17 skills found
hosseinmoein / DataFrameC++ DataFrame for statistical, financial, and ML analysis in modern C++
neurons-me / This.walletCrypto Wallets for Me. this.wallet structures financial data for seamless interactions within your wallet, enabling machine learning-driven analysis, and intelligent processing of transactions, balances, and financial insights.
shaungt1 / Open Source Datasets For Data ScienceBest free, open-source datasets for data science and machine learning projects. Top government data including census, economic, financial, agricultural, image datasets, labeled and unlabeled, autonomous car datasets, and much more. Data.gov NOAA - https://www.ncdc.noaa.gov/cdo-web/ atmospheric, ocean Bureau of Labor Statistics - https://www.bls.gov/data/ employment, inflation US Census Data - https://www.census.gov/data.html demographics, income, geo, time series Bureau of Economic Analysis - http://www.bea.gov/data/gdp/gross-dom... GDP, corporate profits, savings rates Federal Reserve - https://fred.stlouisfed.org/ curency, interest rates, payroll Quandl - https://www.quandl.com/ financial and economic Data.gov.uk UK Dataservice - https://www.ukdataservice.ac.uk Census data and much more WorldBank - https://datacatalog.worldbank.org census, demographics, geographic, health, income, GDP IMF - https://www.imf.org/en/Data economic, currency, finance, commodities, time series OpenData.go.ke Kenya govt data on agriculture, education, water, health, finance, … https://data.world/ Open Data for Africa - http://dataportal.opendataforafrica.org/ agriculture, energy, environment, industry, … Kaggle - https://www.kaggle.com/datasets A huge variety of different datasets Amazon Reviews - https://snap.stanford.edu/data/web-Am... 35M product reviews from 6.6M users GroupLens - https://grouplens.org/datasets/moviel... 20M movie ratings Yelp Reviews - https://www.yelp.com/dataset 6.7M reviews, pictures, businesses IMDB Reviews - http://ai.stanford.edu/~amaas/data/se... 25k Movie reviews Twitter Sentiment 140 - http://help.sentiment140.com/for-stud... 160k Tweets Airbnb - http://insideairbnb.com/get-the-data.... A TON of data by geo UCI ML Datasets - http://mlr.cs.umass.edu/ml/ iris, wine, abalone, heart disease, poker hands, …. Enron Email dataset - http://www.cs.cmu.edu/~enron/ 500k emails from 150 people From 2001 energy scandal. See the movie: The Smartest Guys in the Room. Spambase - https://archive.ics.uci.edu/ml/datase... Emails Jeopardy Questions - https://www.reddit.com/r/datasets/com... 200k Questions and answers in json Gutenberg Ebooks - http://www.gutenberg.org/wiki/Gutenbe... Large collection of books
babdulhakim2 / Financial Analysis With LlmThe application uses a combination of natural language processing (NLP), and financial analysis techniques to extract, process, and analyze data from uploaded financial documents (Excel format). It provides insights such as transaction summaries, suspicious trends, cash deposit detection, entity recognition, and potential ML/TF activities.
LinaSachuk / The Complete Python And Machine Learning For Financial AnalysisThe course is divided into 3 main parts covering python programming fundamentals, financial analysis in Python and AI/ML application in Finance/Banking Industry.
meppps / ML Financial Analysis WebAppStock Forecasting Web App with Machine Learning 📊
ManikantaSanjay / Financial Analysis Using Python And ML LibrariesThis repository has been created as part of my Udemy Course learning "Python & Machine Learning for Financial Analysis" by Dr. Ryan Ahmed.
olonok69 / QUANTA comprehensive collection of quantitative finance research spanning classical trading strategies, deep learning models for price prediction, ensemble ML methods, and modern LLM-powered financial analysis.
rishabhathiya / Bank Marketing# Bank Marketing Dataset ## Marketing Introduction: The process by which companies create value for customers and build strong customer relationships in order to capture value from customers in return. - Kotler and Armstrong (2010). Marketing campaigns are characterized by focusing on the customer needs and their overall satisfaction. Nevertheless, there are different variables that determine whether a marketing campaign will be successful or not. There are certain variables that we need to take into consideration when making a marketing campaign. ## The 4 Ps: 1) Segment of the Population: To which segment of the population is the marketing campaign going to address and why? This aspect of the marketing campaign is extremely important since it will tell to which part of the population should most likely receive the message of the marketing campaign. 2) Distribution channel to reach the customer's place: Implementing the most effective strategy in order to get the most out of this marketing campaign. What segment of the population should we address? Which instrument should we use to get our message out? (Ex: Telephones, Radio, TV, Social Media Etc.) 3) Price: What is the best price to offer to potential clients? (In the case of the bank's marketing campaign this is not necessary since the main interest for the bank is for potential clients to open depost accounts in order to make the operative activities of the bank to keep on running.) 4) Promotional Strategy: This is the way the strategy is going to be implemented and how are potential clients going to be address. This should be the last part of the marketing campaign analysis since there has to be an indepth analysis of previous campaigns (If possible) in order to learn from previous mistakes and to determine how to make the marketing campaign much more effective. ## What is a Term Deposit? A Term deposit is a deposit that a bank or a financial institurion offers with a fixed rate (often better than just opening deposit account) in which your money will be returned back at a specific maturity time. For more information with regards to Term Deposits please click on this link from Investopedia: https://www.investopedia.com/terms/t/termdeposit.asp ## Outline: 1. Import data from dataset and perform initial high-level analysis: look at the number of rows, look at the missing values, look at dataset columns and their values respective to the campaign outcome. 2. Clean the data: remove irrelevant columns, deal with missing and incorrect values, turn categorical columns into dummy variables. 3. Use machine learning techniques to predict the marketing campaign outcome and to find out factors, which affect the success of the campaign. ## Dataset Link https://archive.ics.uci.edu/ml/datasets/Bank+Marketing ## Dataset Information The data is related with direct marketing campaigns of a Portuguese banking institution. The marketing campaigns were based on phone calls. Often, more than one contact to the same client was required, in order to access if the product (bank term deposit) would be ('yes') or not ('no') subscribed. There are four datasets: 1) bank-additional-full.csv with all examples (41188) and 20 inputs, ordered by date (from May 2008 to November 2010), very close to the data analyzed in [Moro et al., 2014] 2) bank-additional.csv with 10% of the examples (4119), randomly selected from 1), and 20 inputs. 3) bank-full.csv with all examples and 17 inputs, ordered by date (older version of this dataset with less inputs). 4) bank.csv with 10% of the examples and 17 inputs, randomly selected from 3 (older version of this dataset with less inputs). The smallest datasets are provided to test more computationally demanding machine learning algorithms (e.g., SVM). The classification goal is to predict if the client will subscribe (yes/no) a term deposit (variable y). ## Attribute Information Input variables: #### bank client data: 1-age (numeric) 2-job : type of job (categorical: 'admin.','blue-collar','entrepreneur','housemaid','management','retired','self-employed','services','student','technician','unemployed','unknown') 3-marital : marital status (categorical: 'divorced','married','single','unknown'; note: 'divorced' means divorced or widowed) 4-education(categorical:'basic.4y','basic.6y','basic.9y','high.school','illiterate','professional.course','university.degree','unknown') 5-default: has credit in default? (categorical: 'no','yes','unknown') 6-housing: has housing loan? (categorical: 'no','yes','unknown') 7-loan: has personal loan? (categorical: 'no','yes','unknown') #### related with the last contact of the current campaign: 8-contact: contact communication type (categorical: 'cellular','telephone') 9-month: last contact month of year (categorical: 'jan', 'feb', 'mar', ..., 'nov', 'dec') 10-day_of_week: last contact day of the week (categorical: 'mon','tue','wed','thu','fri') 11-duration: last contact duration, in seconds (numeric). Important note: this attribute highly affects the output target (e.g., if duration=0 then y='no'). Yet, the duration is not known before a call is performed. Also, after the end of the call y is obviously known. Thus, this input should only be included for benchmark purposes and should be discarded if the intention is to have a realistic predictive model. #### other attributes: 12-campaign: number of contacts performed during this campaign and for this client (numeric, includes last contact) 13-pdays: number of days that passed by after the client was last contacted from a previous campaign (numeric; 999 means client was not previously contacted) 14-previous: number of contacts performed before this campaign and for this client (numeric) 15-poutcome: outcome of the previous marketing campaign (categorical: 'failure','nonexistent','success') #### social and economic context attributes 16-emp.var.rate: employment variation rate - quarterly indicator (numeric) 17-cons.price.idx: consumer price index - monthly indicator (numeric) 18-cons.conf.idx: consumer confidence index - monthly indicator (numeric) 19-euribor3m: euribor 3 month rate - daily indicator (numeric) 20-nr.employed: number of employees - quarterly indicator (numeric) Output variable (desired target): 21-y - has the client subscribed a term deposit? (binary: 'yes','no') ## License This dataset is public available for research. Citations - 1.Moro et al., 2014] S. Moro, P. Cortez and P. Rita. A Data-Driven Approach to Predict the Success of Bank Telemarketing. Decision Support Systems, Elsevier, 62:22-31, June 2014 2.Dua, D. and Graff, C. (2019). UCI Machine Learning Repository [http://archive.ics.uci.edu/ml]. Irvine, CA: University of California, School of Information and Computer Science.
SudeepRed / Financial Transaction Fraud DetectionComprehensive analysis of various ML models to detect fraud in financial transactions.
Cazzy-Aporbo / MarketPulse Analytics StudioMarketPulse Analytics demonstrates best practices for financial ML. Sentiment analysis.🏞️
abhishek-chattopadhyay / Mid Project Ml Financial AnalysisA data science and machine learning project exploring how macroeconomic indicators like GDP, inflation, and unemployment influence the S&P 500. Built as part of the Ironhack Data Science Bootcamp, this project includes data wrangling, exploratory analysis, predictive modeling, and an interactive Streamlit app.
mcf-long-short / Machine LearningData analysis of various financial datasets and applying numerous ML models with strong emphasis on feature engineering and model evaluation and selection.
ankitakulkarnigit / Finance Intelligence Agent🚀 AI-powered financial market intelligence system using Google Gemini, LangChain, RAG & multi-agent orchestration. Real-time stock analysis, sentiment analysis, and ML-based predictions.
yogeshsingh-11 / Automatic Trading SystemExtracted financial data(equity, commodity) via APIs and web scraping. Created technical indicators(MA, MACD, RSI) and conducted fundamental analysis. Designed, backtested and assessed trading strategies to calculate KPIs(Sharpe, Sortino etc.). Implemented ML strategies to achieve full automation
yashvisharma1204 / Financial Fraud DetectionA scalable fraud detection system leveraging Apache Spark on AWS EMR for large-scale financial transaction processing. This project enables distributed inference, efficient ML predictions, and AI-generated fraud analysis reports stored in AWS S3
ShivaniPatnaik / STOCK PRICE PREDICTION OF FMCG SECTORIn finance stock trading is one of the most important activities. Stock market prediction is an act of trying to determine the future value of a stock other financial instrument traded on a financial exchange. This project is about the prediction of a stock using Machine Learning. This analysis is used by the most of the stockbrokers while making the stock predictions. The programming language is used to predict the stock market using machine learning is Python. In this we propose a Machine Learning (ML) approach that will be trained from the available stocks data and gain intelligence and then uses the acquired knowledge for an accurate prediction. This study uses a machine learning technique called Support Vector Machine (SVM), Random Forest Classifier and Linear Regression methods to predict stock prices for the large and small capitalizations. The study aims to analyse the analysis of NSE listed FMCG companies in India with a sample size of four companies for a period from 2000 to 2018. From the Economic analysis, it is found that Gross Domestic Product, Inflation, Interest rates, Exchange rate and Consumer Confidence has impact on FMCG sector.
