INTRODUCTION – Explore Data And Create Models To Predict Numeric Values

Data exploration and analysis are fundamental to data science. Data scientists need proficiency in languages like Python to explore, visualize, and manipulate data effectively. In this module, you will learn how to utilize Python for these tasks. Additionally, you will discover how to use regression techniques to develop a machine-learning model for predicting numeric values. The scikit-learn framework in Python will be used to train and evaluate this regression model.

Learning Objectives

• Use Python to explore, visualize, and manipulate data
• Describe how regression can be used to create a machine learning model that predicts numeric values
• Use the scikit-learn framework in Python to train and evaluate a regression model

PRACTICE QUIZ: KNOWLEDGE CHECK 1

1. Which Python libraries are used in machine learning and deep learning?

Select all options that apply.

• Scikit-learn
• Matplotlib
• TensorFlow (CORRECT)
• PyTorch (CORRECT)

Correct: TensorFlow is an end-to-end open-source platform for machine learning.

Correct: PyTorch is an open-source machine learning framework that accelerates the path from research prototyping to production deployment.

2. What are the benefits of using the NumPy and Pandas libraries in your Python project?

• Supplying machine learning and deep learning capabilities
• Offering simple and predictive data analysis
• Providing attractive data visualizations
• Simplifying analyzing and manipulating data (CORRECT)

Correct: NumPy and Pandas libraries enables these features in Python.

3. Which Python library provides functionalities similar to excel?

• Pandas (CORRECT)
• NumPy
• Matplotlib

Correct: Pandas is like excel for Python – providing easy-to-use functionality for data tables.

4. Which Python library provides functionality comparable to mathematical tools such as MATLAB and R?

• Scikit-learn
• NumPy (CORRECT)
• TensorFlow

Correct: NumPy significantly simplifies the user experience and it also offers comprehensive mathematical functions.

5. What should you use if you want to run basic scripts in a web browser?

• NumPy
• Pandas
• Jupyter notebook (CORRECT)

Correct: Jupyter notebooks are a popular way of running basic scripts using your web browser. Typically, these notebooks are a single webpage, broken up into text sections and code sections that are executed on the server rather than your local machine.

PRACTICE QUIZ: KNOWLEDGE CHECK 2

1. In a regression model, what is a feature and what is a label?

• A feature is the variable to be predicted.
• A label is the variable that represents characteristics.
• A label is the variable to be predicted. (CORRECT)
• A feature is the variable that represents characteristics. (CORRECT)
• Correct: The variable we’re trying to predict is known as the label.

Correct: Variables in the data that represent characteristics are known as the features.

2. When you want to train a regression model based on historical data, which are the two subsets into which you split the data sample?

• A confirmation dataset
• A performance dataset
• A validation dataset (CORRECT)
• A training dataset (CORRECT)

Correct: A validation or test dataset can be used to evaluate the model by using it to generate predictions for the label and comparing them to the actual known label values.

Correct: In this data set you’ll apply an algorithm that determines a function encapsulating the relationship between the feature values and the known label values.

3. True or False? In machine learning, the difference between a predicted label value and the actual value is known as “the residuals”.

• True
• False (CORRECT)

Correct: In practice, the “actual” values are based on sample observations (which themselves may be subject to some random variance). “The residuals” is the difference between comparing a predicted value (ŷ) with an observed value (y).

4. To randomly split the data between training and validation subsets, you can use the train_test_split function. In which python library can you find this function?

• Pytorch
• Numpy
• Scikit-learn (CORRECT)
• Matplotlib
• Scikit-learn

Correct: This library contains the train_test_split function.

5. This evaluation metric yields a relative metric in which the smaller the value, the better the fit of the model. Which evaluation metric is described above?

• Mean Square Error (MSE) (CORRECT)
• Coefficient of Determination (usually known as R-squared or R2)
• Root Mean Square Error (RMSE)

Correct: This is the described metric, which is the mean of the squared differences between predicted and actual values.

QUIZ: TEST PREP

1. How would a list and a NumPy array behave when they are multiplied by 3?

• Multiplying a list by 3 performs an element-wise calculation on the list, which sees the list stay the same size, but each element has been multiplied by 3.
• Multiplying an NumPy array by 3 creates a new array 3 times the length with the original sequence repeated 3 times.
• Multiplying a list by 3 creates a new list 3 times the length with the original sequence repeated 3 times. (CORRECT)
• Multiplying a NumPy array by 3 performs an element-wise calculation on the array, which sees the array stay the same size, but each element has been multiplied by 3. (CORRECT)

Correct: This is how a list behaves when multiplied.

Correct: This is how a NumPy array behaves when multiplied.

2. If you have a NumPy array with the shape (2,35), what does this tell you about the elements in the array?

• The array contains 2 elements with the values of 2 and 35.
• The array contains 35 elements, all with the value 2.
• The array is two dimensional, consisting of two arrays with 35 elements each. (CORRECT)

Correct: A shape of (2,35) indicates a multidimensional array with two arrays, each containing 35 elements.

3. Suppose you have a Pandas DataFrame named df_sales containing daily sales data. The DataFrame has the following columns: year, month, day_of_month, sales_total. If you want to find the average sales_total value, which code should you use?

• mean(df_sales[‘sales_total’])
• df_sales[‘sales_total’].mean() (CORRECT)
• df_sales[‘sales_total’].avg()

Correct: This code will return the average of the sales_total column values.

4. You work on a DataFrame containing data about daily ice cream sales. You use the corr method to compare the avg_temp and units_sold columns, and get a result of 0.95. What does this result indicate?

• Days with high avg_temp values tend to coincide with days that have high units_sold values. (CORRECT)
• On the day with the maximum units_sold value, the avg_temp value was 0.95.
• The units_sold value is, on average, 95% of the avg_temp value.

Correct: The corr method returns the correlation, and a value near 1 indicates a positive correlation.

5. This is a relative metric in which the higher the value, the better the fit of the model.Which evaluation model is described?

• Mean Square Error (MSE)
• Coefficient of Determination (known as R-squared or R2) (CORRECT)
• Root Mean Square Error (RMSE)

Correct: This is the evaluation metric described. In essence, this metric represents how much of the variance between predicted and actual label values the model is able to explain.

6. This evaluation metric yields an absolute metric in the same unit as the label.

Which metric is described?

• Mean Square Error (MSE)
• Root Mean Square Error (RMSE) (CORRECT)
• Coefficient of Determination (known as R-squared or R2)

Correct: This is the described metric. This means that the smaller the value, the better the model.

7. You’ve just created a model object using the LinearRegression class from the scikit-learn library.

What should you do next to train the model?

• Call the predict() method of the model object, specifying the training feature and label arrays.
• Call the fit() method of the model object, specifying the training feature and label arrays. (CORRECT)
• Call the score() method of the model object, specifying the training feature and test feature arrays.

Correct: To train the model, use the fit() method.

CONCLUSION – Explore Data And Create Models To Predict Numeric Values

In conclusion, mastering data exploration and analysis with Python is crucial for any aspiring data scientist. This module equips you with the necessary skills to explore, visualize, and manipulate data. Furthermore, you will gain practical experience in building and evaluating regression models using the scikit-learn framework. These competencies will form a solid foundation for your journey in data science and machine learning.