anudeepvanjavakam1/lit_or_not_on_reddit

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

Language: Jupyter Notebook - Size: 33.1 MB - Last synced at: about 1 month ago - Pushed at: about 1 month ago - Stars: 2 - Forks: 0

anudeepvanjavakam1/Time-Series-Analysis

Exploring Time Series in R - This is an exploration of time series analysis that includes moving average, holt-winters smoothing, and ARIMA models.

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anudeepvanjavakam1/churn_prediction

A flask app to predict customer churn for a subscription service business

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anudeepvanjavakam1/ML_zoomcamp

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anudeepvanjavakam1/anudeepvanjavakam1

Anudeep's GitHub Profile

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anudeepvanjavakam1/anudeepvanjavakam1.github.io Fork of hugo-toha/hugo-toha.github.io

My Portfolio - hugo static site with Toha theme

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anudeepvanjavakam1/ImageClassifier-Pytorch

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.

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anudeepvanjavakam1/Income-Classification-of-people-in-US

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 naïve Bayes, XGBoost and SVM models for classification

Language: R - Size: 1.4 MB - Last synced at: about 2 years ago - Pushed at: over 8 years ago - Stars: 0 - Forks: 1

anudeepvanjavakam1/video_games_rating_analysis

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.

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anudeepvanjavakam1/Recommendations_with_IBM

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.

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anudeepvanjavakam1/Mobile-Games--Cookie-Cats--A_B-Testing-with-bootstrap-analysis

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.

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anudeepvanjavakam1/Mobile-Games-Cookie-Cats-A_B-Testing-with-bootstrap-analysis

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.

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anudeepvanjavakam1/disaster_response_NLP

This Project is part of Data Science Nanodegree Program by Udacity in collaboration with Figure Eight. The dataset contains pre-labelled tweet 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.

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anudeepvanjavakam1/stackoverflow Fork of jjrunner/stackoverflow

Findings from Stackoverflow 2017

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anudeepvanjavakam1/course-collaboration-travel-plans Fork of udacity/course-collaboration-travel-plans

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anudeepvanjavakam1/MarketMatching Fork of christophertull/MarketMatching

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anudeepvanjavakam1/greengov-challenge Fork of ucd-cwee/greengov-challenge

Entry into the 2016 CA Water Board Data Innovation Challenge

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anudeepvanjavakam1/awesome-machine-learning Fork of josephmisiti/awesome-machine-learning

A curated list of awesome Machine Learning frameworks, libraries and software.

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anudeepvanjavakam1/ML_for_Hackers Fork of johnmyleswhite/ML_for_Hackers

Code accompanying the book "Machine Learning for Hackers"

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anudeepvanjavakam1/RateComparison Fork of California-Data-Collaborative/RateComparison

Easily compare the revenue, equity, and demand implications of different water rate structures.

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anudeepvanjavakam1/Machine-Learning-Stanford-University

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

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