Mellogwayo

Mellogwayo's Stars

• chakaya/Synthetic-Data-Generation

  Synthetic Data for Electronic Health Records, Generation and Benchmarking methods intern project summer 2024

  1

• jparep/housePricePrediction-nn-automated

  Automate the house price prediction in neural network

  Language:Python1

• UKDataServiceOpen/Crime_Data_in_R

  This is a repository collecting all the materials, code, resources and extras for a UKDS workshop on Crime Data in R.

  Language:HTML238

• UKDataServiceOpen/web-scraping

  Materials associated with the Web-scraping for Social Science Research training series

  Language:Jupyter Notebook2515

• aws-solutions-library-samples/fraud-detection-using-machine-learning

  Setup end to end demo architecture for predicting fraud events with Machine Learning using Amazon SageMaker

  Language:Jupyter Notebook294156

• Kaedim/working-at-kaedim

  Open roles at Kaedim and a summary of what it's like to work with us.

  181

• AmandaZou/Data-Science-books-

  A repository of books in data science

  452204

• UKDataServiceOpen/text-mining

  Materials associated with theText-mining for Social Science Research training series

  Language:Jupyter Notebook236

• KuberKhandelwal/Final-Project-Classification

  Your client is a retail banking institution. Term deposits are a major source of income for a bank. A term deposit is a cash investment held at a financial institution. Your money is invested for an agreed rate of interest over a fixed amount of time, or term. The bank has various outreach plans to sell term deposits to their customers such as email marketing, advertisements, telephonic marketing and digital marketing. Telephonic marketing campaigns still remain one of the most effective way to reach out to people. However, they require huge investment as large call centers are hired to actually execute these campaigns. Hence, it is crucial to identify the customers most likely to convert beforehand so that they can be specifically targeted via call. You are provided with the client data such as : age of the client, their job type, their marital status, etc. Along with the client data, you are also provided with the information of the call such as the duration of the call, day and month of the call, etc. Given this information, your task is to predict if the client will subscribe to term deposit.

  Language:Jupyter Notebook1

• MTrajK/coding-problems

  Solutions for various coding/algorithmic problems and many useful resources for learning algorithms and data structures

  Language:Python3.3k614

• TejVed/Hybrid-LSTM-GARCHE-model-for-NASDAQ-Stock-Prediction-Forecasting

  To create a data-web application deployed using the azure app service, which was made on Streamlit, the leading Pythonic data application service. On this website, we display candlestick plots of various stocks listed on the Nasdac, according to the option of the user; and utilize the Garch based time forecasting algorithm done using Seasonal arima model and conduct a virtual future prediction for the given stock, so as to be able to conduct non-pairs algorithmic trading using time forecasting and Garch-based deep learning.

  Language:Python104

• yitaohu88/Empirical-Method-in-Finance

  Winter 2020 Course description: Econometric and statistical techniques commonly used in quantitative finance. Use of estimation application software in exercises to estimate volatility, correlations, stability, regressions, and statistical inference using financial time series. Topic 1: Time series properties of stock market returns and prices  Class intro: Forecasting and Finance  The random walk hypothesis  Stationarity  Time-varying volatility and General Least Squares  Robust standard errors and OLS Topic 2: Time-dependence and predictability  ARMA models  The likelihood function, exact and conditional likelihood estimation  Predictive regressions, autocorrelation robust standard errors  The Campbell-Shiller decomposition  Present value restrictions  Multivariate analysis: Vector Autoregression (VAR) models, the Kalman Filter Topic 3: Heteroscedasticity  Time-varying volatility in the data  Realized Variance  ARCH and GARCH models, application to Value-at-Risk Topic 4: Time series properties of the cross-section of stock returns  Single- and multifactor models  Economic factors: Models and data exploration  Statistical factors: Principal Components Analysis  Fama-MacBeth regressions and characteristics-based factors

  Language:R3720

• zzp1012/tesla-stock-prediction

  Explore TESLA stock price (time-series) using ARIMA & GARCH model.

  Language:Python178

• Cyrille-Sandry/Quantitative-Risk-Management

  This project focuses on the analysis and the modelisation of the financial time series of the CAC40 returns stock price using python environment. Our analysis is realized in three main parts. We start the first part by a preliminary analysis of the daily closing stock prices and returns of CAC40. The stationarity of the return series is also investigated. The second part intends to fit an appropriate ARMA-GARCH model to the log-returns stock prices of the CAC40 and the last part focuses on using fitted model to predict future returns and prices of the CAC40 stock.

  Language:Jupyter Notebook4

• jrjohansson/scientific-python-lectures

  Lectures on scientific computing with python, as IPython notebooks.

  Language:Jupyter Notebook3.5k1.8k