Machine learning session 3
- 1. PROGRAMMING CRASH COURSE KEDS BIODESIGNS WEEK : II CLASS : 3 SESSION :ML SYSTEM
- 2. MOST IMPORTANT THING ?? It is the data that we need to load for starting any of the ML project. With respect to data, the most common format of data for ML projects is CSV (comma-separated values). CSV is a simple file format which is used to store tabular data (number and text) such as a spreadsheet in plain text. In Python, we can load CSV data into with different ways but before loading CSV data we must have to take care about some considerations.
- 3. CONSIDERATION WHILE LOADING CSV DATA CSV data format is the most common format for ML data, but we need to take care about following major considerations while loading the same into our ML projects. File Header In CSV data files, the header contains the information for each field. We must use the same delimiter for the header file and for data file because it is the header file that specifies how should data fields be interpreted. The following are the two cases related to CSV file header which must be considered − Case-I: When Data file is having a file header − It will automatically assign the names to each column of data if data file is having a file header. Case-II: When Data file is not having a file header − We need to assign the names to each column of data manually if data file is not having a file header. In both the cases, we must need to specify explicitly weather our CSV file contains header or not.
- 4. Remarks Remarks in any information document are having their criticalness. In CSV information record, remarks are demonstrated by a hash (#) toward the beginning of the line. We have to consider remarks while stacking CSV information into ML ventures in such a case that we are having remarks in the document then we may need to demonstrate, relies on the technique we decide for stacking, regardless of whether to anticipate those remarks or not. Delimiter In CSV information documents, comma (,) character is the standard delimiter. The job of delimiter is to isolate the qualities in the fields. It is critical to consider the job of delimiter while transferring the CSV record into ML ventures since we can likewise utilize an alternate delimiter, for example, a tab or void area. In any case, on account of utilizing an alternate delimiter than standard one, we should need to determine it expressly.
- 5. Statements In CSV information documents, the twofold citation (" ") mark is the default quote character. It is imperative to think about the job of statements while transferring the CSV record into ML ventures we can likewise utilize another statement character than the twofold quote. Be that as it may, if there should be an occurrence of utilizing an alternate statement character than the standard one, we need to determine it unequivocally.
- 6. METHODS TO LOAD CSV DATA FILE Load CSV with Python Standard Library The first and most used approach to load CSV data file is the use of Python standard library which provides us a variety of built-in modules namely csv module and the reader () function. The following is an example of loading CSV data file with the help of it
- 7. Another approach to load CSV data file is NumPy and numpy.loadtxt() function . The following is an example of loading CSV data file with the help of it − Example In this example, we are using the Pima Indians Dataset having the data of diabetic patients. This dataset is a numeric dataset with no header. It can also be downloaded into our local directory. After loading the data file, we can convert it into NumPy array and use it for ML projects. The following is the Python script for loading CSV data file.
- 8. SCRIPT The following is the Python script for loading CSV data file, along with providing the headers names too, using Pandas on Pima Indians Diabetes dataset