Name: Anudeep Vanjavakam
Type: User
Company: Moulton Niguel Water District
Bio: Exploring the data universe, one project at a time π | Passionate about AI, analytics, and finding hidden patterns in data π | Let's connect and collaborate!
Twitter: AnudeepVanjavak
Location: California
Blog: www.linkedin.com/in/anudeepvanjavakam
Anudeep Vanjavakam's Projects
Anudeep's GitHub Profile
My Portfolio - hugo static site with Toha theme
A curated list of awesome Machine Learning frameworks, libraries and software.
A flask app to predict customer churn for a subscription service business
This Project is in collaboration with Figure Eight. The dataset contains pre-labelled tweets and messages from real-life disaster events. The project aim is to build a Natural Language Processing (NLP) model to categorize messages on a real time basis.
Entry into the 2016 CA Water Board Data Innovation Challenge
Project code for Udacity's AI Programming with Python Nanodegree program: In this project, I developed code for an image classifier built with PyTorch, then converted it into a command line application.
a predictive model to determine the income level for people in US. Imputed and manipulated large and high dimensional data using data.table in R. Performed SMOTE as the dataset is highly imbalanced. Developed naΓ―ve Bayes, XGBoost and SVM models for classification
This app searches reddit posts and comments to determine if a product or service has a positive or negative sentiment and predicts top product mentions using Named Entity Recognition
Learnt to apply the most advanced machine learning algorithms to problems such as anti-spam, image recognition, clustering, building recommender systems, and many other problems. I am also learning how to select the right algorithm for the right job, as well as becoming expert at 'debugging' and figuring out how to improve a learning algorithm's performance
Code accompanying the book "Machine Learning for Hackers"
Cookie Cats is a hugely popular mobile puzzle game developed by Tactile Entertainment. In this project, we will look at the impact of a in-game feature change on player retention.
Easily compare the revenue, equity, and demand implications of different water rate structures.
In the IBM Watson Studio, there is a large collaborative community ecosystem of articles, datasets, notebooks, and other A.I. and ML. assets. Users of the system interact with all of this. This is a recommendation system project to enhance the user experience and connect them with assets. This personalizes the experience for each user.
Findings from Stackoverflow 2017
Exploring Time Series in R - This is an exploration of time series analysis that includes moving average, holt-winters smoothing, and ARIMA models.
EDA for more than 30K game ratings collected from [IGDB API](https://api-docs.igdb.com/#about) using [igdb-api-v4 for python](https://github.com/twitchtv/igdb-api-python). This notebook explores any common trends for games that have ratings from igdb and external critics.