soundaryabala123 Goto Github PK

cartoonify_image_cv2.py

Cartoonify_image_cv2

coursera---getting-and-cleaning-data

Tidy Data project

coursera-exploratory-data-analysis-course-project-1

Plotting Assignment 1 for Exploratory Data Analysis

coursera-exploratory-data-analysis-project-1

week 1 project - exploratory data analysis course - coursera

coursera-getting-and-cleaning-data-course-project

Coursera Getting and Cleaning Data Course Week 4 Project

coursera-getting-and-cleaning-data-project

course project for Coursera "Getting and Cleaning Data"

coursera-getting-and-cleaning-data-project-1

Getting and cleaning data project on Coursera

datasciencecoursera

datasharing

The Leek group guide to data sharing

diseaseprediction.py

exploratory-data-analysis

## Introduction This assignment uses data from the <a href="http://archive.ics.uci.edu/ml/">UC Irvine Machine Learning Repository</a>, a popular repository for machine learning datasets. In particular, we will be using the "Individual household electric power consumption Data Set" which I have made available on the course web site: * <b>Dataset</b>: <a href="https://d396qusza40orc.cloudfront.net/exdata%2Fdata%2Fhousehold_power_consumption.zip">Electric power consumption</a> [20Mb] * <b>Description</b>: Measurements of electric power consumption in one household with a one-minute sampling rate over a period of almost 4 years. Different electrical quantities and some sub-metering values are available. The following descriptions of the 9 variables in the dataset are taken from the <a href="https://archive.ics.uci.edu/ml/datasets/Individual+household+electric+power+consumption">UCI web site</a>: <ol> <li><b>Date</b>: Date in format dd/mm/yyyy </li> <li><b>Time</b>: time in format hh:mm:ss </li> <li><b>Global_active_power</b>: household global minute-averaged active power (in kilowatt) </li> <li><b>Global_reactive_power</b>: household global minute-averaged reactive power (in kilowatt) </li> <li><b>Voltage</b>: minute-averaged voltage (in volt) </li> <li><b>Global_intensity</b>: household global minute-averaged current intensity (in ampere) </li> <li><b>Sub_metering_1</b>: energy sub-metering No. 1 (in watt-hour of active energy). It corresponds to the kitchen, containing mainly a dishwasher, an oven and a microwave (hot plates are not electric but gas powered). </li> <li><b>Sub_metering_2</b>: energy sub-metering No. 2 (in watt-hour of active energy). It corresponds to the laundry room, containing a washing-machine, a tumble-drier, a refrigerator and a light. </li> <li><b>Sub_metering_3</b>: energy sub-metering No. 3 (in watt-hour of active energy). It corresponds to an electric water-heater and an air-conditioner.</li> </ol> ## Loading the data When loading the dataset into R, please consider the following: * The dataset has 2,075,259 rows and 9 columns. First calculate a rough estimate of how much memory the dataset will require in memory before reading into R. Make sure your computer has enough memory (most modern computers should be fine). * We will only be using data from the dates 2007-02-01 and 2007-02-02. One alternative is to read the data from just those dates rather than reading in the entire dataset and subsetting to those dates. * You may find it useful to convert the Date and Time variables to Date/Time classes in R using the `strptime()` and `as.Date()` functions. * Note that in this dataset missing values are coded as `?`. ## Making Plots Our overall goal here is simply to examine how household energy usage varies over a 2-day period in February, 2007. Your task is to reconstruct the following plots below, all of which were constructed using the base plotting system. First you will need to fork and clone the following GitHub repository: [https://github.com/rdpeng/ExData_Plotting1](https://github.com/rdpeng/ExData_Plotting1) For each plot you should * Construct the plot and save it to a PNG file with a width of 480 pixels and a height of 480 pixels. * Name each of the plot files as `plot1.png`, `plot2.png`, etc. * Create a separate R code file (`plot1.R`, `plot2.R`, etc.) that constructs the corresponding plot, i.e. code in `plot1.R` constructs the `plot1.png` plot. Your code file **should include code for reading the data** so that the plot can be fully reproduced. You should also include the code that creates the PNG file. * Add the PNG file and R code file to your git repository When you are finished with the assignment, push your git repository to GitHub so that the GitHub version of your repository is up to date. There should be four PNG files and four R code files. The four plots that you will need to construct are shown below. ### Plot 1  ### Plot 2  ### Plot 3  ### Plot 4 

getting-and-cleaning-data-project

# Coursera: Getting and Cleaning Data Course : Course Project This repository contains course project, made for the Coursera's "Getting and Cleaning Data" course. Files in this repo: 1. run_analysis.R - script, that make all the computation, that are need to be made, according to course project task. 2. README.md - this file 3. README_RU.txt - source dataset's readme file translated into my native language 4. features_info_RU.txt - source dataset's features_info file, translated to my native language 5. LICENSE - MIT license file 6. .gitignore - git ignore file **Usage:** 1. Get run_analysis.R somehow (download or git clone) 2. Run run_analysis.R in RStudio **What script does:** 1. If source dataset doesn't exists, download it from the website and extract from zip 2. Load features and activities names 3. Load train and test data 4. Merge train and test data into single dataframe 5. Remove unneded data features from dataframe 6. Get average data per subject and per activity 7. Save the last mentioned data into tidy.txt file

hello-world

Hello welcome to github

llm

Practice

machinelearningmodeltoheroku-master

ml_project

mnist

programmingassignment2

Repository for Programming Assignment 2 for R Programming on Coursera

reproducible-research-course-project-2

Repository created.

reproducible-research-project-1

Coursera Reproducible Research Project 1

speaker-recognition

testing

titanicsurvivors_predictionmodel_tensorflow

twitter_sentiment.py

twitter sentiment