1. Python Basics
Python is one of the most popular programming languages for data science due to its readability, versatility, and rich ecosystem of libraries. Here we cover the absolute basics you need to get started with Python for data science.
1.1 Introduction to Python: Why Python for Data Science?
Python is a versatile and user-friendly language, favored by data scientists and beginners alike. It is high-level, meaning it abstracts complex details of the computer, allowing you to focus on learning programming and data analysis principles. Python also boasts a rich ecosystem of libraries like NumPy, Pandas, and Matplotlib that are specifically designed for data science tasks.
1.2 Python Syntax and Data Types
Python uses a simple, readable syntax that makes it a great choice for beginners. Here are the basic data types in Python:
1.2.1 Strings
Strings are sequences of character data. In Python, you define a string by enclosing the text in quotation marks.
For example:
1.2.2 Numbers
Python supports different types of numbers, including integers and floating-point numbers.
Here is how you can use numbers in Python:
1.2.3 Booleans
Booleans represent one of two values: True or False. They are typically used in conditional statements.
For example:
1.3 Variables and Operators
Variables are containers for storing data values. Python has no command for declaring a variable; it is created the moment you first assign a value to it.
Python supports various types of operators such as arithmetic operators (+, -, *, /, %, etc.), assignment operators (=, +=, -=, etc.), and comparison operators (==, !=, >, <, etc.).
Here’s an example of variables and operators in action:
1.4 Conditional Statements and Loops
1.4.1 Conditional Statements
- Equals:
a == b - Not Equals:
a != b - Less than:
a < b - Less than or equal to:
a <= b - Greater than:
a > b - Greater than or equal to:
a >= b
Here’s an example of conditional statements in Python:
1.4.2 Loops
Python has two primitive loop commands:
whileloopsforloops
Here’s an example of a for loop in Python:
1.5 Functions
A function is a block of code that only runs when it is called. You can pass data, known as parameters, into a function. A function can return data as a result.
Here’s an example of a function in Python:
1.6 Data Structures: Lists, Tuples, Sets, Dictionaries
Python provides four built-in data structures: Lists, Tuples, Sets, and Dictionaries.
- List: A collection that is ordered and changeable. Allows duplicate members.
- Tuple: A collection that is ordered and unchangeable. Allows duplicate members.
- Set: A collection that is unordered and unindexed. No duplicate members.
- Dictionary: A collection that is unordered, changeable, and indexed. No duplicate members.
Here’s an example for each data structure:
This is a very high-level overview of Python basics. In the next sections, we’ll delve deeper into Python’s data science-specific features. Let’s move on to learn about some of the most important Python libraries for data science.
2. Working with Libraries
Python’s power for data science comes from its extensive set of libraries, which are packages or modules written by others for specific functionalities. In this section, we’ll discuss some essential libraries that every data scientist should be familiar with.
2.1 Introduction to Python Libraries for Data Science
In Python, a library is a module or package that provides additional functionality beyond what’s included in the base Python language. For data science, certain libraries have become very popular due to their power and ease of use. These libraries are:
- NumPy: Stands for ‘Numerical Python’, it is the foundational library for numerical computing in Python. It provides support for arrays, matrices, and many mathematical functions to operate on these data structures.
- Pandas: Provides high-performance, easy-to-use data structures like DataFrame (a table of data with rows and columns) and data analysis tools.
- Matplotlib: A plotting library that provides a MATLAB-like interface for making all sorts of plots and charts.
- Seaborn: Based on Matplotlib, Seaborn provides a high-level interface for drawing attractive statistical graphics, such as regression plots, heat maps, etc.
- SciPy: Used for scientific and technical computing. It extends NumPy by adding more modules for optimization, linear algebra, integration, interpolation, etc.
- Scikit-learn: A machine learning library that provides simple and efficient tools for data analysis and modeling. It’s built on NumPy, SciPy, and Matplotlib.
- Statsmodels: A library built specifically for statistics. It allows users to explore data, estimate statistical models, and perform statistical tests.
2.2 Installing and Importing Libraries
Before you can use a library, it has to be installed on your system. The Python package manager, pip, makes this easy. Here’s how you can install these libraries:
# Install NumPy, Pandas, Matplotlib, Seaborn, SciPy, scikit-learn, and statsmodels
pip install numpy pandas matplotlib seaborn scipy scikit-learn statsmodels
Once a library is installed, you need to import it to make its functions available in your code. Here’s how to import these libraries:
By convention, we often import libraries using a shorter alias. For example, numpy is often imported as np, and pandas as pd. This makes code less verbose and easier to write.
Let’s see how we can use these libraries in the following sections.
I hope this gives a good starting point for the “Working with Libraries” section. Feel free to ask for more details or any changes you’d like.
3. Data Acquisition
In data science, acquiring data is the first and one of the most crucial steps. Python, with its rich library ecosystem, provides many ways to load data from various sources.
3.1 Reading Data
3.1.1 CSV
We can read CSV files using the Pandas library’s read_csv function. In this case, we’ll use the ds_salaries.csv file, which contains salary information for data scientists sourced from Kaggle.
3.1.2 Excel
Excel files can also be read using Pandas’ read_excel function.
3.1.3 JSON
JSON (JavaScript Object Notation) is a popular data exchange format. We can read JSON files using Pandas’ read_json function.
3.1.4 APIs
APIs (Application Programming Interfaces) are often used to retrieve data from the internet. Python’s requests library makes it easy to pull data from APIs.
3.1.5 Web Scraping
We can use web scraping to extract data embedded in a webpage’s HTML. Python’s BeautifulSoup library is commonly used for this. Here, we’ll scrape the Wikipedia homepage.
3.1.6 SQL Databases
Python can interact with SQL databases using libraries like sqlite3 or sqlalchemy. Here is how to read data from an SQLite database using sqlite3 and convert it to a data frame.
3.2 Writing Data
Just as you can read data from various sources, you can also write data to different file formats using Pandas.
3.2.1 CSV
Write data to a CSV file using the to_csv function.
3.2.2 Excel
Write data to an Excel file using the to_excel function.
3.2.3 JSON
Write data to a JSON file using the to_json function.
3.2.4 SQL Databases
Write data to a SQL database using the to_sql function.
# Write DataFrame to a SQLite database
data.to_sql('new_tablename', conn, if_exists='replace', index=False)
3.3 Introduction to Pandas DataFrames
Pandas is a powerful data manipulation library. Its primary data structure is the DataFrame, a two-dimensional table of data with rows and columns.
Pandas is a powerful data manipulation library. Its primary data structure is the DataFrame, a two-dimensional table of data with rows and columns.
3.3.1 Creating a DataFrame
DataFrames can be created from a variety of data structures like dictionaries, lists, or even NumPy arrays.
3.3.2 Viewing Data
To view the first or last few rows of data, we can use the head() and tail() methods, respectively. The default number of rows is 5, but you can provide a different number as an argument.
3.3.3 DataFrame Shape
You can check the number of rows and columns in your DataFrame using the shape attribute.
3.3.4 Columns and Index
DataFrame’s columns and index (row labels) can be accessed using their respective attributes.
3.3.5 Data Selection
We can select data using column names, or with conditional indexing.
This is just the beginning of what you can do with Pandas and Python when it comes to data acquisition. As you explore more, you’ll find Python’s capabilities for this task are incredibly expansive and flexible. In the next section, we’ll delve into how to clean and preprocess this data.
4. Data Cleaning
Data cleaning is one of the most critical steps in any data science project. It involves correcting, removing, and imputing the inaccuracies or errors in a dataset. This section will introduce you to various data-cleaning techniques and how they can be implemented using Python.
4.1 Identifying Missing Data
The first step in data cleaning is identifying missing data. Missing data in the dataset can arise due to various reasons, and they can cause a significant problem for any machine learning model.
In Python, we use the pandas library for handling data and identifying missing data. The isnull() or isna() methods in pandas help to find the missing values in a DataFrame. Here’s how you can do it:
The isnull() function in pandas will return a DataFrame where each cell is either True or False depending on that cell’s null status.
4.2 Data Imputation Techniques
Once we have identified the missing data, the next step is to handle these missing values. One common way is through imputation, where the missing values are replaced with substituted values.
4.2.1 Mean/Median/Mode Imputation
This is one of the simplest methods where the missing values are replaced with the mean, median, or mode of the rest of the data in the column. This method is useful when the data is normally distributed.
4.2.2 Forward Fill and Backward Fill
Forward fill (ffill) carries forward the previous value, and backward fill (bfill) carries forward the next value. These methods are useful in the case of time series data where data observation is dependent on time.
4.2.3 Interpolation
Interpolation uses various interpolation techniques from the Scipy library to fill in missing values. This is most appropriate for numerical data which follows some trend.
4.2.4 Regression Imputation
In regression imputation, we use a regression model to predict missing values based on other data. It can lead to better results but can also introduce a higher level of noise to the data.
4.3 Handling Duplicates
Duplicate values can often be present in the data, and these can affect the results of data analysis. You can identify duplicate values in pandas using the duplicated() method and remove them using drop_duplicates().
4.4 Dealing with Inconsistent Data Types
In data science, it’s not uncommon to encounter a dataset where different columns contain different types of data. For example, one column may contain integers, while another contains strings. There may even be inconsistencies within the same column, with some data points registered as strings while others as integers or floating numbers.
Such inconsistencies in data types can pose serious challenges when processing and analyzing the data. Therefore, it’s crucial to ensure the data is consistent and that each column’s data type aligns with the nature of the data it contains.
4.5 Outlier Detection and Treatment
Outliers are data points that differ significantly from other observations. They can occur by chance in any distribution, but they often indicate errors or anomalies. Here we’ll go through some of the most common techniques for identifying and treating outliers.
4.5.1 Z-Score Method
The Z-Score method identifies outliers by finding data points that are too many standard deviations away from the mean. In the following code, we define outliers as points that have a Z-score absolute value higher than 3.
4.5.2 Interquartile Range (IQR) Method
The IQR method identifies outliers by finding data points that fall outside of the Interquartile Range (IQR). A common rule of thumb is that a data point is considered an outlier if it is less than Q1 – 1.5IQR or greater than Q3 + 1.5IQR.
4.5.3 Box Plot Method
Box plots are a graphical depiction of numerical data through their quartiles. Outliers may be plotted as individual points that are in line with whiskers. It gives a good visual representation of outliers.
In this plot, the data points that are shown above the top whisker and below the bottom whisker are considered outliers.
For treating outliers, one common way is to remove these observations from the dataset, as we’ve done in the Z-Score and IQR methods. But remember, this should be done cautiously as these could be valuable pieces of information. In some cases, they can be replaced with the upper or lower boundaries calculated from the IQR. In some other cases, especially in time-series data, you may use a method called winsorizing, which replaces the extreme values with certain percentiles. The decision is very case-specific.
In the next sections, we’ll delve deeper into data exploration and analysis.
5. Data Exploration and Analysis
Data exploration is a crucial step in data analysis, where we try to understand various aspects of the data. We use descriptive statistics and visualization techniques to understand the distribution of data, identify anomalies, and discover patterns. Here, we’ll cover descriptive statistics and data visualization using Matplotlib and Seaborn.
5.1 Descriptive Statistics
Descriptive statistics provide a summary of the data at hand through certain measures. They help us understand and describe the features of a specific dataset. Some of the common measures include mean, median, mode, standard deviation, and variance.
5.1.1 Mean, Median, and Mode
Let’s start by creating a pandas DataFrame and computing these measures.
5.1.2 Standard Deviation and Variance
The standard deviation is a measure of the amount of variation or dispersion of a set of values. Variance is a square of standard deviation. A low standard deviation means that the values tend to be close to the mean (also called the expected value) of the set, while a high standard deviation means that the values are spread out over a wider range.
5.2 Data Visualization
Data visualization is an important part of data exploration, as our brain is mainly an image processor, not a text processor. Visuals are processed faster and are easier to understand compared to text. Python provides several libraries for data visualization like Matplotlib, Seaborn, Plotly, etc. Here, we’ll cover Matplotlib and Seaborn.
5.2.1 Histograms
Histograms allow us to see the distribution of a numeric variable. We can create a histogram using Matplotlib.
5.2.2 Box plots
Box plots are used to display the summary of the set of data values having properties like minimum, first quartile, median, third quartile, and maximum. In the box plot, a box is created from the first quartile to the third quartile, a vertical line is also there which goes through the box at the median.
5.2.3 Scatter plots
Scatter plots are used to find the correlation between two variables. Here, dots are used to represent the values obtained for the two variables.
5.2.4 Line charts
Line charts are used to represent the relation of two data series on different axes: X and Y.
5.2.5 Heatmaps
Heatmaps are used to visualize data in 2D that can be created by using two categories. One category will be represented on the X-axis, the second category on the Y-axis, and the correlation will be shown in the intensity of color.
5.3 Exploratory Data Analysis (EDA)
Exploratory Data Analysis refers to the critical process of performing initial investigations on data so as to discover patterns, to spot anomalies, to test hypotheses, and to check assumptions with the help of summary statistics and graphical representations. It involves all of the techniques described above.
If you want to deep dive into EDA, please check out the detailed articles in the Data Analysis section.
5.4 Correlation and Covariance
Correlation is a measure that determines the degree to which two variables’ movements are associated. Covariance provides insight into how two variables are related to one another. More precisely, it measures the degree to which two variables move in tandem relative to their averages.
In the next section, we’ll learn how to manipulate data with pandas which is crucial for preparing data for machine learning models.
6. Data Manipulation with Pandas
Pandas is a high-level data manipulation tool developed by Wes McKinney. It is built on the Numpy package and its key data structure is called the DataFrame. DataFrames allow you to store and manipulate tabular data in rows of observations and columns of variables. Let’s dive into how to use Pandas for data manipulation.
6.1 Filtering and Selecting Data
Filtering and selecting data in Pandas involves selecting specific rows and columns of data from a DataFrame.
Here’s an example of how to create a DataFrame and filter data:
6.2 Sorting Data
Sorting data in Pandas can be done with the sort_values() function. You can sort the data in ascending or descending order.
Here’s an example of sorting data:
6.3 Grouping and Aggregating Data
Pandas has a handy groupby function which allows you to group rows of data together and call aggregate functions.
Here’s an example of grouping and aggregating data:
6.4 Merging and Joining DataFrames
Pandas provide various ways to combine DataFrames including merge and join. In this section, we will cover how to use these functions.
Here’s an example of merging two DataFrames:
6.5 Reshaping DataFrames: Pivoting, Stacking, Melting
Reshaping data refers to the process of changing the way data is organized into rows and columns. In Pandas, we can reshape data in various ways, such as pivoting, stacking, and melting.
Here’s an example of reshaping data using the melt function:
Pandas is a powerful tool for data manipulation in Python. Mastering these functionalities will help you in your data science journey.
In the next section, we will delve into NumPy, another important Python library for data science.
7. Introduction to NumPy
NumPy, which stands for Numerical Python, is a fundamental package for scientific computing and data analysis in Python. It introduces a simple yet powerful n-dimensional array object, which makes it a go-to package for numerical operations on arrays, especially for data analysis, machine learning, and other data-driven tasks. In this section, we will explore some key concepts and operations in NumPy.
7.1 Understanding NumPy Arrays
NumPy arrays, or simply numpy.ndarray, are somewhat like native Python lists but are capable of performing mathematical operations on an element-by-element basis. You can also create multi-dimensional arrays.
7.2 Array Operations
With NumPy, you can perform various operations such as addition, multiplication, reshaping, slicing, and indexing.
7.2.1 Addition
Element-wise addition of two arrays.
7.2.2 Multiplication
Element-wise multiplication of two arrays.
7.2.3 Reshaping
Changing the number of rows and columns in the array.
7.2.4 Slicing
Getting a subset of the array.
7.2.5 Indexing
Getting a specific element of the array.
7.3 NumPy Functions
NumPy also provides a host of functions to perform mathematical and statistical operations.
7.3.1 Mathematical Functions
For example, you can easily calculate the sine of all elements.
7.3.2 Statistical Functions
Functions like mean(), median(), std(), and many others provide descriptive statistics on NumPy arrays.
In the next section, we’ll explore how to work with dates and times in Python, which is a crucial part of handling time-series data.
8. Working with Dates and Times
Working with date and time data is crucial in many data science projects. Python provides robust modules to handle date and time data efficiently. Let’s dive in to understand this in detail.
8.1 Python’s datetime Module
Python’s built-in datetime module can be used to create and manipulate date and time objects.
This code demonstrates how to get the current date and time and how to extract specific components from a datetime object.
8.2 Converting Strings to Dates
Often, dates are imported as strings from CSV files or databases. These need to be converted to datetime objects for efficient manipulation. Let’s see how this can be done.
In the above code, we use strptime() function, which allows us to specify the format of the date string.
8.3 Extracting Date Components
Python allows us to extract specific components from a datetime object, like the year, month, day, etc.
The above code demonstrates how to extract year, month, and day from a datetime object.
8.4 Time Series Analysis with Pandas
Pandas library is quite powerful when it comes to handling and analyzing time-series data.
In this example, we create a DataFrame with a date range and set the Date column as the index. This is a common operation in time series analysis.
8.5 Advanced Time Series Analysis
Now that we have a handle on Python’s datetime module and working with dates and times in Pandas, let’s dive deeper into some more complex aspects of time series analysis.
8.5.1 Resampling
When working with time-series data, we often need to change the frequency of our data points. This can be done through the process of resampling. Pandas provide convenient methods for frequency conversion.
8.5.1.1 Downsampling
Downsampling involves reducing the frequency of the data points, such as from daily data to monthly data. The resample function in pandas is similar to the groupby function – it groups data into set time intervals. It’s then followed by an aggregation method to summarise the data.
8.5.1.2 Upsampling
Upsampling is increasing the frequency of the data points, like from hourly data to minute-by-minute data. After resampling, we often need to interpolate to fill null values.
8.5.2 Shifting and Lagging
Shifting dataset values a certain amount back or forwards in time can be useful in creating features for machine learning models.
8.5.3 Rolling Windows
Rolling windows can be used to calculate values such as rolling averages over a specific period.
8.5.4 Handling Time Zones
8.5.5 Period and Period Arithmetic
Periods represent timespans, like days, months, quarters, or years.
These are some of the more advanced operations you can perform with dates and times in Python and pandas. They offer a lot of flexibility when dealing with complex time series data.
In the next section, we will deep dive into Statistical Analysis in Python.
9. Introduction to Statistical Analysis
Statistical analysis is a critical component of data science and involves collecting, analyzing, interpreting, presenting, and modeling data. In this section, we’ll learn about probability distributions, hypothesis testing, and regression analysis.
Descriptive Statics has been already covered in Data Exploration and Analysis section. In this section, we will learn a little advanced Statistical Analysis in Python.
9.1 Probability Distributions
Probability distributions are mathematical functions that provide the probabilities of the occurrence of different possible outcomes in an experiment. They form the basis of various statistical techniques. The two most common types of distribution in statistics are the Normal distribution and the Binomial distribution.
9.1.1 Binomial Distribution
A binomial distribution can be thought of as the probability of a SUCCESS or FAILURE outcome in an experiment or survey that is repeated multiple times.
Here’s how you can generate a binomial distribution with Python:
9.1.2 Normal Distribution
The normal distribution, also known as the Gaussian distribution, is a probability distribution that is symmetric about the mean, showing that data near the mean are more frequent in occurrence than data far from the mean. In graph form, the normal distribution appears as a bell curve.
Let’s generate a normal distribution:
9.2 Hypothesis Testing
Hypothesis testing is a statistical method that is used in making statistical decisions using experimental data. It is basically an assumption that we make about the population parameter.
9.2.1 T-test
The T-test is a type of inferential statistic used to determine if there is a significant difference between the means of two groups. The assumption for the test is the data is distributed normally.
Here’s an example of performing a T-test in Python:
9.2.2 Chi-squared test
The Chi-Square test of independence is a statistical test to determine if there is a significant association between two variables.
Here’s how you can perform a Chi-squared test:
9.3 Regression Analysis
Regression analysis is a set of statistical methods used for the estimation of relationships between a dependent variable and one or more independent variables.
9.3.1 Linear Regression
Linear regression attempts to model the relationship between two variables by fitting a linear equation to observed data.
9.3.2 Logistic Regression
Logistic regression is a statistical method for predicting binary classes. The outcome or target variable is binary in nature.
Let’s see how to perform logistic regression in Python:
That concludes our introduction to statistical analysis. With an understanding of these concepts, you’ll have a solid foundation to understand and use many of the algorithms used in data science.
In the next section, we will look at how to pre-process data for machine learning models.
10. Data Preprocessing for Machine Learning
Data preprocessing is an integral step in Machine Learning as the quality of data and the useful information that can be derived from it directly affects the ability of our model to learn; therefore, it directly affects the model’s performance. This step involves feature scaling techniques, handling categorical data, and preparing our data for the machine learning model.
10.1 Feature Scaling: Standardization, Normalization
When we’re working with data, we may find that some features have vastly different scales than others. This discrepancy can cause issues with certain machine learning algorithms (like those that use Euclidean distance). To resolve this, we can use feature scaling methods such as Standardization and Normalization.
10.1.1 Standardization
Standardization rescales the features such that they’ll have the properties of a standard normal distribution with a mean of zero and a standard deviation of one.
Let’s see how it works with the help of Python’s Scikit-learn library. We’ll use the popular Iris dataset for this demonstration.
Please note: you should split your data into a training set and a test set before scaling, but to keep the dataset small and the example simple, we’re scaling the entire dataset.
10.1.2 Normalization
Normalization (or Min-Max Scaling) rescales the features to a fixed range, typically 0 to 1, or -1 to 1 if there are negative values.
Again, let’s see how it works using Scikit-learn and the Iris dataset.
10.2 Handling Categorical Data: One-hot Encoding, Label Encoding
Often, our datasets contain categorical variables. These variables need to be encoded into a form that the machine learning algorithms can understand. The two most common forms of categorical data encoding are Label Encoding and One-hot Encoding.
10.2.1 Label Encoding
Label Encoding is a popular encoding technique for handling categorical variables. In this technique, each label is assigned a unique integer based on alphabetical ordering.
Let’s use a simple example:
10.2.2 One-hot Encoding
One-hot Encoding, on the other hand, creates additional features based on the number of unique values in the categorical feature. Every unique value in the category will be added as a feature.
Let’s use the same simple example:
10.3 Train-Test Split
Before training our machine learning model, we need to split our dataset into a training set and a test set. The training set is used to train the model, while the test set is used to evaluate the model’s performance.
Let’s see how we can perform a train-test split using Scikit-learn:
10.4 Cross-validation
Cross-validation is a resampling procedure used to evaluate machine learning models on a limited data sample. The most common technique is called k-fold cross-validation, where the data set is divided into k subsets, and the holdout method is repeated k times. Each time, one of the k subsets is used as the test set, and the other k-1 subsets are put together to form a training set.
Let’s see a simple example of cross-validation using Scikit-learn:
That’s it! With these steps, you are now ready to prepare any dataset for machine learning. The next step is to train your model using these preprocessed data and start making predictions!
11. Machine Learning with Scikit-learn
Machine Learning is a vast topic that often forms the core of many data science projects. Python has many libraries dedicated to machine learning, and Scikit-learn is one of the most popular ones due to its robustness and ease of use. In this section, we’ll look at the basics of machine learning and how to implement simple machine learning algorithms using Scikit-learn.
11.1 Supervised Learning
Supervised learning is a type of machine learning where we train a model using labeled data. In other words, we have a dataset where the correct outcomes are already known. We can break down supervised learning into two categories: Classification and Regression.
11.1.1 Classification
Classification is a type of supervised learning where the output is a category. For example, whether an email is spam or not spam is a classification problem.
Let’s use Scikit-learn to implement a simple classification algorithm called Logistic Regression. We’ll use the famous iris dataset for our classification task.
11.1.2 Regression
Regression, on the other hand, is a type of supervised learning where the output is a continuous value. For example, predicting the price of a house based on its features is a regression problem.
We can use the housing dataset to implement a simple linear regression model using Scikit-learn. This dataset contains information about various houses in California, such as their location, size, and median house price.
11.2 Unsupervised Learning
Unsupervised learning involves training a machine learning model using data that is not labeled. This means that the correct outcomes are unknown, and the algorithm must find patterns in the data on its own. Clustering is a common type of unsupervised learning.
11.2.1 Clustering
Clustering involves grouping together similar data points. K-means is a popular clustering algorithm. Here’s how we can use K-means to cluster our iris data into three groups:
11.3 Model Evaluation Metrics
Once we have trained a machine learning model, we need to evaluate its performance. Different metrics can be used depending on the type of machine learning algorithm used.
For classification problems, common metrics include accuracy, precision, recall, and the F1 score. For regression problems, we often use the Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and R² score.
Here’s how to calculate these metrics for our previous models using Scikit-learn:
11.4 Hyperparameter Tuning
Hyperparameters are parameters whose values are set before the learning process begins. Different models have different hyperparameters, and the performance of our model can change based on the values of these hyperparameters.
Scikit-learn provides several methods for hyperparameter tuning, including GridSearchCV and RandomizedSearchCV. Here’s an example of using GridSearchCV to tune the hyperparameters of a support vector classifier:
And that’s it! This is a very high-level overview of machine learning with Scikit-learn. These are very basic models, and real-world datasets require a much more careful and sophisticated approach. However, this should give you a good starting point for further exploration in the field of machine learning.