use scikit-learn to implement simple linear regression. We download a dataset that is related to fuel consumption and Carbon dioxide emission of cars. Then, we split our data into training and test sets, create a model using training set, evaluate your model using test set, and finally use model to predict unknown value.

Python-for-Data-Science-and-Machine-Learning-Bootcamp

November 11, 2018 – May 26, 2019

program with Python, how to create amazing data visualizations, and how to use Machine Learning with Python! Here a just a few of the topics we will be learning: Programming with Python NumPy with Python Using pandas Data Frames to solve complex tasks Use pandas to handle Excel Files Web scraping with python Connect Python to SQL Use matplotlib and seaborn for data visualizations Use plotly for interactive visualizations Machine Learning with SciKit Learn, including: Linear Regression K Nearest Neighbors K Means Clustering Decision Trees Random Forests Natural Language Processing Neural Nets and Deep Learning Support Vector Machines and much, much more!

Web-Scraping-in-Python

October 7, 2018 – December 2, 2019

Implementing Web Scraping in Python with BeautifulSoup There are mainly two ways to extract data from a website: Use the API of the website (if it exists). For example, Facebook has the Facebook Graph API which allows retrieval of data posted on Facebook. Access the HTML of the webpage and extract useful information/data from it. This technique is called web scraping or web harvesting or web data extraction. This article discusses the steps involved in web scraping using implementation of Web Scraping in Python with Beautiful Soup Steps involved in web scraping: Send a HTTP request to the URL of the webpage you want to access. The server responds to the request by returning the HTML content of the webpage. For this task, we will use a third-party HTTP library for python requests. Once we have accessed the HTML content, we are left with the task of parsing the data. Since most of the HTML data is nested, we cannot extract data simply through string processing. One needs a parser which c