Trending February 2024 # Github Integration With Selenium: Complete Tutorial # Suggested March 2024 # Top 7 Popular

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Git Hub is a Collaboration platform. It is built on top of git. It allows you to keep both local and remote copies of your project. A project which you can publish it among your team members as they can use it and update it from there itself.

Advantages of Using Git Hub For Selenium.

When multiple people work on the same project they can update project details and inform other team members simultaneously.

Jenkins can help us to regularly build the project from the remote repository this helps us to keep track of failed builds.

In this tutorial, you will learn

Before we start selenium and git hub integration, we need to install the following components.

Jenkins Installation.

Maven Installation.

Tomcat Installation.

You can find this installation steps in the following links:

Git Binaries Installation

Now let us start by installing “Git Binaries”.

Step 2) Download the latest stable release.

Step 4) Go to the download location or icon and run the installer.

Another window will pop up,

Step 8) In this step,

Select the Directory where you want to install “Git Binaries” and

Step 11) In this step,

Select Use Git from the Windows Command Prompt to run Git from the command line and

Step 12) In this step,

Select Use Open SSH It will help us to execute the command from the command line, and it will set the environmental path.

Step 13) In this step,

Select “Checkout windows-style, commit Unix-style line ending”.(how the git hub should treat line endings in text files).

Step 14) In this step,

Select Use MinTTY is the default terminal of MSys2 for Git Bash

Once git is installed successfully, you can access the git.

Open Command prompt and type “git” and hit “Enter” If you see below screen means it is installed successfully

Jenkins Git Plugin Install

Now let’s start with Jenkins Git Plugin Installation.

Step 1) Launch the Browser and navigate to your Jenkins.

Step 5) In this step,

Select GitHub plugin then

Now it will install the following plugins.

Once the Installation is finished. Restart your Tomcat server by calling the “shutdown.bat” file

After Restarting the tomcat and Jenkins we can see plugins are installed in the “Installed” TAB.

Setting Up our Eclipse with GitHub Plugin

Now let’s install GitHub Plugin for Eclipse.

Step 1) Launch Eclipse and then

Step 3) In this step,

Type the name “EGIT” and

Then restart the eclipse.

Building a repository on Git

Step 3) In this step,

Enter the name of the repository and

Testing Example Of Using Selenium with Git Hub.

Step 1) Once we are done with the new repository, Launch Eclipse

Step 2) In this step,

Select Maven Project and browse the location.

Step 3) In this step,

Select project name and location then

Step 5) In this step,

Enter Group Id and

Artifact Id and

Step 6)

Now let’s create a sample script

Let’s push the code/local repository to Git Hub.

Step 7) In this step,

Open eclipse and then navigate to the project

Select share project

In this step,

Select the local repository and

Now it’s time to push our code to Git Hub Repository

Step 9) In this step,

Step 10) In this step,

Enter a commit message and

Select the files which we want to send to Git Hub repository

Once you are done with it, you could see the icons in the project is being changed it says that we have successfully pushed and committed our code to Git Hub

We can verify in the Git hub in the repository that our project is successfully pushed into repository

Now it’s time for executing our project from Git Hub in Jenkins

Step 11) Launch browser and open your Jenkins.

Step 13) In this step,

Enter Item name

Select Maven Project

Step 14) In this step, we will configure Git Hub in Jenkins

Enter the Repository URI

If you have multiple repositories in Git Hub, you need to add name Refspec field of the repository.

We can get the URI in Git Hub

Step 15) In this step,

Add the chúng tôi file location in the textbox and

Specify the goals and options for Maven then

Select option on how to run the test

Finally, we can verify that our build is successfully completed/executed.

You're reading Github Integration With Selenium: Complete Tutorial

Integrate Slack With Microsoft Teams, Slack Microsoft Teams Integration With Ai

Table of Content Slack and Microsoft Teams Integration

Integrating Slack with Microsoft Teams can enhance your productivity and streamline your workflow. By connecting these two apps using Appy Pie Connect, powered by AI, you can automate repetitive tasks, reduce manual effort, and achieve better collaboration between teams.

Moreover, Appy Pie Connect offers a range of pre-built integrations and automation workflows for Slack and Microsoft Teams, which can be customized to meet your specific requirements. This means that you can set up workflows to trigger actions in one app based on events in the other app, or create automated processes that run in the background without any manual intervention.

By leveraging the power of AI in Appy Pie Connect, you can optimize your workflow, reduce errors, and increase efficiency even further. So why wait? Sign up for Appy Pie Connect today and start exploring the possibilities of app integration.

Benefits of Integrating Slack with Microsoft Teams Using Appy Pie Connect

Integrating different apps can help businesses streamline their workflow and improve productivity. Using Appy Pie Connect, you can easily integrate Slack with Microsoft Teams and experience a range of benefits.

Benefits Description Example

Increase productivity Integrating Slack with Microsoft Teams through Appy Pie Connect powered by AI allows you to streamline your workflow and automate repetitive tasks, ultimately saving you time and increasing productivity. Automatically create tasks in Microsoft Teams based on new emails received in Slack

Better collaboration By integrating Slack with Microsoft Teams using Appy Pie Connect powered by AI, you can improve collaboration between team members by making it easier to share information and stay on top of tasks. Automatically post updates in Microsoft Teams when new emails are received in Slack

Cost-effective Appy Pie Connect powered by AI offers an affordable way to integrate Slack with Microsoft Teams, as you don’t need to hire a developer or purchase expensive software. Suitable for small businesses or startups with limited budgets

Customizable With Appy Pie Connect, you can customize your integrations to suit your specific needs. Choose which events trigger actions in each app, set up filters to exclude certain data, and more.

Easy to set up Integrating Slack with Microsoft Teams using Appy Pie Connect powered by AI is a simple and straightforward process, even for those with little to no coding experience. Step-by-step instructions for creating and configuring your integrations, and offers a user-friendly interface for managing them.

Streamlined workflow By integrating Slack with Microsoft Teams, you can streamline your workflow and reduce the time and effort required to complete tasks.

Improved communication Integrating Slack with Microsoft Teams can improve communication and collaboration between different teams and departments within your organization. Set up automated notifications in Microsoft Teams whenever a new task is created in Slack

Enhanced data visibility Integrating Slack with Microsoft Teams can provide enhanced data visibility, allowing you to gain insights into your business operations and make better-informed decisions. Track the progress of a project in Slack and view it in real-time in Microsoft Teams

Increased efficiency By automating repetitive tasks, integrating Slack with Microsoft Teams can increase efficiency and productivity within your organization. This can help you to save time and money while also reducing errors and improving overall accuracy.

This can help you to deliver better products and services to your customers, increasing customer satisfaction and loyalty.

How to Integrate Slack with Microsoft Teams using Appy Pie Connect

Here’s a Step-By-Step Guide to Integrating Slack withMicrosoft Teams Using Appy Pie Connect:

Steps Description

1. Sign up for Appy Pie Connect: First, sign up for Appy Pie Connect and create an account.

2. Choose Slack and Microsoft Teams as your apps: Once you’ve logged in, choose Slack and Microsoft Teams as your apps from the list of available apps.

3. Choose a trigger and an action: Next, choose a trigger and an action for your integration. For example, you can choose “New Email” as the trigger for Slack and “Post a Message” as the action for Microsoft Teams.

4. Connect your accounts: After selecting the trigger and action, connect your Slack and Microsoft Teams accounts to Appy Pie Connect. Follow the on-screen instructions to enter your login credentials and authorize the connection.

5. Map the fields: Once your accounts are connected, you will need to map the fields for your trigger and action. For example, you can map the subject and body of the email to the message content in Microsoft Teams.

6. Test your integration:

7. Turn on your integration: Finally, turn on your integration to start automating your workflow. Your integration will run in the background and automatically post new emails to Microsoft Teams as they come in.

Advanced features of Slack and Microsoft Teams integration on Appy Pie Connect

Appy Pie Connect offers a powerful integration platform that enables you to connect different apps and automate your workflow. One of the most popular integrations on the platform is between Slack and Microsoft Teams. By integrating these two apps, you can streamline your workflow and automate repetitive tasks.

Appy Pie Connect Advanced Features Description

Multi-step workflows With Appy Pie Connect powered by AI, you can set up multi-step workflows that involve multiple apps and actions. For example, you can create a workflow that automatically sends a message in Slack when a new task is created in Microsoft Teams, and then creates a follow-up task in Slack when the message is read.

Custom triggers and actions Appy Pie Connect allows you to create custom triggers and actions for your integrations. This means that you can set up workflows that are specific to your business needs. For example, you can create a custom trigger that sends a notification to your team in Microsoft Teams when a specific event occurs in Slack.

Conditional workflows Appy Pie Connect powered by AI also allows you to set up conditional workflows based on certain criteria. For example, you can create a workflow that only sends a message in Slack if a certain condition is met in Microsoft Teams.

Syncing specific fields If you only want to sync specific fields between Slack and Microsoft Teams, you can set up custom field mapping in Appy Pie Connect. This ensures that only the necessary data is synced between the two apps.

Real-time syncing Appy Pie Connect powered by AI offers real-time syncing between Slack and Microsoft Teams. This means that any changes made in one app are immediately reflected in the other app.

Best Practices for Slack and Microsoft Teams Integration on Appy Pie Connect

Integrating Slack with Microsoft Teams using Appy Pie Connect can significantly improve your productivity and streamline your workflow. However, to ensure a seamless integration, it is important to follow these best practices:

Best Practices How to Implement Tips and Tricks

Clearly define your integration goals Identify your specific needs and goals before setting up the integration Determine what kind of data you want to sync between the two apps and which actions you want to automate. This will help you choose the right triggers and actions for your integration.

Use appropriate triggers and actions Appy Pie Connect offers a wide range of triggers and actions for each app. Choose the ones that are most relevant to your integration goals. If you want to post a message in Microsoft Teams every time a new email arrives in Slack, use the “New Email” trigger in Slack and the “Post a Message” action in Microsoft Teams.

Map the fields accurately When setting up your integration, make sure to map the fields accurately. Ensure that the data from one app is mapped to the correct field in the other app.

Test your integration Test your integration thoroughly before turning it on to ensure it works as intended. Send test data and verify that it is being synced between the two apps correctly.

Monitor your integration Monitor your integration regularly to ensure it continues to work smoothly. Keep an eye on any error notifications or issues that may arise, and take corrective action promptly.

Stay organized Keep your integrations organized to ensure they’re functioning properly. Use descriptive names and labels for your integrations to easily identify them and troubleshoot any issues that may arise.

Test thoroughly Test your integration thoroughly before putting it into production. This will help you avoid any errors or issues that could potentially impact your workflow.

Monitor performance Regularly monitor the performance of your integration. Keep an eye on any error logs or metrics provided by Appy Pie Connect to ensure your integration is running smoothly.

Keep your apps up to date Keep your apps up to date to ensure they’re compatible with Appy Pie Connect. This will ensure that any changes or updates made to the integration platform are compatible with your apps.

Seek support when needed Don’t hesitate to seek support if you run into issues or have questions about setting up your integration. The Appy Pie Connect team or the support teams for your respective apps can assist you in troubleshooting any issues and ensuring your integration is set up correctly.

Troubleshooting common issues with the Slack and Microsoft Teams integration

If you’re experiencing issues with the integration between Slack and Microsoft Teams on Appy Pie Connect, here are some common problems and troubleshooting steps you can take to resolve them:

Problem Solution Tips

The integration isn’t working as expected. Double-check that you’ve set up the integration correctly and that all the necessary permissions have been granted. You may also want to try disconnecting and reconnecting the apps to Appy Pie Connect. Test the integration thoroughly before turning it on. Keep the apps up to date to ensure they’re compatible with Appy Pie Connect and any changes made to the integration platform. Seek support from Appy Pie Connect or the support teams for the apps if you run into any issues or have questions about setting up the integration.

The data isn’t syncing between the apps. Make sure that the correct triggers and actions have been selected in Appy Pie Connect. You may also want to check if there are any restrictions or limits on the amount of data that can be synced between the apps. Map the fields accurately to ensure that the data from one app is mapped to the correct field in the other app. Monitor the performance of the integration regularly to ensure that it continues to work smoothly.

There are duplicate entries or missing data. This can happen if there are conflicting settings in the integration or if the data is being synced incorrectly. Try to review and adjust the mapping of fields and data to ensure that everything is correctly synced between the two apps. Stay organized by using descriptive names and labels for your integrations to easily identify them and troubleshoot any issues that may arise. Use appropriate triggers and actions that are most relevant to your integration goals.

The integration is causing errors or crashes. Check for any updates or changes in the apps or the integration platform that may be causing the errors. You may also want to reach out to the support team of the apps or Appy Pie Connect for assistance. Use the appropriate triggers and actions for your integration goals. Monitor the performance of the integration regularly to ensure that it continues to work smoothly.

The integration has stopped working altogether. This could be due to changes in the apps or the integration platform, such as updates or changes in the API. You may need to reconfigure the integration or reach out to the support team for assistance. Clearly define your integration goals before setting up the integration. Test the integration thoroughly before turning it on. Use appropriate triggers and actions that are most relevant to your integration goals. Monitor the integration regularly to ensure that it continues to work smoothly.

By following these troubleshooting steps, you can identify and resolve common issues with the Slack and Microsoft Teams integration on Appy Pie Connect powered by AI . If you’re still experiencing problems, don’t hesitate to reach out to the support team for further assistance.

Here’s a Comparison of Appy Pie Connect to IFTTT, Workato, and Tray.io:

Integration Platform Number of App Integrations Support for Multi-Step Integrations User-Friendly Interface Pricing Plans Free Trial Available

Appy Pie Connect 1,000+ Yes, with conditional logic and custom fields Yes, drag-and-drop interface Affordable plans Yes

IFTTT 600+ No, only supports simple one-step integrations Yes, mobile app interface N/A

Workato 1,000+ Yes, with conditional logic and custom fields Yes, drag-and-drop interface Flexible plans based on usage and features Yes

Tray.io 600+ Yes, with conditional logic and custom fields Yes, drag-and-drop interface Flexible plans based on usage and features Yes

Reviews and Ratings from Appy Pie Connect Users

At Appy Pie Connect, we value feedback from our users. Here are some reviews and ratings from our users who have used Slack and Microsoft Teams integration:

“Setting up the Slack and Microsoft Teams integration on Connect was incredibly easy. We were up and running in just a few minutes, and the integration has been working flawlessly ever since.” – James Smith, 4 stars

“We’ve been using Appy Pie Connect for a few months now, and it’s been a game-changer for our business. The Slack and Microsoft Teams integration has saved us countless hours of manual work and allowed us to focus on more important tasks.” – Joseph Levi, 5 stars

These are just a few examples of the positive feedback we’ve received from our users. We’re constantly working to improve our integrations and provide the best possible experience for our users. If you have any feedback or suggestions, please don’t hesitate to reach out to our support team.

Frequently Asked Questions

Here are some frequently asked questions about Slack and Microsoft Teams Integration with Appy Pie Connect:

Question Answer

Can I integrate more than two apps using Appy Pie Connect? Yes, you can integrate more than two apps using Appy Pie Connect. Our platform supports multiple integrations that you can create based on your needs.

How long does it take to set up an integration between Slack and Microsoft Teams? The time it takes to set up an integration between Slack and Microsoft Teams depends on the complexity of the integration. With Appy Pie Connect’s user-friendly interface, most integrations can be set up in a matter of minutes.

How often does Appy Pie Connect sync data between Slack and Microsoft Teams? Appy Pie Connect can sync data between Slack and Microsoft Teams in real-time or at set intervals. You can choose the frequency of data syncing based on your needs.

What happens if I disconnect one of the apps from Appy Pie Connect? If you disconnect one of the apps from Appy Pie Connect, the integration will no longer work, and data will not be synced between the two apps. However, you can easily reconnect the app and resume the integration.

Can I customize the fields that are synced between Slack and Microsoft Teams? Yes, you can customize the fields that are synced between Slack and Microsoft Teams based on your specific needs. You can choose which fields to sync and map them to corresponding fields in the other app.

Is there a limit to the number of integrations I can set up using Appy Pie Connect? No, there is no limit to the number of integrations you can set up using Appy Pie Connect. You can set up as many integrations as you need, depending on the number of apps you use.

What if I need help setting up my integration? If you need help setting up your integration, you can contact Appy Pie Connect’s support team. They are available 24/7 to assist you with any issues you may have.

Conclusion

Top 10 Github Data Science Projects With Source Code

The importance of “data” in today’s world is something we do not need to emphasize. As of 2023, the data generated has touched over 120 zettabytes! This is far more than what we can imagine. What’s more surprising is that the number will cross 180 within the next two years. This is why data science is rapidly growing, requiring skilled professionals who love wrangling and working with data. If you are considering foraying into a data-based profession, one of the best ways is to work on GitHub data science projects and build a data scientist portfolio, showcasing your skills and experience.

So, if you are passionate about data science and eager to explore new datasets and techniques, read on and explore the top 10 data science projects you can contribute to.

List of Top 10 Data Science Projects on Github For Beginners

Here is a list of data science projects available for beginners with step by step procedures.

Exploring the Enron Email Dataset

Predicting Housing Prices with Machine Learning

Identifying Fraudulent Credit Card Transactions

Image Classification with Convolutional Neural Networks

Sentiment Analysis on Twitter Data

Analyzing Netflix Movies and TV Shows

Customer Segmentation with K-Means Clustering

Medical Diagnosis with Deep Learning

Music Genre Classification with Machine Learning

Predicting Credit Risk with Logistic Regression

1. Exploring the Enron Email Dataset

The first on our list of data science capstone project on GitHub is about exploring the Enron Email Dataset. This will give you an initial idea of standard data science tasks. Link to the dataset: Enron Email Dataset.

Problem Statement

The project aims to explore the email dataset (of internal communications) from the Enron Corporation, globally known for a huge corporate fraud that led to the bankruptcy of the company. The exploration would be to find patterns and classify emails in an attempt to detect fraudulent emails.

Brief Overview of the Project and the Enron Email Dataset

Let’s start by knowing the data. The dataset belongs to the Enron Corpus, a massive database of more than 6,00,000 emails belonging to the employees of Enron Corp. The dataset presents an opportunity for data scientists to dive deeper into one of the biggest corporate frauds, the Enron Fraud by studying patterns in the company data.

In this project, you will download the Enron dataset and create a copy of the original repository containing the existing project under your account. You can also create an entirely new project.  

Step-by-Step Guide to the Project

The project involves you working on the following:

Clone the original repository and familiarize yourself with the Enron dataset: This step would include reviewing the dataset or any documentation provided, understanding the data types, and keeping track of the elements.

After the introductory analysis, you will move on to data preprocessing. Given that it is an extensive dataset, there will be a lot of noise (unnecessary elements), necessitating data cleaning. You may also need to work around the missing values in the dataset.

 After preprocessing, you should perform EDA (exploratory data analysis). This may involve creating visualizations to understand the distribution of data better.

You can also undertake statistical analyses to identify correlations between data elements or anomalies.

Some relevant GitHub repositories that will help you to study the Enron Email Dataset are listed below:

Code Snippet:

2. Predicting Housing Prices with Machine Learning

Predicting housing prices is one of the most popular data analyst projects on GitHub. 

Problem Statement

The goal of this project is to predict the prices of houses based on several factors and study the relationship between them. On completion, you will be able to interpret how each of these factors affects housing prices.

Brief Overview of the Project and the Housing Price Dataset

Here, you will use a dataset with over 13 features, including ID (to count the records), zones, area (size of the lot in square feet), build type (type of dwelling), year of construction, year of remodeling (if valid), sale price (to be predicted), and a few more. Link to the dataset: Housing Price Prediction.

Step-by-Step Guide to the Project

You will work on the following processes while doing the machine learning project.

Like any other GitHub project, you will start by exploring the dataset for data types, relationships, and anomalies.

The next step will be to preprocess the data, reduce noise, and fill in the missing values (or remove the respective entries) based on your requirement. 

As predicting housing prices involves several features, feature engineering is essential. This could include techniques such as creating new variables through combinations of existing variables and selecting appropriate variables.

The next step is to select the most appropriate ML model by exploring different ML models like linear regression, decision trees, neural networks, and others.

Lastly, you will evaluate the chosen model based on metrics like root mean squared error, R-squared values, etc., to see how your model performs.

Some relevant GitHub repositories that will help you predict housing prices are listed below:

Code Snippet:

import pandas as pd import numpy as np from sklearn.model_selection import train_test_split from sklearn.linear_model import LinearRegression from sklearn.metrics import mean_squared_error, r2_score housing_df = pd.read_csv('housing_data.csv') housing_df = housing_df.drop(['MSZoning', 'LotConfig', 'BldgType', 'Exterior1st'], axis=1) housing_df = housing_df.dropna(subset=['BsmtFinSF2', 'TotalBsmtSF', 'SalePrice']) X = housing_df.drop('SalePrice', axis=1) y = housing_df['SalePrice'] X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) lr = LinearRegression() lr.fit(X_train, y_train) 3. Identifying Fraudulent Credit Card Transactions

Fraud detection in credit card transactions is an excellent area of practising GitHub data science projects. It will make you proficient in identifying data patterns and anomalies.

Problem Statement

This GitHub data science project is to detect patterns in data containing information about credit card transactions. The outcome should give you certain features/patterns that all fraudulent transactions share.

Brief Overview of the Project and the Dataset

In this GitHub project, you can work with any credit card transaction dataset, like the European cardholders’ data containing transactions made in September 2013. This dataset contains over 492 fraud transactions out of 284,807 total transactions. The features are denoted by V1, V2,…, etc. Link to the dataset: Credit Card Fraud Detection.

Step-by-step Guide to the Project

You will start with data exploration to understand the structure and check for missing values in the dataset working with the Pandas library.

Once you familiarize yourself with the dataset, preprocess the data, handle the missing values, remove unnecessary variables, and create new features via feature engineering.

The next step is to train a machine-learning model. Consider different algorithms like SVM, random forests, regression, etc., and fine-tune them to achieve the best results.

Evaluate its performance on various metrics like recall, precision, F1-score, etc. 

Some relevant GitHub repositories that will help you detect fraudulent credit card transactions are listed below.

Code Snippet:

import pandas as pd from sklearn.model_selection import train_test_split from sklearn.ensemble import RandomForestClassifier from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score creditcard_df = pd.read_csv('creditcard_data.csv') X = creditcard_df.drop('Class', axis = 1) y = creditcard_df['Class'] X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state = 42) rf = RandomForestClassifier(n_estimators=100, random_state = 42) rf.fit(X_train, y_train) 4. Image Classification with Convolutional Neural Networks

Another one on our list of GitHub data science projects focuses on image classification using CNNs (convolutional neural networks). CNNs are a subtype of neural networks with built-in convolutional layers to reduce the high-dimensionality of images without compromising on the information/quality.

Problem Statement

The aim of this project is to classify images based on certain features using convolutional neural networks. On completion, you will develop a deep understanding of how CNNs proficiently work with image datasets for classification.

Brief Overview of the Project and the Dataset

In this project, you can use a dataset of Bing images by crawling image data from URLs based on specific keywords. You will need to use Python and Bing’s multithreading features for the same using the pip install bing-images command on your prompt window and import “bing” to fetch image URLs.

Step-by-step Guide to Image Classification 

You will start by filter-searching for the kind of images you wish to classify. It could be anything, for example, a cat or a dog. Download the images in bulk via the multithreading feature.

The next is data organizing and preprocessing. Preprocess the images by resizing them to a uniform size and converting them to grayscale if required. 

Split the dataset into a testing and training set. The training set trains the CNN model, while the validation set monitors the training process.

Define the architecture of the CNN model. You can also add functionality, like batch normalization, to the model. This prevents over-fitting.

Train the CNN model on the training set using a suitable optimizer like Adam or SGD and evaluate its performance.

Some relevant GitHub repositories that will help you classify images using CNN are listed below.

Code Snippet:

import numpy as np import matplotlib.pyplot as plt from keras.datasets import cifar10 from keras.models import Sequential from keras.layers import Conv2D, MaxPooling2D, Dense, Flatten, Dropout from keras.utils import np_utils # Load the dataset (X_train, y_train), (X_test, y_test) = ‘dataset’.load_data() # One-hot encode target variables y_train = np_utils.to_categorical(y_train) y_test = np_utils.to_categorical(y_test) # Define the model architecture model = Sequential() model.add(Conv2D(32, (3, 3), activation='relu', padding='same', input_shape=X_train.shape[1:])) model.add(Conv2D(32, (3, 3), activation='relu')) model.add(MaxPooling2D(pool_size=(2, 2))) model.add(Dropout(0.25)) model.add(Conv2D(64, (3, 3), activation='relu', padding='same')) model.add(Conv2D(64, (3, 3), activation='relu')) model.add(MaxPooling2D(pool_size=(2, 2))) model.add(Dropout(0.25)) model.add(Flatten()) model.add(Dense(512, activation='relu')) model.add(Dropout(0.5)) model.add(Dense(10, activation='softmax')) # Compile the model # Train the model history = model.fit(X_train, y_train, batch_size=128, epochs=20, validation_data=(X_test, y_test)) # Evaluate the model on the test set scores = model.evaluate(X_test, y_test, verbose=0) print("Test Accuracy:", scores[1]) 5. Sentiment Analysis on Twitter Data

Twitter is a famous ground for all kinds of data, making its data a good source for practicing machine learning and data science tasks.

Problem Statement Brief Overview of the Project and the Dataset

In this GitHub data science project, you will gather Twitter data using the Streaming Twitter API, Python, MySQL, and Tweepy. Then you will perform sentiment analysis to identify specific emotions and opinions. By monitoring these sentiments, you could help individuals or organizations to make better decisions on customer engagement and experiences, even as a beginner.

You can use the Sentiment 140 dataset containing over 1.6 million tweets. The tweets Link to the dataset: Sentiment140 dataset.

Step-by-step Guide to the Project

The first step is to use Twitter’s API to collect data based on specific keywords, users, or tweets. Once you have the data, remove unnecessary noise and other irrelevant elements like special characters. 

You can also remove certain stop words (words that do not add much value), “the,” “and,” etc. Additionally, you can perform lemmatization. Lemmatization refers to converting different forms of the word into a single form; for example, “eat,” “eating,” and “eats” becomes “eat” (the lemma).

The next important step in NLP-based analysis is tokenization. Simply put, you will break down the data into smaller units of tokens or individual words. This makes it easier to assign meaning to smaller chunks that constitute the entire text.

Once the data has been tokenized, the next step is to classify the sentiment of each token using a machine-learning model. You can use Random Forest Classifiers, Naive Bayes, or RNNs, for the same.

Some relevant GitHub repositories that will help you analyze sentiments from Twitter data are listed below.

Code Snippet:

import nltk nltk.download('stopwords') nltk.download('punkt') nltk.download('wordnet') import string import re import pandas as pd from nltk.corpus import stopwords from nltk.tokenize import word_tokenize from chúng tôi import WordNetLemmatizer from sklearn.feature_extraction.text import CountVectorizer, TfidfTransformer from sklearn.model_selection import train_test_split from sklearn.naive_bayes import MultinomialNB from sklearn.metrics import classification_report # Load the dataset data = pd.read_csv('tweets.csv', encoding='latin-1', header=None) # Assign new column names to the DataFrame column_names = ['target', 'id', 'date', 'flag', 'user', 'text'] data.columns = column_names # Preprocess the text data stop_words = set(stopwords.words('english')) lemmatizer = WordNetLemmatizer() def preprocess_text(text): # Remove URLs, usernames, and hashtags text = re.sub(r'@w+', '', text) text = re.sub(r'#w+', '', text) # Remove punctuation and convert to lowercase text = text.translate(str.maketrans('', '', string.punctuation)) text = text.lower() # Tokenize the text and remove stop words tokens = word_tokenize(text) filtered_tokens = [token for token in tokens if token not in stop_words] # Lemmatize the tokens lemmatized_tokens = [lemmatizer.lemmatize(token) for token in filtered_tokens] # Join the tokens back into text preprocessed_text = ' '.join(lemmatized_tokens) return preprocessed_text data['text'] = data['text'].apply(preprocess_text) # Split the data into training and testing sets X_train, X_test, y_train, y_test = train_test_split(data['text'], data['target'], test_size=0.2, random_state=42) # Vectorize the text data count_vect = CountVectorizer() X_train_counts = count_vect.fit_transform(X_train) tfidf_transformer = TfidfTransformer() X_train_tfidf = tfidf_transformer.fit_transform(X_train_counts) # Train the model clf = MultinomialNB().fit(X_train_tfidf, y_train) # Test the model X_test_counts = count_vect.transform(X_test) X_test_tfidf = tfidf_transformer.transform(X_test_counts) y_pred = clf.predict(X_test_tfidf) # Print the classification report print(classification_report(y_test, y_pred))

Output:

6. Analyzing Netflix Movies and TV Shows

Netflix is probably everyone’s favorite movie streaming service. This GitHub data science project is based on analyzing Netflix movies and TV shows.

Problem Statement

The aim of this project is to run data analysis workflows, including EDA, visualization, and interpretation, on Netflix user data.

Brief Overview of the Project and the Dataset

This data science project aims to hone your skills and visually create and interpret Netflix data using libraries like Matplotlib, Seaborn, and worldcloud and tools like Tableau. For the same, you can use the Netflix Original Films and IMDb scores dataset available on Kaggle. It contains all Netflix Originals released as of June 1, 2023, with their corresponding IMDb ratings. Link to the dataset: Netflix Originals.

Step-by-step Guide to Analyzing Netflix Movies

After downloading the dataset, preprocess the dataset by removing unnecessary noise and stopwords like “the,” “an,” and “and.”

Then comes tokenization of the cleaned data. This step involves breaking bigger sentences or paragraphs into smaller units or individual words. 

You can also use stemming/lemmatization to convert different forms of words into a single item. For instance, “sleep” and “sleeping” becomes “sleep.”

Once the data is preprocessed and lemmatized, you can extract features from text using count vectorizer, tfidf, etc and then use a machine learning algorithm to classify the sentiments. You can use Random Forests, SVMs, or RNNs for the same.

Create visualizations and study the patterns and trends, such as the number of movies released in a year, the top genres, etc. 

The project can be extended to text analysis. Analyze the titles, directors, and actors of the movies and TV shows. 

You can use the resulting insights to create recommendations.

Some relevant GitHub repositories that will help you analyze Netflix Movies and TV Shows are listed below.

Code Snippet:

import pandas as pd import nltk nltk.download('vader_lexicon') from nltk.sentiment import SentimentIntensityAnalyzer # Load the Netflix dataset netflix_data = pd.read_csv('netflix_titles.csv', encoding='iso-8859-1') # Create a new column for sentiment scores of movie and TV show titles sia = SentimentIntensityAnalyzer() netflix_data['sentiment_scores'] = netflix_data['Title'].apply(lambda x: sia.polarity_scores(x)) # Extract the compound sentiment score from the sentiment scores dictionary netflix_data['sentiment_score'] = netflix_data['sentiment_scores'].apply(lambda x: x['compound']) # Group the data by language and calculate the average sentiment score for movies and TV shows in each language language_sentiment = netflix_data.groupby('Language')['sentiment_score'].mean() # Print the top 10 languages with the highest average sentiment score for movies and TV shows print(language_sentiment.sort_values(ascending=False).head(10))

Output:

7. Customer Segmentation with K-Means Clustering

Customer segmentation is one of the most important applications of data science. This GitHub data science project will require you to work with the K-clustering algorithm. This popular unsupervised machine learning algorithm clusters data points into K clusters based on similarity.

Problem Statement

The goal of this project is to segment customers visiting a mall based on certain factors like their annual income, spending habits, etc., using the K-means clustering algorithm.

Brief Overview of the Project and the Dataset

The project will require you to collect data, undertake preliminary research and data preprocessing, and train and test a K-means clustering model to segment customers. You can use a dataset on Mall Customer Segmentation containing five features (CustomerID, Gender, Age, Annual Income, and Spending Score) and corresponding information about 200 customers. Link to the dataset: Mall Customer Segmentation.

Step-by-step Guide to the Project

Follow the steps below:

Load the dataset, import all necessary packages, and explore the data.

After familiarizing with the data, clean the dataset by removing duplicates or irrelevant data, handling missing values, and formatting the data for analysis.

Select all relevant features. This could include annual income, spending score, gender, etc.

Train a K-Means clustering model on the preprocessed data to identify customer segments based on these features. You can then visualize the customer segments using Seaborn and make scatter plots, heatmaps, etc.

Lastly, analyze the customer segments to gain insights into customer behavior.

Some relevant GitHub repositories that will help you segment customers are listed below.

Code Snippet:

import pandas as pd import matplotlib.pyplot as plt from sklearn.cluster import KMeans from sklearn.preprocessing import StandardScaler # Load the customer data customer_data = pd.read_csv('customer_data.csv') customer_data = customer_data.drop('Gender', axis=1) # Standardize the data scaler = StandardScaler() scaled_data = scaler.fit_transform(customer_data) # Find the optimal number of clusters using the elbow method wcss = [] for i in range(1, 11): kmeans = KMeans(n_clusters=i, init='k-means++', random_state=42) kmeans.fit(scaled_data) wcss.append(kmeans.inertia_) plt.plot(range(1, 11), wcss) plt.title('Elbow Method') plt.xlabel('Number of Clusters') plt.ylabel('WCSS') plt.show() # Perform K-Means clustering with the optimal number of clusters kmeans = KMeans(n_clusters=4, init='k-means++', random_state=42) kmeans.fit(scaled_data) # Add the cluster labels to the original DataFrame customer_data['Cluster'] = kmeans.labels_ # Plot the clusters based on age and income plt.scatter(customer_data['Age'], customer_data['Annual Income (k$)'], c=customer_data['Cluster']) plt.title('Customer Segmentation') plt.xlabel('Age') plt.ylabel('Income') plt.show() 8. Medical Diagnosis with Deep Learning

Deep learning is a relatively nascent branch of machine learning consisting of multiple layers of neural networks. It is widely used for complex applications because of its high computational capability. Consequently, working on a Github data science project, including deep learning, will be very good for your data analyst portfolio on Github.

Problem Statement

This GitHub data science project aims to identify different pathologies in chest X-rays using deep-learning convolutional models. Upon completion, you should get an idea of how deep learning/machine learning is used in radiology.

Brief Overview of the Project and the Dataset

In this data science capstone project, you will work with the GradCAM model interpretation method and use chest X-rays to diagnose over 14 kinds of pathologies, like Pneumothorax, Edema, Cardiomegaly, etc. The goal is to utilize deep learning-based DenseNet-121 models for classification. 

You will work using a public dataset of chest X-rays with over 108,948 frontal view X-rays of more than 32,717 patients. A subset of ~1000 images would be enough for the project. Link to the dataset: Chest X-rays.

Step-by-step Guide to the Project

Download the dataset. Once you have it, you must preprocess it by resizing the images, normalizing pixels, etc. This is done to ensure that your data is ready for training.

The next step is to train the deep learning model, DenseNet121 using PyTorch or TensorFlow. 

Using the model, you could predict the pathology and other underlying issues (if any). 

You can evaluate your model on F1 score, precision, and accuracy metrics. If trained correctly, the model can result in accuracies as high as 0.9 (ideal is the closest to 1).

Some relevant GitHub repositories that will help you with medical diagnoses using deep learning are listed below.

Code Snippet:

import tensorflow as tf from tensorflow.keras.preprocessing.image import ImageDataGenerator from tensorflow.keras.models import Sequential from tensorflow.keras.layers import Conv2D, MaxPooling2D, Flatten, Dense # Set up data generators for training and validation sets train_datagen = ImageDataGenerator(rescale=1./255, shear_range=0.2, zoom_range=0.2, horizontal_flip=True) train_generator = train_datagen.flow_from_directory('train_dir', target_size=(128, 128), batch_size=32, class_mode='binary') val_datagen = ImageDataGenerator(rescale=1./255) val_generator = val_datagen.flow_from_directory('val_dir', target_size=(128, 128), batch_size=32, class_mode='binary') # Build a convolutional neural network for medical diagnosis model = Sequential() model.add(Conv2D(32, (3, 3), activation='relu', input_shape=(128, 128, 3))) model.add(MaxPooling2D((2, 2))) model.add(Conv2D(64, (3, 3), activation='relu')) model.add(MaxPooling2D((2, 2))) model.add(Conv2D(128, (3, 3), activation='relu')) model.add(MaxPooling2D((2, 2))) model.add(Flatten()) model.add(Dense(128, activation='relu')) model.add(Dense(1, activation='sigmoid')) # Compile the model # Train the model on the training set and evaluate it on the validation set history = model.fit(train_generator, epochs=10, validation_data=val_generator) # Plot the training and validation accuracy and loss curves plt.plot(history.history['accuracy'], label='Training Accuracy') plt.plot(history.history['val_accuracy'], label='Validation Accuracy') plt.title('Training and Validation Accuracy') plt.xlabel('Epoch') plt.ylabel('Accuracy') plt.legend() plt.show() plt.plot(history.history['loss'], label='Training Loss') plt.plot(history.history['val_loss'], label='Validation Loss') plt.title('Training and Validation Loss') plt.xlabel('Epoch') plt.ylabel('Loss') plt.legend() plt.show() 9. Music Genre Classification with Machine Learning

This is among the most interesting GitHub data science projects. While it is a great project, it is equally challenging as getting a proper dataset would be a very time-consuming part of this project, given it’s all music!

Problem Statement

This unique GitHub project is aimed to help you learn how to work with non-standard data types like musical data. Further, you will also learn how to classify such data based on different features.

Brief Overview of the Project and Dataset

In this project, you will collect music data and use it to train and test ML models. Since music data is highly subject to copyrights, we make it easier using MSD (Million Song Dataset). This freely available dataset contains audio features and metadata for almost a million songs. These songs belong to various categories like Classical, Disco, HipHop, Reggae, etc. However, you need a music provider platform to stream the “sounds.” 

Link to the dataset: MSD.

Step-by-step Guide to the Project

The first step is to collect the music data. 

The next step is to preprocess data. Music data is typically preprocessed by converting audio files into feature vectors that can be used as input.

After processing the data, it is essential to explore features like frequency, pitch, etc. You can study the data using the Mel Frequency Cepstral Coefficient method, rhythm features, etc. You can classify the songs later using these features.

Select an appropriate ML model. It could be multiclass SVM, or CNN, depending on the size of your dataset and desired accuracy. 

Some relevant GitHub repositories that will help you segment customers are listed below.

Code Snippet: 

import os import librosa import numpy as np import pandas as pd from sklearn.model_selection import train_test_split from sklearn.preprocessing import LabelEncoder, StandardScaler from keras import models, layers # Set up paths to audio files and genre labels AUDIO_PATH = 'audio' CSV_PATH = 'data.csv' # Load audio files and extract features using librosa def extract_features(file_path): audio_data, _ = librosa.load(file_path, sr=22050, mono=True, duration=30) mfccs = librosa.feature.mfcc(y=audio_data, sr=22050, n_mfcc=20) chroma_stft = librosa.feature.chroma_stft(y=audio_data, sr=22050) spectral_centroid = librosa.feature.spectral_centroid(y=audio_data, sr=22050) spectral_bandwidth = librosa.feature.spectral_bandwidth(y=audio_data, sr=22050) spectral_rolloff = librosa.feature.spectral_rolloff(y=audio_data, sr=22050) features = np.concatenate((np.mean(mfccs, axis=1), np.mean(chroma_stft, axis=1), np.mean(spectral_centroid), np.mean(spectral_bandwidth), np.mean(spectral_rolloff))) return features # Load data from CSV file and extract features data = pd.read_csv(CSV_PATH) features = [] labels = [] for index, row in data.iterrows(): file_path = os.path.join(AUDIO_PATH, row['filename']) genre = row['label'] features.append(extract_features(file_path)) labels.append(genre) # Encode genre labels and scale features encoder = LabelEncoder() labels = encoder.fit_transform(labels) scaler = StandardScaler() features = scaler.fit_transform(np.array(features, dtype=float)) # Split data into training and testing sets train_features, test_features, train_labels, test_labels = train_test_split(features, labels, test_size=0.2) # Build a neural network for music genre classification model = models.Sequential() model.add(layers.Dense(256, activation='relu', input_shape=(train_features.shape[1],))) model.add(layers.Dropout(0.3)) model.add(layers.Dense(128, activation='relu')) model.add(layers.Dropout(0.2)) model.add(layers.Dense(64, activation='relu')) model.add(layers.Dropout(0.1)) model.add(layers.Dense(10, activation='softmax')) # Compile the model # Train the model on the training set and evaluate it on the testing set history = model.fit(train_features, train_labels, epochs=50, batch_size=128, validation_data=(test_features, test_labels)) # Plot the training and testing accuracy and loss curves plt.plot(history.history['accuracy'], label='Training Accuracy') plt.plot(history.history['val_accuracy'], label='Testing Accuracy') plt.title('Training and Testing Accuracy') plt.xlabel('Epoch') plt.ylabel('Accuracy') plt.legend() plt.show() plt.plot(history.history['loss'], label='Training Loss') plt.plot(history.history['val_loss'], label='Testing Loss') plt.title('Training and Testing Loss') plt.xlabel('Epoch') plt.ylabel('Loss') plt.legend() plt.show() 10. Predicting Credit Risk with Logistic Regression Problem Statement

This project is another application of machine learning in the financial sector. It aims to predict the credit risks of different customers based on their financial records, income, debt size, and a few other factors.

Brief Overview of the Project and Dataset

In this project, you will be working on a dataset including lending details of customers. It includes many features like loan size, interest rate, borrower income, debt-to-income ratio, etc. All these features, when analyzed together, will help you determine the credit risk of each customer. Link to the dataset: Lending.

Step-by-step Guide to the Project

After sourcing the data, the first step is to process it. The data needs to be cleaned to ensure it is suitable for analysis.

Explore the dataset to gain insights into different features and find anomalies and patterns. This can involve visualizing the data with histograms, scatterplots, or heat maps.

Choose the most relevant features to work with. For instance, target the credit score, income, or payment history while estimating the credit risk.

Spilt the dataset into training and testing and used the training data to fit a logistic regression model using maximum likelihood estimation. This stage approximates the likelihood of customers who fail to repay.

Once your model is ready, you can evaluate it using metrics like, precision, recall, etc. 

Some relevant GitHub repositories that will help you predict credit risk are listed below.

Code Snippet:

import pandas as pd import numpy as np from sklearn.linear_model import LogisticRegression from sklearn.model_selection import train_test_split from sklearn.preprocessing import StandardScaler from sklearn.metrics import accuracy_score, confusion_matrix # Load data from CSV file data = pd.read_csv('credit_data.csv') # Clean data by removing missing values data.dropna(inplace=True) # Split data into features and labels features = data[['loan_size', 'interest_rate', 'borrower_income', 'debt_to_income', 'num_of_accounts', 'derogatory_marks', 'total_debt']] labels = data['loan_status'] # Scale features to have zero mean and unit variance scaler = StandardScaler() features = scaler.fit_transform(features) # Split data into training and testing sets train_features, test_features, train_labels, test_labels = train_test_split(features, labels, test_size=0.2) # Build a logistic regression model for credit risk prediction model = LogisticRegression() # Train the model on the training set model.fit(train_features, train_labels) # Predict labels for the testing set predictions = model.predict(test_features) # Evaluate the model's accuracy and confusion matrix accuracy = accuracy_score(test_labels, predictions) conf_matrix = confusion_matrix(test_labels, predictions) print('Accuracy:', accuracy) print('Confusion Matrix:', conf_matrix)

Output:

Best Practices for Contributing to Data Science Projects on GitHub

If you are an aspiring data scientist, working on GitHub data science projects and being familiar with how the platform works is a necessity. As a data scientist, you must know how to work your way in collecting data, modifying projects, implementing changes, and collaborating with others. This section discusses some of the best practices you should follow while working on GitHub projects.

Communication and Collaboration with Other Contributors

When the scale of the project increases, handling them alone is next to impossible. You must collaborate with others working on a similar project or concept. This also gives you and the other person a chance to leverage a more diverse skill set and perspective, resulting in better code, faster development, and enhanced model performance.

Following Community Guidelines and Project Standards.

GitHub is a globally renowned public repository of code that many people in the data science and machine learning domain use. Following community guidelines and standards is the only way to keep track of all updates and maintain consistency throughout the platform. These standards can ensure that code is high quality, secure, and adheres to industry best practices. 

GitHub

Writing Clean Code and Documenting Changes

Coding is an intuitive process. There could be countless ways to code a single task or application. However, the preferred version would be the most readable and clean because it is easier to understand and maintain over time. This helps to reduce errors and improve the quality of the code. 

Moreover, documenting the changes and contributions to existing code makes the process more credible and transparent for everyone. This helps build an element of public trust on the platform.

Testing and Debugging Changes

Continuous testing and debugging code changes are excellent ways to ensure quality and consistency. It helps identify compatibility issues with different systems, browsers, or platforms, ensuring the project works as expected across different environments. This reduces the long-term cost of code maintenance as issues are fixed early on.

How to Showcase Your Data Science Projects on GitHub?

If you are wondering how to put your GitHub data science project forward, this section is there for your reference. You can start by building a legitimate data analyst or data scientist portfolio on GitHub. Follow the below steps once you have a profile.

Create a new repository with a descriptive name and a brief description.

Add a README file with an overview of your GitHub data science project, dataset, methodology, and any other information you want to provide. This can include your contributions to the project, impact on society, cost, etc.

Add a folder with the source code. Make sure that the code is clean and well-documented.

Include a license if you want to publicize your repository and are open to receiving feedback/suggestions. GitHub provides numerous license options. 

Conclusion 

As someone interested in the field, you must have seen that the world of data science is constantly evolving. Whether exploring new data sets or building more complex models, data science constantly adds value to day-to-day business operations. This environment has necessitated people to explore it as a profession. For all aspiring data scientists and existing professionals, GitHub is the go-to platform for data scientists to showcase their work and learn from others. This is why this blog has explored the top 10 GitHub data science projects for beginners that offer diverse applications and challenges. By exploring these projects, you can dive deeper into data science workflows, including data preparation, exploration, visualization, and modelling. 

To gain more insight into the field, Analytics Vidhya, a highly credible educational platform, offers numerous resources on data science, machine learning, and artificial intelligence. With these resources (blogs, tutorials, certifications, etc.), you can get practical experience working with complex datasets in a real-world context. Moreover, AV offers a comprehensive Blackbelt course that introduces you to the application of AI and ML in several fields, including data science. Head over to the website and see for yourself.

Frequently Asked Questions

Q1. What projects should I do for data science?

A. Projects for data science can vary depending on your interests and goals. Some popular project ideas include analyzing real-world datasets, building predictive models, creating data visualizations, conducting sentiment analysis, or developing recommendation systems. Choose projects that align with your desired skill set and allow you to showcase your expertise in specific areas of data science.

Q2. How do I start my own data science project?

A. To start your own data science project, begin by identifying a problem or question you want to explore. Define clear objectives, gather relevant data, and preprocess it as needed. Select appropriate tools and techniques for analysis, such as statistical modeling, machine learning algorithms, or data visualization libraries. Document your process and findings, and present your results effectively to communicate your insights.

Q3. What is a data science project?

A. A data science project refers to a systematic and structured endeavor that applies data analysis techniques and methodologies to extract meaningful insights from data. It involves defining a problem, collecting and preprocessing data, performing exploratory data analysis, applying statistical or machine learning techniques, and interpreting and communicating the results to inform decision-making.

Q4. What are data science projects for a portfolio?

A. Data science projects for a portfolio are projects that showcase your skills and expertise as a data scientist. These projects should demonstrate your ability to analyze and interpret data, apply relevant techniques and algorithms, and effectively communicate your findings. Examples could include predicting customer churn, sentiment analysis of social media data, or building a recommendation system. The projects should highlight your problem-solving skills and provide tangible evidence of your proficiency in data science.

Related

Dplyr Tutorial: Merge And Join Data In R With Examples

Introduction to Data Analysis

Data analysis can be divided into three parts:

Extraction: First, we need to collect the data from many sources and combine them.

Transform: This step involves the data manipulation. Once we have consolidated all the sources of data, we can begin to clean the data.

Visualize: The last move is to visualize our data to check irregularity.

Data Analysis Process

One of the most significant challenges faced by data scientists is the data manipulation. Data is never available in the desired format. Data scientists need to spend at least half of their time, cleaning and manipulating the data. That is one of the most critical assignments in the job. If the data manipulation process is not complete, precise and rigorous, the model will not perform correctly.

In this tutorial, you will learn:

R Dplyr

R has a library called dplyr to help in data transformation. The dplyr library is fundamentally created around four functions to manipulate the data and five verbs to clean the data. After that, we can use the ggplot library to analyze and visualize the data.

We will learn how to use the dplyr library to manipulate a Data Frame.

Merge Data with R Dplyr

dplyr provides a nice and convenient way to combine datasets. We may have many sources of input data, and at some point, we need to combine them. A join with dplyr adds variables to the right of the original dataset.

Dplyr Joins

Following are four important types of joins used in dplyr to merge two datasets:

Function Objective Arguments Multiple keys

left_join() Merge two datasets. Keep all observations from the origin table data, origin, destination, by = “ID” origin, destination, by = c(“ID”, “ID2”)

right_join() Merge two datasets. Keep all observations from the destination table data, origin, destination, by = “ID” origin, destination, by = c(“ID”, “ID2”)

inner_join() Merge two datasets. Excludes all unmatched rows data, origin, destination, by = “ID” origin, destination, by = c(“ID”, “ID2”)

full_join() Merge two datasets. Keeps all observations data, origin, destination, by = “ID” origin, destination, by = c(“ID”, “ID2”)

We will study all the joins types via an easy example.

First of all, we build two datasets. Table 1 contains two variables, ID, and y, whereas Table 2 gathers ID and z. In each situation, we need to have a key-pair variable. In our case, ID is our key variable. The function will look for identical values in both tables and bind the returning values to the right of table 1.

library(dplyr) df_primary <- tribble( ~ID, ~y, "A", 5, "B", 5, "C", 8, "D", 0, "F", 9) df_secondary <- tribble( ~ID, ~z, "A", 30, "B", 21, "C", 22, "D", 25, "E", 29) Dplyr left_join()

The most common way to merge two datasets is to use the left_join() function. We can see from the picture below that the key-pair matches perfectly the rows A, B, C and D from both datasets. However, E and F are left over. How do we treat these two observations? With the left_join(), we will keep all the variables in the original table and don’t consider the variables that do not have a key-paired in the destination table. In our example, the variable E does not exist in table 1. Therefore, the row will be dropped. The variable F comes from the origin table; it will be kept after the left_join() and return NA in the column z. The figure below reproduces what will happen with a left_join().

Example of dplyr left_join()

left_join(df_primary, df_secondary, by ='ID')

Output:

## # A tibble: 5 x 3 ## ID y.x y.y ## 1 A 5 30 ## 2 B 5 21 ## 3 C 8 22 ## 4 D 0 25 ## 5 F 9 NA Dplyr right_join()

The right_join() function works exactly like left_join(). The only difference is the row dropped. The value E, available in the destination data frame, exists in the new table and takes the value NA for the column y.

Example of dplyr right_join()

right_join(df_primary, df_secondary, by = 'ID')

Output:

## # A tibble: 5 x 3 ## ID y.x y.y ## 1 A 5 30 ## 2 B 5 21 ## 3 C 8 22 ## 4 D 0 25 ## 5 E NA 29 Dplyr inner_join()

When we are 100% sure that the two datasets won’t match, we can consider to return only rows existing in both dataset. This is possible when we need a clean dataset or when we don’t want to impute missing values with the mean or median.

The inner_join()comes to help. This function excludes the unmatched rows.

Example of dplyr inner_join()

inner_join(df_primary, df_secondary, by ='ID')

Output:

## # A tibble: 4 x 3 ## ID y.x y.y ## 1 A 5 30 ## 2 B 5 21 ## 3 C 8 22 ## 4 D 0 25 Dplyr full_join()

Finally, the full_join() function keeps all observations and replace missing values with NA.

Example of dplyr full_join()

full_join(df_primary, df_secondary, by = 'ID')

Output:

## # A tibble: 6 x 3 ## ID y.x y.y ## 1 A 5 30 ## 2 B 5 21 ## 3 C 8 22 ## 4 D 0 25 ## 5 F 9 NA ## 6 E NA 29 Multiple Key pairs

Last but not least, we can have multiple keys in our dataset. Consider the following dataset where we have years or a list of products bought by the customer.

If we try to merge both tables, R throws an error. To remedy the situation, we can pass two key-pairs variables. That is, ID and year which appear in both datasets. We can use the following code to merge table1 and table 2

df_primary <- tribble( ~ID, ~year, ~items, "A", 2024,3, "A", 2024,7, "A", 2023,6, "B", 2024,4, "B", 2024,8, "B", 2023,7, "C", 2024,4, "C", 2024,6, "C", 2023,6) df_secondary <- tribble( ~ID, ~year, ~prices, "A", 2024,9, "A", 2024,8, "A", 2023,12, "B", 2024,13, "B", 2024,14, "B", 2023,6, "C", 2024,15, "C", 2024,15, "C", 2023,13) left_join(df_primary, df_secondary, by = c('ID', 'year'))

Output:

## # A tibble: 9 x 4 ## ID year items prices ## 1 A 2024 3 9 ## 2 A 2024 7 8 ## 3 A 2023 6 12 ## 4 B 2024 4 13 ## 5 B 2024 8 14 ## 6 B 2023 7 6 ## 7 C 2024 4 15 ## 8 C 2024 6 15 ## 9 C 2023 6 13 Data Cleaning Functions in R

Following are the four important functions to tidy (clean) the data:

Function Objective Arguments

gather() Transform the data from wide to long (data, key, value, chúng tôi = FALSE)

spread() Transform the data from long to wide (data, key, value)

separate() Split one variables into two (data, col, into, sep= “”, remove = TRUE)

unit() Unit two variables into one (data, col, conc ,sep= “”, remove = TRUE)

If not installed already, enter the following command to install tidyr:

install tidyr : install.packages("tidyr")

The objectives of the gather() function is to transform the data from wide to long.

Syntax

gather(data, key, value, chúng tôi = FALSE) Arguments: -data: The data frame used to reshape the dataset -key: Name of the new column created -value: Select the columns used to fill the key column -na.rm: Remove missing values. FALSE by default

Example

Below, we can visualize the concept of reshaping wide to long. We want to create a single column named growth, filled by the rows of the quarter variables.

library(tidyr) # Create a messy dataset messy <- data.frame( country = c("A", "B", "C"), q1_2024 = c(0.03, 0.05, 0.01), q2_2024 = c(0.05, 0.07, 0.02), q3_2024 = c(0.04, 0.05, 0.01), q4_2024 = c(0.03, 0.02, 0.04)) messy

Output:

## country q1_2024 q2_2024 q3_2024 q4_2024 ## 1 A 0.03 0.05 0.04 0.03 ## 2 B 0.05 0.07 0.05 0.02 ## 3 C 0.01 0.02 0.01 0.04 # Reshape the data gather(quarter, growth, q1_2024:q4_2024) tidier

Output:

## country quarter growth ## 1 A q1_2024 0.03 ## 2 B q1_2024 0.05 ## 3 C q1_2024 0.01 ## 4 A q2_2024 0.05 ## 5 B q2_2024 0.07 ## 6 C q2_2024 0.02 ## 7 A q3_2024 0.04 ## 8 B q3_2024 0.05 ## 9 C q3_2024 0.01 ## 10 A q4_2024 0.03 ## 11 B q4_2024 0.02 ## 12 C q4_2024 0.04

In the gather() function, we create two new variable quarter and growth because our original dataset has one group variable: i.e. country and the key-value pairs.

The spread() function does the opposite of gather.

Syntax

spread(data, key, value) arguments: data: The data frame used to reshape the dataset key: Column to reshape long to wide value: Rows used to fill the new column

Example

We can reshape the tidier dataset back to messy with spread()

# Reshape the data spread(quarter, growth) messy_1

Output:

## country q1_2024 q2_2024 q3_2024 q4_2024 ## 1 A 0.03 0.05 0.04 0.03 ## 2 B 0.05 0.07 0.05 0.02 ## 3 C 0.01 0.02 0.01 0.04

The separate() function splits a column into two according to a separator. This function is helpful in some situations where the variable is a date. Our analysis can require focussing on month and year and we want to separate the column into two new variables.

Syntax

separate(data, col, into, sep= "", remove = TRUE) arguments: -data: The data frame used to reshape the dataset -col: The column to split -into: The name of the new variables -sep: Indicates the symbol used that separates the variable, i.e.: "-", "_", "&" -remove: Remove the old column. By default sets to TRUE.

Example

We can split the quarter from the year in the tidier dataset by applying the separate() function.

separate(quarter, c(“Qrt”, “year”), sep =”_”) head(separate_tidier)

Output:

## country Qrt year growth ## 1 A q1 2023 0.03 ## 2 B q1 2023 0.05 ## 3 C q1 2023 0.01 ## 4 A q2 2023 0.05 ## 5 B q2 2023 0.07 ## 6 C q2 2023 0.02

The unite() function concanates two columns into one.

Syntax

unit(data, col, conc ,sep= "", remove = TRUE) arguments: -data: The data frame used to reshape the dataset -col: Name of the new column -conc: Name of the columns to concatenate -sep: Indicates the symbol used that unites the variable, i.e: "-", "_", "&" -remove: Remove the old columns. By default, sets to TRUE

Example

In the above example, we separated quarter from year. What if we want to merge them. We use the following code:

unite(Quarter, Qrt, year, sep =”_”) head(unit_tidier)

Output:

## country Quarter growth ## 1 A q1_2024 0.03 ## 2 B q1_2024 0.05 ## 3 C q1_2024 0.01 ## 4 A q2_2024 0.05 ## 5 B q2_2024 0.07 ## 6 C q2_2024 0.02 Summary

Data analysis can be divided into three parts: Extraction, Transform, and Visualize.

R has a library called dplyr to help in data transformation. The dplyr library is fundamentally created around four functions to manipulate the data and five verbs to clean the data.

dplyr provides a nice and convenient way to combine datasets. A join with dplyr adds variables to the right of the original dataset.

The beauty of dplyr is that it handles four types of joins similar to SQL:

left_join() – To merge two datasets and keep all observations from the origin table.

right_join() – To merge two datasets and keep all observations from the destination table.

inner_join() – To merge two datasets and exclude all unmatched rows.

full_join() – To merge two datasets and keep all observations.

Using the tidyr Library, you can transform a dataset using following functions:

gather(): Transform the data from wide to long.

spread(): Transform the data from long to wide.

separate(): Split one variable into two.

unit(): Unit two variables into one.

Database Testing Using Selenium: How To Connect?

Database Connection in Selenium

Selenium Webdriver is limited to Testing your applications using Browser. To use Selenium Webdriver for Database Verification you need to use the JDBC (“Java Database Connectivity”).

JDBC (Java Database Connectivity) is a SQL level API that allows you to execute SQL statements. It is responsible for the connectivity between the Java Programming language and a wide range of databases. The JDBC API provides the following classes and interfaces

Driver Manager

Driver

Connection

Statement

ResultSet

SQLException

How to Connect Database in Selenium

In order to test your Database using Selenium, you need to observe the following 3 steps

Step 1) Make a connection to the Database

In order to make a connection to the database the syntax is

DriverManager.getConnection(URL, "userid", "password" )

Here,

Userid is the username configured in the database

Password of the configured user

And the code to create connection looks like

Connection con = DriverManager.getConnection(dbUrl,username,password);

You also need to load the JDBC Driver using the code

Class.forName("com.mysql.jdbc.Driver"); Step 2) Send Queries to the Database

Once connection is made, you need to execute queries.

You can use the Statement Object to send queries.

Statement stmt = con.createStatement();

Once the statement object is created use the executeQuery method to execute the SQL queries

stmt.executeQuery(select * from employee;); Step 3) Process the results

Results from the executed query are stored in the ResultSet Object.

Example of Database Testing with Selenium

Step 1) Install MySQL Server and MySQL Workbench

Check out the complete guide to Mysql & Mysql Workbench here

While installing MySQL Server, please note the database

Username

Password

Port Number

It will be required in further steps.

Step 2) In MySQL WorkBench, connect to your MySQL Server

In the next screen,

Select Local Instance of MySQL

Enter Port Number

Enter Username

Enter Password

Step 3) To Create Database,

Enter Name of Schema/Database

Step 4) In the navigator menu,

Enter Table name as employee

Enter Fields as Name and Age

Step 5) We will create following data

Name Age

Top 25

Nick 36

Bill 47

To create data into the Table

In navigator, select the employee table

Enter Name and Age

Repeat the process until all data is created

Step 6) Download the MySQL JDBC connector here

Step 7) Add the downloaded Jar to your Project

Select the libraries

You can see MySQL connector java in your library

Step 8) Copy the following code into the editor

Package htmldriver; import java.sql.Connection; import java.sql.Statement; import java.sql.ResultSet; import java.sql.DriverManager; import java.sql.SQLException; public class SQLConnector { public static void main(String[] args) throws ClassNotFoundException, SQLException { String dbUrl = "jdbc:mysql://localhost:3036/emp"; String username = "root"; String password = "guru99"; String query = "select * from employee;"; Class.forName("com.mysql.jdbc.Driver"); Connection con = DriverManager.getConnection(dbUrl,username,password); Statement stmt = con.createStatement(); ResultSet rs= stmt.executeQuery(query); while (rs.next()){ String myName = rs.getString(1); String myAge = rs.getString(2); System. out.println(myName+" "+myAge); } con.close(); } }

Step 8) Execute the code, and check the output

Selenium Database Testing Summary

Step 1) Make a connection to the Database using method.

DriverManager.getConnection(URL, "userid", "password")

Step 2) Create Query to the Database using the Statement Object.

Statement stmt = con.createStatement();

Step 3) Send the query to database using execute query and store the results in the ResultSet object.

ResultSet rs = stmt.executeQuery(select * from employee;);

We will create following data

How To Download & Install Selenium Webdriver

Selenium WebDriver Installation

Selenium installation is a 3-step process:

Step 4: Configure Eclipse IDE with WebDriver

In this tutorial, we will learn how to install Selenium Webdriver. Below is the detailed process

NOTE: The versions of Java, Eclipse, Selenium will keep updating with time. But the installation steps will remain the same. Please select the latest version and continue the installation steps below-

Step 1 – Install Java Software Development Kit (JDK)

Download and install the Java Software Development Kit (JDK) here.

This JDK version comes bundled with Java Runtime Environment (JRE), so you do not need to download and install the JRE separately.

Once installation is complete, open command prompt and type “java”. If you see the following screen you are good to move to the next step.

Step 2 – Install Eclipse IDE

Download the latest version of “Eclipse IDE for Java Developers” here. Be sure to choose correctly between Windows 32 Bit and 64 Bit versions.

You should be able to download an exe file named “eclipse-inst-win64” for Setup.

This will start eclipse neon IDE for you.

Step 3 – Selenium WebDriver Installation

You can download Selenium Webdriver for Java Client Driver here. You will find client drivers for other languages there, but only choose the one for Java.

This download comes as a ZIP file named “selenium-3.14.0.zip”. For simplicity of Selenium installation on Windows 10, extract the contents of this ZIP file on your C drive so that you would have the directory “C:selenium-3.14.0”. This directory contains all the JAR files that we would later import on Eclipse for Selenium setup.

Step 4 – Configure Eclipse IDE with WebDriver

Launch the “eclipse.exe” file inside the “eclipse” folder that we extracted in step 2. If you followed step 2 correctly, the executable should be located on C:eclipseeclipse.exe.

When asked to select for a workspace, just accept the default location.

A new pop-up window will open. Enter details as follow

Project Name

Location to save a project

Select an execution JRE

Select the layout project option

4. In this step,

A pop-up window will open to name the package,

Enter the name of the package

Name of the class

This is how it looks like after creating class.

Now selenium WebDriver’s into Java Build Path

In this step,

Select all files inside the lib folder.

Select files outside lib folder

6. Add all the JAR files inside and outside the “libs” folder. Your Properties dialog should now look similar to the image below.

Different Drivers

HTMLUnit is the only browsers that WebDriver can directly automate – meaning that no other separate component is needed to install or run while the test is being executed. For other browsers, a separate program is needed. That program is called as the Driver Server.

A driver server is different for each browser. For example, Internet Explorer has its own driver server which you cannot use on other browsers. Below is the list of driver servers and the corresponding browsers that use them.

You can download these drivers here

Browser Name of Driver Server Remarks

HTMLUnit HtmlUnitDriver WebDriver can drive HTMLUnit using HtmlUnitDriver as driver server

Firefox Mozilla GeckoDriver WebDriver can drive Firefox without the need of a driver server Starting Firefox 45 & above one needs to use gecko driver created by Mozilla for automation

Internet Explorer Internet Explorer Driver Server Available in 32 and 64-bit versions. Use the version that corresponds to the architecture of your IE

Chrome ChromeDriver Though its name is just “ChromeDriver”, it is, in fact, a Driver Server, not just a driver. The current version can support versions higher than Chrome v.21

Opera OperaDriver Though its name is just “OperaDriver”, it is, in fact, a Driver Server, not just a driver.

PhantomJS GhostDriver PhantomJS is another headless browser, just like HTMLUnit.

Safari SafariDriver Though its name is just “SafariDriver”, it is, in fact, a Driver Server, not just a driver.

Summary

Aside from a browser, you will need the following to start using WebDriver

When starting a WebDriver project in Eclipse, do not forget to import the Java Client Driver files onto your project. These files will constitute your Selenium Library.

With a new version of Selenium, there is no browser that you can automate without the use of a Driver Server.

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