Publications by saisree
Final project- obesity
My final project focuses on finding the distinctions between those who are obese and those who are not. The project will be split into three primary parts: lifestyle habits, family history, and physical activity. The obesity dataset will be used in this research because it contains a variety of characteristics, such as family history, physical ...
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Final Project - Obesity
My final project focuses on finding the distinctions between those who are obese and those who are not. The project will be split into three primary parts: lifestyle habits, family history, and physical activity. The obesity dataset will be used in this research because it contains a variety of characteristics, such as family history, physical ...
9580 sym Python (190785 sym/40 pcs) 6 img
Week 13 | Data Dive — Critiquing Models and Analyses
I did this assignment based on week 6 lab notes. # 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 datas...
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Week 12 | Data Dive — Time-based Data
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 1.9.3 ✔ tidyr 1.3.1...
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Week 11 | Data Dive — GLMs (Part 2)
# 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 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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