Machine learning for beginner(Data Science enthusiast)
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Updated
Sep 7, 2023 - Jupyter Notebook
Machine learning for beginner(Data Science enthusiast)
This repository is created for storing the components of Statistical Tests of One Pop, Two Pops and Three or more pops using Python.
This repository contains all of the statistical Inference-related projects I've worked on. The projects are part of the graduate course at the University of Tehran.
This repository contains introductory notebooks for basic hypothesis testing problems.
Examples of using the test statistics to test hypotheses
Large sample tests for Means and proportions based on single and two samples using R
working to understand the results of an A/B test run by an e-commerce website. The company has developed a new web page in order to try and increase the number of users who "convert," meaning the number of users who decide to pay for the company's product.
Analysis of Romantic Relationships using Statistical Modeling
Performed rigorous preprocessing, and data cleaning, and conducted exploratory data analysis to identify trends, patterns, and outliers, leading to valuable insights. Employed various statistical methods concepts to get insights about the data for prediction.
Homeworks and R projects for the course Foundations of Data Science
The data relates to several user actions or interests recorded on two variants of landing pages for an online news portal. The objective is to analyse these interests by performing statistical analyses to determine if one variant is more effective based on chosen metrics (A/B testing).
This repo contains various Statistics techniques like descriptive_Statistics including Central tendency, CLT, Z_Score and inferential statistics etc..
My first classification problem solved with logistic regression.
This repository has been created to link my statistical testing assignments and independent studies for Thinkful as I navigate thru the Data Analytics Immersion program from October 2020 - January 2021.
E-news Express, an online news portal, aims to expand its business by acquiring new subscribers. This python based project performs EDA, data visualization and conducts a no of statistical tests to determine key business insights: in particular whether the new landing page can get greater viewership duration and subscriber conversion
Analyse efficacy of your own methods for calculating confidence interval
Example of Proportion Z-Test for A/B Testing in Python
Probability and Statistics for Data Scientist - Course Project. Various Testing(Hypothesis, T-Test, Z-Test, KS Test etc) are performed on Covid and crime dataset for Los Angeles. Auto Regression and Linear Regression are also performed without using scimitar libraries in python.
An electronic news aggregator is testing new landing pages in an effort to halt a decline in new subscriber rates. This analysis details the engagement levels and conversion rates of the new pages.
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