Pinned Repositories
26-Weeks-Of-Data-Science
Email Newsletter
Advanced-Deep-Learning-with-Keras
Advanced Deep Learning with Keras, published by Packt
AlgoSolutions
:octocat:LeetCode, LintCode, Project Euler, SGU, HackerRank, Cracking the Coding Interview(ctci):palm_tree:
alphalens
Performance analysis of predictive (alpha) stock factors
Analytics
awesome-python
A curated list of awesome Python frameworks, libraries, software and resources
Bootcamp
coronavirus_dashboard
The Coronavirus Dashboard
course-v3
The 3rd edition of course.fast.ai
cs-video-courses
List of Computer Science courses with video lectures.
pramaugit's Repositories
pramaugit/26-Weeks-Of-Data-Science
Email Newsletter
pramaugit/Advanced-Deep-Learning-with-Keras
Advanced Deep Learning with Keras, published by Packt
pramaugit/AlgoSolutions
:octocat:LeetCode, LintCode, Project Euler, SGU, HackerRank, Cracking the Coding Interview(ctci):palm_tree:
pramaugit/alphalens
Performance analysis of predictive (alpha) stock factors
pramaugit/Analytics
pramaugit/awesome-python
A curated list of awesome Python frameworks, libraries, software and resources
pramaugit/coronavirus_dashboard
The Coronavirus Dashboard
pramaugit/course-v3
The 3rd edition of course.fast.ai
pramaugit/cs-video-courses
List of Computer Science courses with video lectures.
pramaugit/data-science-ipython-notebooks
Data science Python notebooks: Deep learning (TensorFlow, Theano, Caffe, Keras), scikit-learn, Kaggle, big data (Spark, Hadoop MapReduce, HDFS), matplotlib, pandas, NumPy, SciPy, Python essentials, AWS, and various command lines.
pramaugit/data-structures-algorithms-python
This tutorial playlist covers data structures and algorithms in python. Every tutorial has theory behind data structure or an algorithm, BIG O Complexity analysis and exercises that you can practice on.
pramaugit/helpful
Helpful commands and code for different applications and use cases
pramaugit/interactive-coding-challenges
120+ interactive Python coding interview challenges (algorithms and data structures). Includes Anki flashcards.
pramaugit/lists
The definitive list of lists (of lists) curated on GitHub and elsewhere
pramaugit/machine-learning-for-software-engineers
A complete daily plan for studying to become a machine learning engineer.
pramaugit/Machine-Learning-with-Python
Python code for common Machine Learning Algorithms
pramaugit/MachineLearning
pramaugit/Natural-Language-Processing-with-AWS-AI-Services
Natural Language Processing with AWS AI Services
pramaugit/nlp_course
YSDA course in Natural Language Processing
pramaugit/pyfolio
Portfolio and risk analytics in Python
pramaugit/Python
All Algorithms implemented in Python
pramaugit/Python-for-Algorithms--Data-Structures--and-Interviews
Files for Udemy Course on Algorithms and Data Structures
pramaugit/QuantEcon.py
A community based Python library for quantitative economics
pramaugit/quantstats
Portfolio analytics for quants, written in Python
pramaugit/Real-time-stock-market-prediction
In this repository, I have developed the entire server-side principal architecture for real-time stock market prediction with Machine Learning. I have used Tensorflow.js for constructing ml model architecture, and Kafka for real-time data streaming and pipelining.
pramaugit/SA2021_W5
pramaugit/stanford-cs-229-machine-learning
VIP cheatsheets for Stanford's CS 229 Machine Learning
pramaugit/Stanford-Project-Predicting-stock-prices-using-a-LSTM-Network
Stanford Project: Artificial Intelligence is changing virtually every aspect of our lives. Today’s algorithms accomplish tasks that until recently only expert humans could perform. As it relates to finance, this is an exciting time to adopt a disruptive technology that will transform how everyone invests for generations. Models that explain the returns of individual stocks generally use company and stock characteristics, e.g., the market prices of financial instruments and companies’ accounting data. These characteristics can also be used to predict expected stock returns out-of-sample. Most studies use simple linear models to form these predictions [1] or [2]. An increasing body of academic literature documents that more sophisticated tools from the Machine Learning (ML) and Deep Learning (DL) repertoire, which allow for nonlinear predictor interactions, can improve the stock return forecasts [3], [4] or [5]. The main goal of this project is to investigate whether modern DL techniques can be utilized to more efficiently predict the movements of the stock market. Specifically, we train a LSTM neural network with time series price-volume data and compare its out-of-sample return predictability with the performance of a simple logistic regression (our baseline model).
pramaugit/Stock-Market-Prediction-Web-App-using-Machine-Learning-And-Sentiment-Analysis
Stock Market Prediction Web App based on Machine Learning and Sentiment Analysis of Tweets (API keys included in code). The front end of the Web App is based on Flask and Wordpress. The App forecasts stock prices of the next seven days for any given stock under NASDAQ or NSE as input by the user. Predictions are made using three algorithms: ARIMA, LSTM, Linear Regression. The Web App combines the predicted prices of the next seven days with the sentiment analysis of tweets to give recommendation whether the price is going to rise or fall
pramaugit/zipline
Zipline, a Pythonic Algorithmic Trading Library