Why Most Agencies Fail at LinkedIn Management

Let’s address the elephant in the room: managing multiple LinkedIn accounts manually is a recipe for disaster.

When I audit agency operations, I consistently find these critical bottlenecks:

Time drain: Even with just 5 clients, you’re looking at 40+ hours weekly of repetitive tasks
Inconsistent posting: LinkedIn’s algorithm rewards 3-5 weekly posts — miss this, and engagement drops 150%+
Reporting headaches: Clients want specific metrics that are impossible to track manually
Scaling ceiling: Each new client creates exponential complexity without proper systems
Security nightmares: Juggling multiple client logins is a disaster waiting to happen

The reality is that .. clients don’t care about your process problems. They want results, and they want them consistently.

The right tools don’t replace strategy – they amplify it. I’ve seen agencies double their client load with the same team size simply by implementing the right LinkedIn automation stack.

The 10 Best LinkedIn Tools for Agencies in 2025

After testing dozens of options, these are the tools that actually deliver for agencies managing multiple client accounts.

1. LiGo – The Complete LinkedIn Management System for Agencies

Best for: Agencies managing multiple client accounts that need to preserve unique voices

In my decade of analyzing agency tools, I’ve never seen a platform so purpose-built for solving agency-specific LinkedIn challenges. While most tools are designed for individual creators then awkwardly adapted for agencies, LiGo was engineered from the ground up for multi-account management.

What separates LiGo from everything else I’ve tested:

Theme-based content ecosystem: Create distinct content themes for each client that maintain their authentic voice and expertise level
Multi-account dashboard: Manage unlimited client accounts from one interface (a godsend for agencies)
AI that actually sounds human: Their proprietary system learns each client’s voice and produces content that doesn’t have that “AI written” feel
Multi-variant generation: Get 6 different post versions (3 in your client’s style, 3 optimized for virality) with one click
Chrome extension that works: Generate posts and engage with comments directly while browsing LinkedIn
Analytics that clients understand: Detailed performance tracking with white-label reporting capabilities

What particularly impressed me was how the system gets smarter over time. Unlike other AI tools that produce the same generic output regardless of performance, LiGo’s memory-enhanced system continuously improves its understanding of what works for each specific client.

The agencies I’ve seen implement LiGo typically report a 60-80% reduction in content production time while maintaining or improving content quality — a rare combination in the tool space.

See how LiGo compares to Taplio for agencies →

2. Taplio – Content Discovery and Basic Scheduling

Best for: Individual creators or small agencies focused on content inspiration

I’ve tracked Taplio’s evolution closely, and while it’s gained popularity, I’ve found it works best for individual creators rather than agencies managing multiple clients.

In my testing, these are the key strengths and limitations:

Strengths:

Decent post scheduling system
Useful content inspiration from other LinkedIn posts
Basic performance analytics (on par with the basic analytics LinkedIn itself offers)
Entry-level AI writing assistance (not sure why they haven’t been able to crack it yet)

Agency Limitations:

Multi-account structure requires separate subscriptions for each client (costs add up quickly)
AI generates relatively generic content that requires substantial editing
Analytics lack the depth needed for professional client reporting
Team collaboration features are minimal

I’ve seen agencies try to scale with Taplio and inevitably hit a wall around 5-7 clients, where the inefficiencies in multi-account management create significant operational challenges.

Compare Taplio vs. LiGo feature-by-feature →

3. AuthoredUp – LinkedIn Post Formatting Enhancement

Best for: Agencies focused primarily on visual formatting improvements

I’ve used AuthoredUp extensively, and it excels at one specific thing: enhancing the visual presentation of LinkedIn posts. However, it addresses only a fraction of the agency workflow.

Strengths:

Enhanced text formatting options
Clean visual templates
Simple post scheduling

Agency Limitations:

Covers only the formatting aspect of content creation
No support for content strategy, ideation, or analytics
Requires multiple additional tools to create a complete workflow
Limited team collaboration capabilities

AuthoredUp works well as a specialized formatting tool, but agencies typically need 3-4 additional platforms to create a functional process, creating significant inefficiencies.

See how AuthoredUp compares to more comprehensive solutions →

4. EasyGen – Basic AI Post Generation

Best for: Individual users or small agencies with simple content needs

I’ve analyzed EasyGen extensively and found it offers straightforward AI content generation but lacks the strategic framework and multi-account capabilities that professional agencies require.

Strengths:

Simple AI content generation
Basic post scheduling
User-friendly interface

Agency Limitations:

Content tends toward generic “AI-sounding” posts
No strategic framework for developing cohesive content themes
Lacks team collaboration features and approval workflows
No multi-account architecture for agency scaling

The agencies I’ve consulted with who attempted to use EasyGen for client work consistently report significant challenges in maintaining client-specific voice and expertise levels.

Compare EasyGen vs. LiGo for agency workflows →

5. Scripe – Basic Post Drafting Tool

Best for: Individual users seeking basic post formatting assistance

In my testing, Scripe focuses primarily on helping users format basic posts without addressing the broader content strategy or multi-account management needs that define agency operations.

Strengths:

Simple post drafting interface
Basic text formatting options
Straightforward user experience

Agency Limitations:

Addresses only basic post creation
No multi-account management capabilities
Lacks team collaboration workflows
No performance analytics

Like AuthoredUp, Scripe solves only a small segment of the agency workflow, requiring multiple additional tools to create a complete system.

See how Scripe compares to agency-focused alternatives →

6. Dux-Soup – LinkedIn Outreach Automation

Best for: Agencies focusing specifically on client prospecting campaigns

I’ve implemented Dux-Soup for several clients, and it specializes in automating LinkedIn outreach processes rather than content creation or thought leadership development.

Strengths:

Efficient connection request campaigns
Sequential messaging workflows
Profile visit automation
Basic CRM-style tracking

Agency Limitations:

Focuses solely on outreach, not content
Desktop-based operation creates challenges for remote teams
Security concerns with client login credentials
Potential compliance issues with LinkedIn’s terms of service

I’ve observed LinkedIn increasing enforcement against automation tools that simulate user behavior, creating compliance risks for agencies relying heavily on such platforms.

7. Hootsuite – Multi-Platform Social Media Management

Best for: Agencies managing LinkedIn alongside multiple other social platforms

I’ve used Hootsuite since its early days, and while it’s a solid general social media tool, its LinkedIn-specific capabilities are notably limited compared to specialized options.

Strengths:

Cross-platform scheduling
Decent team collaboration features
Established workflow processes

Agency Limitations:

Lacks LinkedIn-specific optimization features
Content creation capabilities are minimal
Analytics don’t provide LinkedIn-specific insights
No support for LinkedIn’s unique content requirements

Agencies I’ve worked with who use Hootsuite for LinkedIn management typically supplement it with 2-3 additional tools to address its limitations.

8. Buffer – Simple Social Scheduling

Best for: Agencies with basic scheduling needs across platforms

My experience with Buffer has shown it’s excellent for straightforward scheduling but lacks depth for serious LinkedIn strategy.

Strengths:

Clean, intuitive interface
Reliable scheduling
Consistent performance

Agency Limitations:

Minimal LinkedIn-specific features
Limited content creation support
Basic analytics that lack strategic insights
No specialized multi-account management

Like Hootsuite, Buffer works well as a general social media tool but requires significant supplementation for comprehensive LinkedIn management.

9. SocialPilot – Team Collaboration for Social Media

Best for: Agencies prioritizing workflow management for social media

In my evaluation, SocialPilot offers strong team collaboration features but lacks the LinkedIn-specific capabilities needed for sophisticated strategy.

Strengths:

Well-designed approval workflows
Client access options
Decent cross-platform scheduling

Agency Limitations:

Limited LinkedIn-specific optimization
Basic content creation capabilities
Analytics lack strategic depth
Not designed specifically for agency scaling

SocialPilot solves workflow challenges but doesn’t address the content strategy and performance optimization aspects of LinkedIn management.

10. LinkedIn Sales Navigator – Advanced Prospecting

Best for: Agencies focused primarily on LinkedIn prospecting

I’ve implemented Sales Navigator for numerous clients, and it’s excellent for its specific purpose: enhancing prospecting capabilities.

Strengths:

Powerful LinkedIn search functionality
Useful lead recommendations
Solid CRM integration options

Agency Limitations:

Purely a prospecting tool with no content capabilities
Requires separate solutions for content strategy and creation
No scheduling or posting features
Significant per-seat licensing costs for agencies

Sales Navigator works well as part of a broader LinkedIn toolkit but doesn’t address content creation or management needs.

How to Choose the Right LinkedIn Tool for Your Agency

After evaluating dozens of tools and consulting with agencies of all sizes, I’ve developed this framework for making the right selection:

10 Critical Questions to Ask Before Investing in Any LinkedIn Tool

Multi-Client Architecture: Does it efficiently manage multiple client accounts, or will you need separate logins/subscriptions for each?
Content Strategy Support: Does it just help you post, or does it provide strategic frameworks for developing cohesive content themes?
Voice Preservation: Can it maintain each client’s unique voice and expertise level, or does everything sound generically “professional”?
Team Workflows: Does it support role-based permissions and approval processes for efficient team collaboration?
Client Reporting: What level of performance data does it provide, and can reports be customized for client presentations?
Integration Capability: Does it work with your existing tech stack, or will it create additional workflow steps?
Security Protocol: How does it handle client login credentials and comply with LinkedIn’s terms of service?
Pricing Structure: Is the pricing model sustainable as you add clients, or will costs balloon with scale?
Scalability Path: Can it support your operations at 2x or 5x your current client load?
Support Quality: What onboarding resources and ongoing support are available when issues arise?

I’ve found that agencies that thoroughly evaluate tools against these criteria typically avoid the costly mistake of implementing systems that create more problems than they solve.

The Future of LinkedIn Management for Agencies in 2025 and Beyond

Based on my analysis of emerging technology and market trends, here’s where I see LinkedIn tools for agencies heading:

5 Transformative Trends Reshaping Agency LinkedIn Tools

Consolidated Ecosystems: The most innovative platforms are shifting from point solutions toward comprehensive ecosystems that support the entire content lifecycle — addressing the operational inefficiency of managing multiple disconnected tools.
Client-Specific AI Models: Advanced platforms now develop specialized AI models for each client that learn from past content and performance, preserving authentic voice while dramatically increasing production capacity.
Agency-First Architecture: Purpose-built agency tools with multi-account management, team collaboration workflows, and client approval systems are replacing platforms designed primarily for individual creators.
Strategic Intelligence: Leading solutions now provide actionable strategy recommendations based on performance patterns rather than just displaying basic engagement metrics.
Compliance-Centered Automation: As LinkedIn increases enforcement against certain automation practices, tools that work within platform guidelines are becoming essential for sustainable agency operations.

I’m particularly watching how platforms like LiGo are pioneering this next generation of tools — comprehensive ecosystems that learn from performance data, adapt to client-specific requirements, and provide actionable intelligence that directly improves outcomes.

Strategic Implementation: My Framework for Agency Success

After observing hundreds of agencies implement LinkedIn tools, I’ve identified clear patterns that separate successful implementations from disappointing ones.

The most effective approach combines systematic automation of repetitive tasks with preservation of the authentic expertise and perspective that makes each client unique:

Start with Strategy, Not Tools: Define clear objectives and content pillars for each client before implementing any automation
Automate Selectively: Target repetitive tactical processes for automation while maintaining human oversight of strategy and voice
Implement Continuous Review: Regularly audit automated outputs to ensure alignment with client voice and objectives
Use Data to Refine: Leverage analytics to continuously improve both strategy and execution
Educate Clients Properly: Help clients understand how technology amplifies rather than replaces authentic expertise

I’ve seen agencies double or even triple their LinkedIn management capacity by selecting tools that support their strategic methodology rather than forcing workflow changes.

FAQ: LinkedIn Tools for Agency Owners

Q: Can’t we just use LinkedIn’s native scheduling?
A: In my testing, native scheduling lacks critical features for agencies: no multi-account management, limited analytics, no content creation support, and basic scheduling options. It’s insufficient for professional agency operations.

Q: How many clients can one person manage with the right tools?
A: With comprehensive platforms like LiGo, I typically see one person effectively managing 15-20 client accounts, compared to 3-5 accounts with manual methods or basic tools.

Q: How do I maintain authentic client voices when scaling?
A: The most sophisticated tools now use AI that learns from existing client content and performance data to maintain authentic voice while scaling production. This approach preserves uniqueness while dramatically increasing efficiency.

Q: Are these tools compliant with LinkedIn’s terms of service?
A: The tools I’ve recommended that focus on content creation and scheduling operate within LinkedIn’s guidelines. I’ve avoided recommending automation tools that simulate user behavior, which can trigger account restrictions.

Q: What’s the ROI timeframe for implementing LinkedIn tools?
A: In my experience with agency implementations, comprehensive platforms typically show positive ROI within 30-45 days through time savings, improved content performance, and enhanced client retention.

The Bottom Line: LinkedIn Success Is a System, Not a Tool

After a decade analyzing agencies’ LinkedIn operations, I’ve found that sustainable success comes from implementing a cohesive system rather than collecting individual tools.

The agencies achieving the most significant growth in 2025 are those leveraging tools that enhance their strategic capabilities rather than simply automating basic tasks. They’re building scalable systems that maintain authenticity while dramatically increasing output and performance.

For agencies committed to scaling their LinkedIn services while preserving client-specific voice and strategic excellence, comprehensive platforms like LiGo represent the most efficient path forward.

This analysis reflects the LinkedIn tool landscape as of March 2025. Always conduct your own evaluation based on your specific agency requirements and client needs.

Google Colab Notebook Tutorial | How to import a dataset

Introduction

In this article, we are going to learn about Google colaboratory notebooks in as much detail as possible. This tutorial is going to cover a variety of things related to Google colab, the details of which you can find in the Table of Contents Below.

What Google colab offers
How to setup Google colab
Enabling GPU and some basic functions
Importing files/datasets to Google colab notebook

If you have already used Google colab and know a few things feel free to skip any part that you’re already familiar with and jump right to the part you are interested in.

What Google Colab Offers

The reason why Google collab has become so popular nowadays is mainly because they offer free GPU for training for up to 12 hours continuously and even after that you can reconnect for a new session and the thing is that most people do not have a GPU in their workstation. Those who do – it’s not manufactured by Nvidia. The current GPU that collab offers is nvidia tesla T4.

How to setup Google Colab

First of all you need a gmail account.
Go to your google drive.
Once you are there, right click, click on more, click on connect more apps.

4. Type colaboratory in the search bar and the click on install.

5. After installation close this tab.

6. After that right click and go to “more” and open colaboratory. It will create a new colab notebook for you.

7. To rename your notebook:

8. Buttons to add more code cells, to add more text cells and to delete cells are as follows. It’s as simple as that.

Enabling GPU and some basic functions

To check which devices are connected to your notebook currently, run the commands mentioned below:

from tensorflow.python.client import device_lib

device_lib.list_local_devices ()

Currently we only have a CPU connected to its memory.

To avail Google’s free GPU service, click on “edit” and then “notebook settings” to enable GPU. Save it as you go. It will take some time to connect (5-6 seconds).

Run the above code again. Now it will show that a GPU device is connected as well. It’s name is Tesla T4.

To install a library or a framework write the command !pip install and the name of the framework or library. Let’s try tensorflow.

Now this command might not always work, in case it doesn’t, just go to Google and write “how to install ‘library name’ on google collaboratory notebooks”. The first few searches will probably give you a single line command that you can run on your notebook to install that library on your Google collab notebooks.

To clone a github repository through your Google Drive simply just go to that repository and copy the link, come back to your notebook and write this command with your link. !wget clone “your repo link” With this your repository will be cloned in your file.

To check the content of your current folder use the command ‘!ls’. As you can see the repository has been cloned in our current directory.

Let’s now learn how to navigate to the Google Drive folders. The LS command shows you the content of your current directory. To move back one directory, this is the command that you would run.

To see the contents of your current directory use the same command “!ls”. This is what you will get.

Let’s go to the content folder with this command. cd content. Let’s see what we have inside it. It has Drive simple data and the file you imported from git just now.

Go to sample data with command cd sample_data to put some datasets in there. First go into the file and check its contents. If you want to remove something just type RM and the name of the file. To see if the file has been removed or not, type the LS command again. It should have been removed.

To see the contents of a file use this command !cat "name of file". It displays what was inside that file

Importing files or datasets into Google colab notebook

Let us now learn how to import our dataset into our Google collaboratory notebook. For that, go to Google Apps website with this link.

Copy this line of code and also import pandas library.

Run this cell**

Go back, copy this line of code, paste it in one cell and run it.

uploaded = files.upload()

Choose files and upload them, in this case it is an Expenses.csv file and try to read that file with the command shown.

To import an online dataset into your Google collaborate notebook the easiest way to do that is to write this command and add the link to it.

!wget "the link to import dataset"

Let’s add this link for example and run the cell. The name of the data set as you can see is Titanic.csv.

Now try to read it with the following command.

The data set has been successfully imported to our Google colaboratory notebook.

Before we continue, I would like to discuss a common error that arises when you’re using colab.

It means that all the GPUs that Google offers are currently busy and you can solve this problem simply by trying a few minutes later.

To see the parameters that a function of any library like tensor-flow, keras and scikit learn uses, this is how you can do it.

The documentation for that function will be shown to you which will include the parameters that the function takes as well as what each parameter means and in some cases it will also include an example of how that function can be used.

That’s all folks. Have a good time coding with Google Colab.

How to code a classification & regression model in Python

In this article, we are going to first get an introduction to Supervised learning, followed by a little dive into the two most common types of supervised learning algorithms; namely, classification & regression. At the end we will have two coding examples, one for classification and one for regression. Both will use a different dataset and go through the steps in each algorithm.

The Table of Contents is added below. Read it before moving to the next parts, so you can first decide if this article is relevant for you.

Introduction to Supervised Learning
Introduction to Classification & Regression
2.1 Classification
2.2 Regression
Prerequisites for the code examples
Classification Example
4.1 Python Code
Regression Example
5.1 Python Code
Supervised Learning Applications
Conclusion

Introduction to Supervised Learning

Supervised Learning is the most common type of learning in Machine Learning. During training, the algorithm is given the dataset with the correct answers/labels, thus the name ‘supervised’. Then, during testing, model tries to predict the correct output for similar new examples on the basis of what it has learnt from the previous data samples. To put this in a more relatable manner, lets consider a student preparing for a Maths exam. (S)he first does practice questions for which they can see the answers. If they get the wrong answer, they backpropagate to see which ‘step’ they messed up in and try to correct that. In the first go, they might get only 2 out of 10 practice questions correct, in which case, they would re-do them. Once they start getting more than 90% of their practice questions right, they could consider themselves ready for the actual exam. In the exam, they will get questions they haven’t seen or solved before, but would use the concepts learned during practice and try to solve them. That’s supervised learning in a nutshell!

Supervised Learning Algorithms: Classification & Regression

We are going to talk about two most important/commonly-used techniques in supervised learning:

Classification

Target variable consists of categories i.e. used to identify to which category an object belongs to. The output variable is discrete. Consider a dataset of cat and dog images. The classifier would take as input an image and its output would fall into two discrete categories: cat or dog. We can take the digit classifier we are going to code as an example, too. In cat vs dog classifier, there are two classes, in digits classifier there will be 10 i.e. Class 0 to Class 9, since there are a total of 10 digits.

Input: Image containing either a cat or a dog
Output: Probability values for each class (Example: {‘Cat: 0.80’, ‘Dog:0.20’})

Regression

Target variable is continuous i.e. used to predict a continuous valued attribute associated with an object. The output variable is a real value. For example, consider a dataset of house prices in a certain area. The classifier would take as input features of the house like number of rooms, area, furnished (yes/no), etc. and based on that has to output the estimated worth of the house. That is a regression task because price will be a continuous output.

Input: Csv file containing columns like number of bedrooms, area of the house in sq. ft. etc.
Output: Predicted Price or worth of the house (Example: $2501)

Prerequisites for the code examples

Before you go ahead, please note that there are a few prerequisites for understanding the code examples. It’s beginner-friendly but you should have some prior basic knowledge of Machine Learning and programming in general, in any language (but preferably Python). You must also have Python 3.7 & Scikit-learn library installed as we will be using its pre-built Digits dataset for our example. Other than that, the rest of the article is pretty easy to follow. We will also be using Jupyter Notebooks for writing the code. If you do not already have it installed, visit Jupyter Notebook before you begin the tutorial.

Coding Language: Python 3.7
IDE: Jupyter Notebook
Libraries: Sklearn, Matplotlib

Classification Example

We will be building an application to recognize handwritten digits using Digits Dataset which is included in scikit-learn’s datasets module. Each sample in this scikit-learn dataset is an 8×8 image representing a handwritten digit. This is a multiclass image classification problem with 10 classes representing digits from 0 to 9. We wish to classify the handwritten digits into their respective classes from 0 to 9 on the basis of the intensity values within the image which depict the shape of the digit. For more on this dataset, visit Digits Dataset.

Python Code

# Importing dataset, libraries, classifiers and performance metric

from __future__ import division  
from sklearn.datasets import load_digits
import matplotlib.pyplot as plt
from sklearn import svm
from sklearn import tree
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split as tts
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score

# Loading digits dataset
digits = load_digits()
# Create feature matrix
x = digits.data
# Create target vector
y = digits.target

# First 6 images stored in the images attribute of the dataset
print("First 6 images of the dataset: ")

for x in range (6):

    plt.subplot(330 + 1 + x)
    plt.imshow(digits.images[x], cmap=plt.get_cmap('gray'))

plt.show()

# Flattening the image to apply classifier
n_samples = len(digits.images)
data = digits.images.reshape((n_samples, -1))

# Splitting the data into training and testing
x_train, x_test, y_train, y_test = train_test_split(data, digits.target, test_size=0.5, shuffle=False)

# Creating a classifier. SVM is set as default but you can test out other two as well by commenting out SVM and un-commennting the one you wish to try
clf = svm.SVC (gamma=0.001)

# Decision Tree Classifier
#clf = tree.DecisionTreeClassifier()

# Random Forest Classifier
#clf = RandomForestClassifier()

# Printing the details of the Classifier used
print ("Using: ", clf)

Output:

Using: SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,
  decision_function_shape='ovr', degree=3, gamma=0.001, kernel='rbf',
  max_iter=-1, probability=False, random_state=None, shrinking=True,
  tol=0.001, verbose=False)

# Training
clf.fit(x_train, y_train)
# Predicting
predictions = clf.predict(x_test)
#print ("\nPredictions:", predictions)

score = 0
for i in range(len(predictions)):

    if predictions[i] == y_test[i]:

        score += 1

print ("Accuracy:", (score / len(predictions)) * 100, "%")
# print accuracy_score(test_labels, predictions)

Output:

Accuracy: 96.88542825361512 %

Regression Example

We are going to build a regression model which predicts the rating of board games. Firstly, we will load the dataset and analyze to filter out garbage features. We’ll be doing that through the correlation matrix (strong correlation with the target/label means it’s an important feature as its value varies in a similar manner to the target value, which in our case is the rating). So lets get to it.

Python Code:

# Importing libraries, classifier and performance metric

import pandas
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import mean_squared_error

In the next few cells, we will load our dataset, analyze it, graph the correlation matrix, use the info in the correlation matrix to remove some features/columns from our dataset, and then in the end, proceed with applying our regression model on it.

# Load dataset
games = pandas.read_csv("games.csv") # download link: 

# Names of all features/columns in the dataset
print(games.columns)
print(games.shape)

# Graph a histogram based on the average_rating column/
plt.hist(games["average_rating"])

# Display the plot
plt.show()

# Data Cleaning

# Delete rows which do not contain user reviews
games = games[games["users_rated"] > 0]
# Drop rows which contain missing values 
games = games.dropna(axis=0)

# Graphing the correlation matrix
corr_mat = games.corr()
fig = plt.figure(figsize = (12, 9))
sns.heatmap(corr_mat, vmax=.8, square=True)
plt.show()

# Get the list of all columns from the dataframe
columns_list = games.columns.tolist()
# Filter the columns to remove ones we don't want.
cols = [col for col in columns_list if col not in ["bayes_average_rating", "average_rating", "type", "name", "id"]]

# the variable we'll be predicting through regression
target = "average_rating"

Splitting the dataset into training and testing set, followed by fitting the model on the training set.

train = games.sample(frac=0.8, random_state=1) # selecting 80% of the dataset as training set
# Select the rows not in the training set and put them in the testing set
test = games.loc[~games.index.isin(train.index)]
# Initialize the model class
model = LinearRegression()
# Fit the model on the training data
model.fit(train[columns], train[target])

Generating predictions and calculating the Mean Squared Error for the test set.

# Generate our predictions for the test set.
predictions = model.predict(test[columns])
print('Prediction on the first instance in Test Set: ', predictions[0])
# Compute error between our test predictions and the actual values.
print("Mean Square Error Value: ", mean_squared_error(predictions, test[target])

Supervised Learning Applications

Some common applications of Supervised Learning:

Optical Character Recognition
Handwriting Recognition
Object Recognition
Speech Recognition
Pattern Recognition
Spam Classifier
Face Recognition
Predicting Stock Price

Conclusion

To sum it all up, we started off by getting an introduction of what supervised learning is and its two main types which are Regression and Classification. We discussed how the two differ and then we went on to build a multiclass classification application about handwritten digits’ recognition, followed by a regression model to predict the average rating of board games. Lastly, we saw a few other use cases of supervised learning. All in all, we learnt how about the importance and use of Supervised learning algorithms in the world of Machine learning.