In today’s data-driven world, the demand for machine learning solutions continues to soar, revolutionizing industries and transforming how businesses operate. To help you better understand the complex landscape of machine learning, we want to take you through the essential steps of the machine learning workflow. We’ll highlight the significance of each stage and show you how you can implement them using Python. So, let’s get started!
Fuelling the Model: Acquiring Your Data
Get an Existing Datasets: There are numerous online repositories to choose from, offering a wealth of datasets ready to download and use.
- UCI Machine Learning Repository (https://archive.ics.uci.edu/),
- Kaggle (https://www.kaggle.com/),
- TensorFlow Datasets (https://www.tensorflow.org/datasets)
Craft Your Dataset: Scrape data from websites using libraries like BeautifulSoup or Selenium, or build a data collection system specific to your needs.
Exploring Your Data:
Understand the data you are working with. Familiarize yourself with all the aspects of the dataset, this will help you get a clear idea of which algorithm to implement. Pandas is a primary tool used to manipulate and explore data.
import pandas as pd
#use pandas to read your dataset
df = pd.read_csv(“path_to_your_file/data.csv”)
#use pandas to print a summary of your dataset
df.describe()
#or you can use
df.info()You can also use matplotlib.pyplot or seaborn to visualize your data. Visualizing your data will help you interpret different patterns and relations between features in your data.
Pre-processing Your Data:
Rawdata often comes in an unusable format, containing inconsistencies like duplicates, missing entries, and outliers. These imperfections can significantly affect our model’s performance. To address these issues and ensure our data is in a suitable format for analysis, data preprocessing is crucial.
Let’s start with missing values: We can either replace the missing values with estimated values (Mean, Median, Mode) or delete them from the dataset. Note: Axis = 0 ( rows ), Axis = 1 ( columns )
df.dropna( axis=1 , inplace = True)Here inplace means that you will be making changes in the original dataset. If you choose not to alter the original dataset, you can create a copy and work on it.
You can fill in the missing values in a pandas DataFrame by calculating the mean, median, or mode of each column and using df.fillna() to replace the missing values with the corresponding statistic.
Inconsistent data format: Some formats allow us to write date as DD/MM/YYYY or YYYY-MM-DD. Choose a standard format. Not just date but all data entries must be consistent.
Remove duplicate entries: Our model will be highly inaccurate if it is trained repeatedly on the same data. Hence it is important to remove duplicate entries. This can be done in pandas.
#identify duplicate rows
print(df.duplicated())
#returns True for every duplicate row
df.drop_duplicates(inplace = True)Feature Selection and Extraction:
Features serve as input variables to statistical models, machine learning algorithms, or analytical techniques, enabling the extraction of meaningful patterns, relationships, and insights from data. Well-engineered features can help models generalize better, improve prediction accuracy, and make them more robust to unseen data.
Feature Engineering is a vast topic itself, it is not something we can cover in this blog. However, if you’re interested in learning more about it, I’d be happy to point you in the right direction. Refer ‘https://thenucleargeeks.com/2020/06/03/feature-selection-in-machine-learning-introduction/ ‘ to gain better understanding of Feature Engineering.
Choosing the Right Tool: Algorithm Selection
Here comes the most crucial step:
This step involves choosing the appropriate machine learning algorithm(s) to solve a specific problem based on the characteristics of the dataset and the requirements of the task at hand. This step is crucial, as the choice of algorithm can significantly impact the performance.
A few factors you need to consider while selecting your model are :
- Task Type: The problem you aim to solve using the model, is it regression (predictive model )? or classification (spam vs not spam) ?
- Characteristics of available data: Size of the data, it’s complexity, format of the data. The amount of data for training varies in different algorithms.
- Do you intend to build a model that is accurate ? Like a black-box where only the output matters. Or an interpretable model, where one can understand the working of the algorithm?
- Future goals of your model, do you plan to scale up your model ? What will your requirements be then ?
Take your time to explore the available algorithms. This will lay a solid foundation to help you build a solution to your problem. If you are not sure, feel free to experiment with different algorithms before choosing the best fit for your data. Scikit-learn makes implementing machine learning algorithms in Python a breeze. With just a few lines of code, you can train models for tasks like classification, regression, and clustering on your data. This streamlines the experimentation process, letting you focus on choosing the best algorithm for your specific problem.
Get Building: Your Machine Learning Journey Begins
Great going! Now you are all set to get started with your Machine Learning model. You have the data, you know the algorithm lets move ahead.
In order to make our model perform the desired tasks, we must first train it. We train our model on a portion of the data to teach it to identify patterns. Testing on a separate portion then shows how well it can apply those learnings to completely new data, ensuring it performs well in real-world scenarios.
In the upcoming blog I will cover various machine learning algorithms, we will understand how to tackle problems like overfitting & underfitting and fine-tune our models for optimal performance.
Thank you for reading !! Keep learning, keep exploring.
THE NUCLEAR GEEKS 