A package for statistically rigorous scientific discovery using machine learning. Implements prediction-powered inference.
A comprehensive exploration of Statistics and Probability Theory concepts, with practical implementations in Python
Credit Risk Modelling | Calculation of PD, LGD, EDA and EL with Machine Learning in Python
E-Commerce Website A/B testing: Recommend which of two landing pages to keep based on A/B testing
Multiple hypothesis testing in Python
Statistical functions based on bootstrapping for computing confidence intervals and p-values comparing machine learning models and human readers
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.
Understand the results of an A/B test run by the website and provide statistical and practical interpretation on the test results
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…
Analysis platform for large-scale dose-dependent data
Implementation of backward elimination algorithm used for dimensionality reduction for improving the performance of risk calculation in life insurance industry.
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…
pMoSS (p-value Model using the Sample Size) is a Python code to model the p-value as an n-dependent function using Monte Carlo cross-validation. Exploits the dependence on the sample size to characterize the differences among groups of large datasets
Analysis of mock A/B Test Results by an e-commerce company. Application of probability, hypothesis testing, sampling distribution, two-sample z-test, and logistic regression to determining whether the company should implement the new web page it developed to increase users' conversion rate
First rank winner in the Machine Learning Course Competition for class 2021-2022. Airline ticket price prediction from end to end (analysis - preprocessing - modeling - testing - deployment - documentation) between Indian cities
Predicted house prices using multiple linear regression. Used back elimination to further improve the model and select features based on p-value and adjusted R squared value.
Adjust p-values for multiple comparisons
collection of utility functions for correlation analysis
I will include two ways of t tests that compare conversion rate and click through rate of two groups
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