The technology landscape is undergoing a profound shift, primarily attributed to the transformative force of machine learning. This groundbreaking technology has ushered in a new era, reshaping our approach to business, technology interaction, and daily existence.
It is estimated that the global machine learning market is poised to achieve remarkable heights, projected to reach an impressive $117.19 billion by 2027! This surge is a testament to the burgeoning demand for artificial intelligence and machine learning solutions.
For now, let us delve into machine learning steps and illustrate its practical Python implementation.
What Is Machine Learning?
Machine learning empowers computers to unravel patterns from data without explicit instructions. Unlike traditional computing, which relies on fixed rules, machine learning delves into inference and autonomy.
The essence of machine learning is more intricate than this initial portrayal. It encompasses multifaceted models far beyond mere thresholds. Think about predicting customer churn using past data – foreseeing who might depart before it occurs.
Modern machine learning has propelled us beyond, fueling advancements like self-driving cars, voice recognition, and email filters that sift through spam.
Wondering how all of this is achievable?
Let us take you through data preprocessing in machine learning, which is the core of machine learning.
Machine Learning Steps
From data collection to efficient data preparation for machine learning, here’s how machine learning steps are followed to field revolutionary advancements.
Collecting Data
This phase typically depicts data, often distilled to a structured format like a table articulated by Guo, serving as our training foundation. This process seamlessly accommodates the utilisation of pre-existing data, including datasets sourced from platforms like Kaggle or UCI, aligning harmoniously within this stage.
Preparing the Data
This step prepares for refinement by tending to data hygiene, encompassing tasks such as purging duplicates, rectifying errors, handling gaps, standardising scales, and converting data types as needed.
It Infuses randomness into the dataset, a strategic manoeuvre that obliterates any trace of data collection or preparation sequence, fostering impartiality. Then it engages in data visualisation, a perceptive exercise that uncovers pertinent connections among variables, unveils potential class disparities and beckons exploratory analyses.
Choosing a Model
At the heart of the machine learning process lies the model, a decisive factor in the outcomes yielded by applying machine learning algorithms to the amassed data. Over time, the ingenuity of scientists and engineers has birthed an array of models meticulously tailored for diverse undertakings – from deciphering speech and images to predictive analytics and beyond.
A crucial dimension of this selection process involves assessing the model’s compatibility with the nature of the data – be it numerical or categorical – and making an informed choice that aligns seamlessly with the data’s essence.
Training the Model
With the foundation set, we proceed to the pivotal phase of model training, a transformative endeavour to enhance performance and attain superior outcomes for the given challenge. Armed with datasets, we refine the model’s capabilities by applying diverse machine learning algorithms.
This process imparts proficiency and fortifies the model’s aptitude for delivering optimal results.
Evaluating the Model
The evaluation stage employs specific metrics or a fusion to accurately gauge the model’s objective performance. This entails subjecting the model to previously unseen data meticulously selected to resemble real-world scenarios.
It’s important to note that this distinct set of unseen data, compared to purely test data, strikes a balance between mirroring real-world dynamics and aiding model enhancement.
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Parameter Tuning
Upon crafting and assessing your model, the quest for enhanced accuracy comes to the fore, compelling a meticulous exploration of potential avenues for refinement. This endeavour centres around parameter tuning, a nuanced practice involving the adjustment of variables within the model – parameters that are predominantly under the programmer’s purview.
Parameter tuning embodies the meticulous process of unearthing these precise values, unravelling the intricacies that unlock heightened performance and propel the model’s efficacy to new heights.
Making Predictions
Advancing in the evaluation journey, a fresh reservoir of test data, previously shielded from the model’s grasp, emerges as the litmus test for its prowess. This data subset is distinguished by its possession of known class labels, an invaluable facet that enhances the accuracy of the assessment.
This dynamic interplay thoroughly scrutinises the model’s mettle, offering a more faithful glimpse into its real-world performance.
How to Implement Machine Learning Steps in Python?
Dive into the intriguing world of machine learning with Python. Let’s set up a machine learning model, step by step.
1. Loading The Data
Our dataset focuses on patient charges. To enhance your understanding, please download this dataset and code with us.
Begin by importing Pandas, our go-to library for data handling.
import pandas as pd |
Pandas is a remarkable resource for data loading and processing. Utilise the read_csv function to get our dataset.
data = pd.read_csv("insurance.csv") |
A sneak peek into the dataset can be availed using the head function.
data.head() |
The dataset has columns like age, sex, BMI, children count, smoking habits, region, and charges.
2. Comprehending The Dataset:
Before embarking on the machine learning journey, it’s imperative to know your data. Start by discovering the size of your dataset.
data.shape |
(1330, 7) |
Clearly, with 1338 rows and 7 columns, it’s a sizable dataset. Delve deeper with the info function.
data.info() |
Suspect missing values? Use the isnull function coupled with sum to tally them.
data.isnull() |
We’ll use the sum method to calculate the total sum of missing data.
data.isnull().sum() |
As we can see, the dataset is full for all the entries. The next step is being aware of column data types is pivotal for model creation. Check out the data types.
data.dtypes
|
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3. Data Preprocessing
Preprocessing in machine learning often involves converting object types to categorical types.
data['sex'] = data['sex'].astype('category') |
data['region'] = data['region'].astype('category')
|
data['smoker'] = data['smoker'].astype('category')
|
Some other data types are:
data.dtypes
|
To understand the numeric data better, consider using the describe function and its transpose for better readability.
data.describe().T |
Explore the distinction in average charges for smokers and non-smokers. Group the data to highlight differences.
smoke_data = data.groupby("smoker").mean().round(2) |
The result–
smoke_data
|
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4. Data Visualisation
For deeper insights into numeric correlations, employ seaborn.
import seaborn as sns |
Seaborn, an extension of matplotlib, is a gem for statistical visualisations. Set an aesthetic theme and get started.
sns.set_style("whitegrid") |
We’ll utilise the pairplot method to visualise the correlations among numeric variables.
sns.pairplot( data[["age", "bmi", "charges", "smoker"]], hue = "smoker", height = 3, palette = "Set1") |
Furthermore, heatmaps provide an excellent way to visualise correlations.
sns.heatmap(data.corr(), annot= True) |
One-Hot Encoding
Transition to one-hot encoding for categorical variables using the get_dummies function.
data = pd.get_dummies(data) |
Recheck your columns to understand the transformation.
data.columns |
Having revamped our dataset, we’re poised for model creation.
5. Developing a Regression Model
Kick-off model creation by discerning input-output variables. Assign ‘charges’ as our target, ‘y’.
y = data["charges"] |
X = data.drop("charges", axis = 1)
|
Separate training and test data using scikit-learn’s train_test_split function.
from sklearn.model_selection import train_test_split |
X_train,X_test,y_train,y_test=train_test_split( X,y, train_size = 0.80, random_state = 1) |
For model creation, lean on linear regression.
from sklearn.linear_model import LinearRegression |
Now, create an instance of the LienarRegression class.
lr = LinearRegression() |
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6. Model Evaluation
The coefficient of determination, closer to 1, signals a better fit.
lr.score(X_test, y_test).round(3) #Output: 0.762 |
Inspect the model’s prediction quality using mean squared error.
lr.score(X_train, y_train).round(3) #Output: 0.748 |
y_pred = lr.predict(X_test) |
from sklearn.metrics import mean_squared_error |
import math |
Now, let’s examine the square root of the mean squared error.
math.sqrt(mean_squared_error(y_test, y_pred)) #Output: 5956.45 |
This value indicates that the model’s predictions exhibit a standard deviation 5956.45.
7. Model Prediction
Showcase the prediction process using a sample from the training data.
data_new = X_train[:1] |
This is the predicted data with our model.
lr.predict(data_new) #Output: 10508. 42 |
This is the real value.
y_train[:1] #Output: 10355.64 |
The real and predicted values are notably close, validating our model’s accuracy.
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Conclusion
In the rapidly evolving landscape of technology, the prowess of machine learning is scaling new heights with each passing day. This transformative field holds immense promise, not only shaping industries but also extending its reach to the realms of education and professional growth.
In this dynamic scenario, upGrad’s MS in Full Stack AI and ML from Golden Gate University emerges as a beacon of advanced education. With a comprehensive curriculum, this program empowers individuals to design, develop, and deploy AI-based solutions tailored to real-world business challenges.
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