| 1. Sign up in docker - https://hub.docker.com/account/signup/ |
2. Login and go to docker hub |
3. You will get an mail from docker, you need to verify your mail id first. |
4. Create a repository in docker hub |
5. login into to your server. (Assume you have already installed docker) |
6. $ sudo docker login |
provide your user id / password / email for docker hub |
7. $ sudo docker ps -- to identify the container, whose image you need to push to docker hub |
8. $ sudo docker commit |
9. $ sudo docker images --- Now you can see new entry in the Repository with your public facing repository and a default TAG named 'latest' |
10. $ sudo docker push userid/repository-name --push the image to your repository created in docker hub |
11. Verify this from docker hub repository. |
Aspect Data Wrangling (Data Preprocessing) Exploratory Data Analysis (EDA) Objective Prepare raw data for modeling by cleaning, transforming, and formatting it appropriately. Explore and understand the data to gain insights, identify patterns, and make decisions on data handling and modeling. Order Typically performed as a preliminary step before EDA. Usually conducted after data wrangling to further investigate data characteristics. Data Handling Focuses on data cleaning, filling missing values, encoding categorical variables, and scaling features. Involves data visualization, statistical analysis, and summary statistics to uncover patterns, relationships, and anomalies. Techniques Techniques include imputation, outlier detection, feature scaling, and one-hot encoding. Techniques include histograms, scatter plots, box plots, correlation matrices, and descriptive statistics. Data Transformation Involves structural changes to the dataset, such as feature engineering, data normaliz...
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