Publications by saisree
Week 10 | Data Dive — GLMs
# Load necessary libraries library(tidyverse) ## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ── ## ✔ dplyr 1.1.4 ✔ readr 2.1.5 ## ✔ forcats 1.0.0 ✔ stringr 1.5.1 ## ✔ ggplot2 3.5.1 ✔ tibble 3.2.1 ## ✔ lubridate 1...
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Week 9 | Data Dive — Regression Diagnostics
library(ggplot2) library(dplyr) ## ## Attaching package: 'dplyr' ## The following objects are masked from 'package:stats': ## ## filter, lag ## The following objects are masked from 'package:base': ## ## intersect, setdiff, setequal, union library(car) ## Loading required package: carData ## ## Attaching package: 'car' ## The fo...
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Week 8 | Data Dive — Regression Modeling
# Load necessary libraries library(ggplot2) library(dplyr) ## ## Attaching package: 'dplyr' ## The following objects are masked from 'package:stats': ## ## filter, lag ## The following objects are masked from 'package:base': ## ## intersect, setdiff, setequal, union # Load the dataset obesity<- read.csv("C:\\Users\\saisr\\Downloa...
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Week 7 | Data Dive — Hypothesis Testing
Some questions which i found interesting after working on this dataset for 6 weeks are: - Which attributes are strongly correlated with obesity levels? - Do certain age groups or genders show higher obesity rates? - Can we identify groups of individuals who share common characteristics that are predictive of higher obesity levels? Hypothesis 1...
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Project Meeting 1: Data Discovery
1.SUMMARY The dataset used for this analysis is the UCI Obesity Dataset, which can be found at the UCI Machine Learning Repositoryhere. The dataset contains information on individual habits, conditions, and medical history with the purpose of identifying risk factors associated with obesity. The dataset includes multiple features such as gender...
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Week 6 | Data Dive — Confidence Intervals
library(ggplot2) library(dplyr) ## ## Attaching package: 'dplyr' ## The following objects are masked from 'package:stats': ## ## filter, lag ## The following objects are masked from 'package:base': ## ## intersect, setdiff, setequal, union obesity <- read.csv("C:\\Users\\saisr\\Downloads\\statistics using R\\estimation+of+obesity+l...
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Week 5 | Data Dive — Documentation
1 The 3 unclear columns from the obesity dataset are: family_history_with_overweight : - This column contains “yes” or “no” values. Without documentation, we may not fully understand what is meant by “family history” in this context. Is it one’s immediate family member, or does it refer to extended family? Reason for Encoding: En...
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week 4- Data Dive — Sampling and Drawing Conclusions
# Load necessary libraries library(tidyverse) ## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ── ## ✔ dplyr 1.1.4 ✔ readr 2.1.5 ## ✔ forcats 1.0.0 ✔ stringr 1.5.1 ## ✔ ggplot2 3.5.1 ✔ tibble 3.2.1 ## ✔ lubridate 1...
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Week 3 | Data Dive — Group By and Probabilities
# Load necessary libraries library(tidyverse) ## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ── ## ✔ dplyr 1.1.4 ✔ readr 2.1.5 ## ✔ forcats 1.0.0 ✔ stringr 1.5.1 ## ✔ ggplot2 3.5.1 ✔ tibble 3.2.1 ## ✔ lubridate 1...
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week 2 data dive
loading and exploring the data set library(dplyr) ## ## Attaching package: 'dplyr' ## The following objects are masked from 'package:stats': ## ## filter, lag ## The following objects are masked from 'package:base': ## ## intersect, setdiff, setequal, union library(ggplot2) library(tidyverse) ## ── Attaching core tidyverse pack...
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