![Top Machine Learning Projects in Python For Beginners [2024]](/__khugblog-next/image/?url=https%3A%2F%2Fd14b9ctw0m6fid.cloudfront.net%2Fugblog%2Fwp-content%2Fuploads%2F2020%2F09%2F915-01.png&w=1920&q=75)
If you want to become a machine learning professional, you’d have to gain experience using its technologies. The best way to do so is by completing projects. That’s why in this article, we’re sharing multiple machine learning projects in Python so you can quickly start testing your skills and gain valuable experience.
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However, before you begin, make sure that you’re familiar with machine learning and its algorithm. If you haven’t worked on a project before, don’t worry because we have also shared a detailed tutorial on one project:
The Iris Dataset: For the Beginners
The Iris dataset is easily one of the most popular machine learning projects in Python. It is relatively small, but its simplicity and compact size make it perfect for beginners. If you haven’t worked on any machine learning projects in Python, you should start with it. The Iris dataset is a collection of flower sepal and petal sizes of the flower Iris. It has three classes, with 50 instances in every one of them.
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We’ve provided sample code on various places, but you should only use it to understand how it works. Implementing the code without understanding it would fail the premise of doing the project. So be sure to understand the code well before implementing it.
Step 1: Import the Libraries
The first step of any machine learning project is importing the libraries. A primary reason why Python is so versatile is because of its robust libraries. The libraries we’ll need in this project are:
- Pandas
- Matplotlib
- Sklearn
- SciPy
- NumPy
There are multiple methods to import libraries into your system, and you should use a particular way to import all the libraries. It would ensure consistency and help you avoid any confusion. Note that installation varies according to your device’s Operating System, so keep that in mind while importing libraries.
Code:
# Load libraries
from pandas import read_csv
from pandas.plotting import scatter_matrix
from matplotlib import pyplot
from sklearn.model_selection import train_test_split
from sklearn.model_selection import cross_val_score
from sklearn.model_selection import StratifiedKFold
from sklearn.metrics import classification_report
from sklearn.metrics import confusion_matrix
from sklearn.metrics import accuracy_score
from sklearn.linear_model import LogisticRegression
from sklearn.tree import DecisionTreeClassifier
from sklearn.neighbors import KNeighborsClassifier
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
from sklearn.naive_bayes import GaussianNB
from sklearn.svm import SVC
Read: Top 10 Machine Learning Datasets Project Ideas For Beginners
Step 2: Load the Dataset
After importing the libraries, it’s time to load the dataset. As we discussed, we’ll use the Iris dataset in this project. You can download it from here.
Ensure that you specify every column’s names while loading the data, and it would help you later on in the project. We recommend downloading the dataset, so even if you face connection problems, your project will remain unaffected.
Code:
# Load dataset
url = “https://raw.githubusercontent.com/jbrownlee/Datasets/master/iris.csv”
names = [‘sepal-length’, ‘sepal-width’, ‘petal-length’, ‘petal-width’, ‘class’]
dataset = read_csv(url, names=names)
Step 3: Summarizing
Before we start using the dataset, we must first look at the data present in it. We’ll begin by checking the dataset’s dimension, which shows us that the dataset has five attributes and 150 instances.
After checking the dimension, you should look at a few rows and columns of the dataset to give you a general idea of its content. Then you should look at the statistical summary of the dataset and see which metrics are the most prevalent in the same.
Finally, you should check the class distribution in the dataset. That means you’d have to check how many instances fall under each class. Here’s code for summarizing our dataset:
# summarize the data
from pandas import read_csv
# Load dataset
url = “https://raw.githubusercontent.com/jbrownlee/Datasets/master/iris.csv”
names = [‘sepal-length’, ‘sepal-width’, ‘petal-length’, ‘petal-width’, ‘class’]
dataset = read_csv(url, names=names)
# shape
print(dataset.shape)
# head
print(dataset.head(20))
# descriptions
print(dataset.describe())
# class distribution
print(dataset.groupby(‘class’).size())
Step 4: Visualize the Data
After summarizing the dataset, you should visualize it for better understanding and analysis. You can use univariate plots to analyze every attribute in detail and multivariate plots to study every feature’s relationships. Data visualization is a crucial aspect of machine learning projects as it helps find essential information present within the dataset.
Step 5: Algorithm Evaluation
After visualizing the data, we’ll evaluate several algorithms to find the best model for our project. First, we’ll create a validation dataset which we’ll take out from the original one. Then we’ll employ 10-fold cross-validation and create various models. As already discussed, we aim to predict the species through the measurements of the flowers. You should use different kinds of algorithms and pick out the one which yields the best results. You can test SVM (Support Vector Machines), KNN (K-Nearest Neighbors), LR (Logistic Regression), and others.
In our implementation, we found SVM to be the best model. Here’s the code:
from pandas import read_csv
from matplotlib import pyplot
from sklearn.model_selection import train_test_split
from sklearn.model_selection import cross_val_score
from sklearn.model_selection import StratifiedKFold
from sklearn.linear_model import LogisticRegression
from sklearn.tree import DecisionTreeClassifier
from sklearn.neighbors import KNeighborsClassifier
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
from sklearn.naive_bayes import GaussianNB
from sklearn.svm import SVC
# Load dataset
url = “https://raw.githubusercontent.com/jbrownlee/Datasets/master/iris.csv”
names = [‘sepal-length’, ‘sepal-width’, ‘petal-length’, ‘petal-width’, ‘class’]
dataset = read_csv(url, names=names)
# Split-out validation dataset
array = dataset.values
X = array[:,0:4]
y = array[:,4]
X_train, X_validation, Y_train, Y_validation = train_test_split(X, y, test_size=0.20, random_state=1, shuffle=True)
# Spot Check Algorithms
models = []
models.append((‘LR’, LogisticRegression(solver=’liblinear’, multi_class=’ovr’)))
models.append((‘LDA’, LinearDiscriminantAnalysis()))
models.append((‘KNN’, KNeighborsClassifier()))
models.append((‘CART’, DecisionTreeClassifier()))
models.append((‘NB’, GaussianNB()))
models.append((‘SVM’, SVC(gamma=’auto’)))
# evaluate each model in turn
results = []
names = []
for name, model in models:
kfold = StratifiedKFold(n_splits=10, random_state=1, shuffle=True)
cv_results = cross_val_score(model, X_train, Y_train, cv=kfold, scoring=’accuracy’)
results.append(cv_results)
names.append(name)
print(‘%s: %f (%f)’ % (name, cv_results.mean(), cv_results.std()))
# Compare Algorithms
pyplot.boxplot(results, labels=names)
pyplot.title(‘Algorithm Comparison’)
pyplot.show()
Step 6: Predict
After you have evaluated different algorithms and have chosen the best one, it’s time to predict the outcomes. We’ll use our model on the validation dataset first to see test its accuracy. After that, we’ll test it on the entire dataset.
Here’s the code for running our model on the dataset:
# make predictions
from pandas import read_csv
from sklearn.model_selection import train_test_split
from sklearn.metrics import classification_report
from sklearn.metrics import confusion_matrix
from sklearn.metrics import accuracy_score
from sklearn.svm import SVC
# Load dataset
url = “https://raw.githubusercontent.com/jbrownlee/Datasets/master/iris.csv”
names = [‘sepal-length’, ‘sepal-width’, ‘petal-length’, ‘petal-width’, ‘class’]
dataset = read_csv(url, names=names)
# Split-out validation dataset
array = dataset.values
X = array[:,0:4]
y = array[:,4]
X_train, X_validation, Y_train, Y_validation = train_test_split(X, y, test_size=0.20, random_state=1)
# Make predictions on validation dataset
model = SVC(gamma=’auto’)
model.fit(X_train, Y_train)
predictions = model.predict(X_validation)
# Evaluate predictions
print(accuracy_score(Y_validation, predictions))
print(confusion_matrix(Y_validation, predictions))
print(classification_report(Y_validation, predictions))
That’s it. You have now completed a machine learning project in Python by using the Iris dataset.
Additional Machine Learning Projects in Python
The Iris dataset is primarily for beginners. If you have some experience working on machine learning projects in Python, you should look at the projects below:
1. Use ML to Predict Stock Prices
An excellent place to apply machine learning algorithms is the share market. Companies are using AI algorithms and ML-based technologies to perform technical analysis for quite some time now. You can also build an ML model that predicts stock prices.
However, to work on this project, you’ll have to use several techniques, including regression analysis, predictive analysis, statistical modelling, and action analysis. You can get the necessary data from the official websites of stock exchanges. They share data on the past performances of shares. You can use that data to train and test your model.
As a beginner, you can focus on one particular company and predict its stock value for three months. Similarly, if you want to make the project challenging, you can use multiple companies and extend your prediction timelines.
What You’ll Learn from This Project:
This project will make you familiar with the applications of AI and ML in the finance industry. You can also study predictive analysis through this project and try different algorithms.
2. Write a Machine Learning Algorithm from Scratch
If you’re a beginner and haven’t worked on any machine learning projects in Python, you can also start with this one. In this project, you have to build an ML algorithm from scratch. Doing this project will help you understand all the basics of the algorithm’s functions while also teaching you to convert mathematical formulae into machine learning code.
Knowing how to convert mathematical concepts into ML code is crucial, as you’ll have to implement it many times in the future. As you’ll tackle more advanced problems, you’ll have to rely on this skill. You can pick any algorithm according to your familiarity with its concepts. It would be best to start with a simple algorithm if you lack experience.
What You’ll Learn from This Project:
You’ll get familiar with the mathematical concepts of artificial intelligence and machine learning.
3. Create a Handwriting Reader
This is a computer vision project. Computer vision is the sector of artificial intelligence related to image analysis. In this project, you’ll create an ML model that can read handwriting. Reading means the model should be able to recognize what’s written on the paper. You’d have to use a neural network in this project to be familiar with deep learning and its relevant concepts.
You’ll first have to pre-process the image and remove unnecessary sections; in other words, perform data cleaning on the image for clarity. After that, you will have to perform segmentation and resizing of the image so the algorithm can read the characters correctly. Once you have completed pre-processing and segmentation, you can move onto the next step, classification. A classification algorithm will distinguish the characters present in the text and put them in their respective categories.
You can use log sigmoid activation to train your ML algorithm for this project.
What You’ll Learn from This Project:
You’ll get to study computer vision and neural networks. Completing this project will also make you familiar with image recognition and analysis.
4. A Sales Predictor
The retail sector has many applications for AI and machine learning. In this project, you’ll discover one such application, that is, predicting sales of products.
A prevalent dataset among machine learning enthusiasts is the BigMart sales dataset. It has more than 1559 products spread across its various outlets in 10 cities. You can use the dataset to build a regression model. According to the outlets, your model has to predict the potential sales of particular products in the coming year. This dataset has specific attributes for every outlet and product to understand their properties and the relation between the two quickly.
What You’ll Learn from This Project:
Working on this project will make you familiar with regression models and predictive analysis. You will also learn about the applications of machine learning in the retail sector.
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We hope that you found this list of machine learning projects in Python useful. If you have any questions or thoughts, please let us know through the comment section. We’d love to answer your queries.
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