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Questions and Answers
The header is extracted by using the method next() on the csvreader object.
The header is extracted by using the method next() on the csvreader object.
True (A)
The csv.reader object is used to read data from a JSON file.
The csv.reader object is used to read data from a JSON file.
False (B)
The .close() method must always be called after reading a file in Python.
The .close() method must always be called after reading a file in Python.
False (B)
The type of the file object returned by the open() method is '_io.TextIOWrapper'.
The type of the file object returned by the open() method is '_io.TextIOWrapper'.
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To read a CSV file, the first step is to create an empty list called 'rows'.
To read a CSV file, the first step is to create an empty list called 'rows'.
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The method csv.writerows(rows) is used to write a single row into a CSV file.
The method csv.writerows(rows) is used to write a single row into a CSV file.
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The dictionary objects in mydict contain information about students' branch, CGPA, name, and year.
The dictionary objects in mydict contain information about students' branch, CGPA, name, and year.
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To open a CSV file in write mode, the mode used is 'r'.
To open a CSV file in write mode, the mode used is 'r'.
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The field names for the CSV file are stored in a variable called fields.
The field names for the CSV file are stored in a variable called fields.
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The DictWriter object is used to convert the file object to write a list of lists to a CSV file.
The DictWriter object is used to convert the file object to write a list of lists to a CSV file.
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The method df.tail(n = 10) retrieves the first 10 rows of a DataFrame.
The method df.tail(n = 10) retrieves the first 10 rows of a DataFrame.
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The describe() method provides summary statistics for all numeric columns in a DataFrame.
The describe() method provides summary statistics for all numeric columns in a DataFrame.
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Using the .head() method without any arguments will display 10 rows by default.
Using the .head() method without any arguments will display 10 rows by default.
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The percentiles argument in the describe() method can be used to modify the quartiles displayed.
The percentiles argument in the describe() method can be used to modify the quartiles displayed.
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The index parameter must always be set to True in the df.to_excel() method.
The index parameter must always be set to True in the df.to_excel() method.
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Study Notes
Lab Manuals
- Introduction to Machine Learning Lab Manual
- Prepared by: Prof. Noureldien A. Noureldien
- Date: October 2024
Table of Contents
- Week 1: Python Build-in-Functions and the Math Module (page 3)
- Week 2: NumPy Module (page 14)
- Week 3: CSV Files (page 27)
- Week 4: Pandas Package (page 41)
- Week 5: Data Preprocessing and Machine learning Modeling (page 61)
- Week 6: Building Supervised Learning Classification Model (page 87)
- Week 7: Building Learning Regression Models (page 113)
- Python Standard Library (page 5)
- Python Libraries for Data Collection (page 5)
- Python Libraries for Data Cleaning and Manipulation (page 5)
- Python Libraries for Data Visualization (page 6)
- Python Libraries for Modeling (page 6)
- Python Basic Build-in Functions (page 6)
- Python Math Module Library (page 8)
- Finding the factorial of the number (page 9)
- Finding the GCD (page 10)
- Finding the Logarithm (page 11)
Lab (1): Python Build-In Functions and the Math Module
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Course Objectives:
- Introduce basic concepts and techniques of Machine learning.
- Provide understanding of various Machine Learning algorithms and the way to evaluate them.
- Apply Machine Learning to learn, predict, and classify real-world problems in Supervised and Unsupervised Learning.
- Develop professional and ethical attitudes in students, promoting multidisciplinary approaches and cost-effective solutions.
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Course Outcomes:
- Students will understand fundamental concepts and techniques of machine learning.
- Students will comprehend various machine learning algorithms and their evaluation.
- Students will demonstrate application of machine learning to deal with real world scenarios through supervised and unsupervised learning.
- Students will develop a multidisciplinary approach and present cost-effective solutions based on insights from machine learning applications.
- Students will develop their problem-solving abilities using machine learning methods.
Lab (2): NumPy Module
- Description: Provides techniques for creating and manipulating arrays in Python.
- Learning Outcomes: Students will use Python NumPy modules and functions to manipulate arrays.
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Description
Test your knowledge on handling CSV files in Python. This quiz covers methods for reading, writing, and managing CSV data using the csv module and pandas library. Enhance your skills in data manipulation and file handling in your Python projects.
