A Julia package for multivariate statistics and data analysis (e.g. dimension reduction)
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Updated
May 28, 2024 - Julia
A Julia package for multivariate statistics and data analysis (e.g. dimension reduction)
Fast Unit Root Tests and OLS regression in C++ with wrappers for R and Python
Popular Econometrics content with code; Simple Linear Regression, Multiple Linear Regression, OLS, Event Study including Time Series Analysis, Fixed Effects and Random Effects Regressions for Panel Data, Heckman_2_Step for selection bias, Hausman Wu test for Endogeneity in Python, R, and STATA.
Multiple econometrics cheat sheets with a complete and summarize review going from the basics of an econometric model to the solution of the most popular problems.
Assignment-04-Simple-Linear-Regression-2. Q2) Salary_hike -> Build a prediction model for Salary_hike Build a simple linear regression model by performing EDA and do necessary transformations and select the best model using R or Python. EDA and Data Visualization. Correlation Analysis. Model Building. Model Testing. Model Predictions.
Assignment-05-Multiple-Linear-Regression-2. Prepare a prediction model for profit of 50_startups data. Do transformations for getting better predictions of profit and make a table containing R^2 value for each prepared model. R&D Spend -- Research and devolop spend in the past few years Administration -- spend on administration in the past few y…
Assignment-04-Simple-Linear-Regression-1. Q1) Delivery_time -> Predict delivery time using sorting time. Build a simple linear regression model by performing EDA and do necessary transformations and select the best model using R or Python. EDA and Data Visualization, Feature Engineering, Correlation Analysis, Model Building, Model Testing and Mo…
Specification Curve is a Python package that performs specification curve analysis: exploring how a coefficient varies under multiple different specifications of a statistical model.
Machine Learning Project
Linear regression on numerical attributes
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Master Degree Coursework: Econometrics I
Conducted data analysis, statistical analysis, and data visualization on an Indian crime dataset. Applied various machine learning algorithms to gain insights from the data. Utilized Time-Series models for prediction and forecasting based on the crime data analysis.
Construct workable datasets from web data and applied various machine learning methods to predict future oil price
House price predictive modeling using linear regression techniques. My first modeling project! Skills demonstrated: Pandas, feature engineering, standardization, encoding, interpreting model performance/error, visualizations.
Fast computation of some matrices useful in statistics
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Supervised Machine Learning Using Regression Analysis
Implemented ordinary least squares regression from scratch in python by computing root mean square error and coefficient estimates
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