HOUSEHOLD ELECTRIC POWER CONSUMPTION ANALYSIS CLUSTERING AND PREDICTION WITH PYTHON is popular PDF and ePub book, written by Vivian Siahaan in 2022-03-03, it is a fantastic choice for those who relish reading online the Technology & Engineering genre. Let's immerse ourselves in this engaging Technology & Engineering book by exploring the summary and details provided below. Remember, HOUSEHOLD ELECTRIC POWER CONSUMPTION ANALYSIS CLUSTERING AND PREDICTION WITH PYTHON can be Read Online from any device for your convenience.
HOUSEHOLD ELECTRIC POWER CONSUMPTION ANALYSIS CLUSTERING AND PREDICTION WITH PYTHON Book PDF Summary
In this project, you will perform analysis, clustering, and prediction on household electric power consumption with python. The dataset used in this project contains 2075259 measurements gathered between December 2006 and November 2010 (47 months). Following are the attributes in the dataset: date: Date in format dd/mm/yyyy; time: time in format hh:mm:ss; globalactivepower: household global minute-averaged active power (in kilowatt); globalreactivepower: household global minute-averaged reactive power (in kilowatt); voltage: minute-averaged voltage (in volt); global_intensity: household global minute-averaged current intensity (in ampere); submetering1: 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); submetering2: 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; and submetering3: energy sub-metering No. 3 (in watt-hour of active energy). It corresponds to an electric water-heater and an air-conditioner. In this project, you will perform clustering using KMeans to get 5 clusters. The machine learning models used in this project to perform regression on total number of purchase and to predict clusters as target variable are K-Nearest Neighbor, Random Forest, Naive Bayes, Logistic Regression, Decision Tree, Support Vector Machine, LGBM, Gradient Boosting, XGB, and MLP. Finally, you will plot boundary decision, distribution of features, feature importance, cross validation score, and predicted values versus true values, confusion matrix, learning curve, performance of the model, scalability of the model, training loss, and training accuracy.
Detail Book of HOUSEHOLD ELECTRIC POWER CONSUMPTION ANALYSIS CLUSTERING AND PREDICTION WITH PYTHON PDF
- Author : Vivian Siahaan
- Release : 03 March 2022
- Publisher : BALIGE PUBLISHING
- ISBN : 978186723xxxx
- Genre : Technology & Engineering
- Total Page : 150 pages
- Language : English
- PDF File Size : 7,8 Mb
If you're still pondering over how to secure a PDF or EPUB version of the book HOUSEHOLD ELECTRIC POWER CONSUMPTION ANALYSIS CLUSTERING AND PREDICTION WITH PYTHON by Vivian Siahaan, don't worry! All you have to do is click the 'Get Book' buttons below to kick off your Download or Read Online journey. Just a friendly reminder: we don't upload or host the files ourselves.