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Machine Learning Basics for a Newbie
6 Steps of Machine learning Lifecycle Introduction to Predictive Modeling
Introduction to Exploratory Data Analysis & Data Insights Descriptive Statistics Inferential Statistics How to Understand Population Distributions?
Reading Data Files into Python Different Variable Datatypes
Probability for Data Science Basic Concepts of Probability Axioms of Probability Conditional Probability
Central Tendencies for Continuous Variables Spread of Data KDE plots for Continuous Variable Overview of Distribution for Continuous variables Normal Distribution Skewed Distribution Skeweness and Kurtosis Distribution for Continuous Variable
Central Tendencies for Categorical Variables Understanding Discrete Distributions Performing EDA on Categorical Variables
Dealing with Missing Values Understanding Outliers Identifying Outliers in Data Outlier Detection in Python Outliers Detection Using IQR, Z-score, LOF and DBSCAN
Sample and Population Central Limit Theorem Confidence Interval and Margin of Error
Bivariate Analysis Introduction
Covariance Pearson Correlation Spearman's Correlation & Kendall's Tau Correlation versus Causation Tabular and Graphical methods for Bivariate Analysis Performing Bivariate Analysis on Continuous-Continuous Variables
Tabular and Graphical methods for Continuous-Categorical Variables Introduction to Hypothesis Testing P-value Two sample Z-test T-test T-test vs Z-test Performing Bivariate Analysis on Continuous-Catagorical variables
Chi-Squares Test Bivariate Analysis on Categorical Categorical Variables
Multivariate Analysis A Comprehensive Guide to Data Exploration The Data Science behind IPL
Supervised Learning vs Unsupervised Learning Reinforcement Learning Generative and Descriminative Models Parametric and Non Parametric model
Machine Learning Pipeline Preparing Dataset Build a Benchmark Model: Regression Build a Benchmark Model: Classification
Evaluation Metrics for Machine Learning Everyone should know Confusion Matrix Accuracy Precision and Recall AUC-ROC Log Loss R2 and Adjusted R2
Dealing with Missing Values Replacing Missing Values Imputing Missing Values in Data Working with Categorical Variables Working with Outliers Preprocessing Data for Model Building
Understanding Cost Function Understanding Gradient Descent Math Behind Gradient Descent Assumptions of Linear Regression Implement Linear Regression from Scratch Train Linear Regression in Python Implementing Linear Regression in R Diagnosing Residual Plots in Linear Regression Models Generalized Linear Models Introduction to Logistic Regression Odds Ratio Implementing Logistic Regression from Scratch Introduction to Scikit-learn in Python Train Logistic Regression in python Multiclass using Logistic Regression How to use Multinomial and Ordinal Logistic Regression in R ? Challenges with Linear Regression Introduction to Regularisation Implementing Regularisation Ridge Regression Lasso Regression
Introduction to K Nearest Neighbours Determining the Right Value of K in KNN Implement KNN from Scratch Implement KNN in Python
Bias Variance Tradeoff Introduction to Overfitting and Underfitting Visualizing Overfitting and Underfitting Selecting the Right Model What is Validation? Hold-Out Validation Understanding K Fold Cross Validation
Introduction to Feature Selection Feature Selection Algorithms Missing Value Ratio Low Variance Filter High Correlation Filter Backward Feature Elimination Forward Feature Selection Implement Feature Selection in Python Implement Feature Selection in R
Introduction to Decision Tree Purity in Decision Tree Terminologies Related to Decision Tree How to Select Best Split Point in Decision Tree? Chi-Squares Information Gain Reduction in Variance Optimizing Performance of Decision Tree Train Decision Tree using Scikit Learn Pruning of Decision Trees
Introduction to Feature Engineering Feature Transformation Feature Scaling Feature Engineering Frequency Encoding Automated Feature Engineering: Feature Tools
Introduction to Naive Bayes Conditional Probability and Bayes Theorem Introduction to Bayesian Adjustment Rating: The Incredible Concept Behind Online Ratings! Working of Naive Bayes Math behind Naive Bayes Types of Naive Bayes Implementation of Naive Bayes
Understanding how to solve Multiclass and Multilabled Classification Problem Evaluation Metrics: Multi Class Classification
Introduction to Ensemble Techniques Basic Ensemble Techniques Implementing Basic Ensemble Techniques Finding Optimal Weights of Ensemble Learner using Neural Network Why Ensemble Models Work well?
Introduction to Stacking Implementing Stacking Variants of Stacking Implementing Variants of Stacking Introduction to Blending Bootstrap Sampling Introduction to Random Sampling Hyper-parameters of Random Forest Implementing Random Forest Out-of-Bag (OOB) Score in the Random Forest IPL Team Win Prediction Project Using Machine Learning Introduction to Boosting Gradient Boosting Algorithm Math behind GBM Implementing GBM in python Regularized Greedy Forests Extreme Gradient Boosting Implementing XGBM in python Tuning Hyperparameters of XGBoost in Python Implement XGBM in R/H2O Adaptive Boosting Implementing Adaptive Boosing LightGBM Implementing LightGBM in Python Catboost Implementing Catboost in Python
Different Hyperparameter Tuning methods Implementing Different Hyperparameter Tuning methods GridsearchCV RandomizedsearchCV Bayesian Optimization for Hyperparameter Tuning Hyperopt
Understanding SVM Algorithm SVM Kernels In-depth Intuition and Practical Implementation SVM Kernel Tricks Kernels and Hyperparameters in SVM Implementing SVM from Scratch in Python and R
Introduction to Principal Component Analysis Steps to Perform Principal Compound Analysis Computation of Covariance Matrix Finding Eigenvectors and Eigenvalues Implementing PCA in python Visualizing PCA A Brief Introduction to Linear Discriminant Analysis Introduction to Factor Analysis
Introduction to Clustering Applications of Clustering Evaluation Metrics for Clustering Understanding K-Means Implementation of K-Means in Python Implementation of K-Means in R Choosing Right Value for K Profiling Market Segments using K-Means Clustering Hierarchical Clustering Implementation of Hierarchial Clustering DBSCAN Defining Similarity between clusters Build Better and Accurate Clusters with Gaussian Mixture Models
Understand Basics of Recommendation Engine with Case Study
8 Ways to Improve Accuracy of Machine Learning Models
Introduction to Dask Working with CuML
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to AutoML Implementation of MLBox Introduction to PyCaret TPOT Auto-Sklearn EvalML
Pickle and Joblib Introduction to Model Deployment
Deploying Machine Learning Model using Streamlit Deploying ML Models in Docker Deploy Using Streamlit Deploy on Heroku Deploy Using Netlify Introduction to Amazon Sagemaker Setting up Amazon SageMaker Using SageMaker Endpoint to Generate Inference Deploy on Microsoft Azure Cloud Introduction to Flask for Model Deploying ML model using Flask
Model Deployment in Android Model Deployment in Iphone

Video Game Clustering Using Python

Subhradeep
Subhradeep Last Updated : 06 Jun, 2022
5 min read

This article was published as a part of the Data Science Blogathon.

Introduction on Video Game Clustering

In this article, I am going to cluster some of the popular video games according to their given features. I have done this project while learning about the K-Means Clustering. After reading this article, you can able to:

  • Data manipulation using Pandas.
  • Impute missing values using KNN.
  • Reduce the dimensionality of data.
  • Apply K-Means for clustering.

I am writing this article in such a way that anyone can understand what I am doing. So, take a cup of coffee and read on.

Import Necessary Packages

Let’s import the necessary packages. The packages are given below.

Reading the Data

Now I read the data using pandas and then do some preprocessing steps on data before feeding the data into the model and gaining insights about the data. The data is in CSV format. You can download the data from my GitHub repo.

data = pd.read_csv("data/video_games.csv")

if you print the top 5 rows in this dataset, the table will look something like this.

Reading the Data
Reading the Data 2

From the above images, we can easily notice that there are 9 columns in this data. Those are:

Reading the Data 3

Data Preprocessing

Now, Let’s see if there are any null values available in the dataset. For this, run the below code.

plt.figure(figsize=(10,8))
sns.heatmap(data.isnull(), cmap='magma', yticklabels=False)

if you run this code, the output will look something like the below.

Data Preprocessing

Let’s see the number of null values numerically.

data.isnull().sum()
Data Preprocessing 3

Woah! there exist so many null values in the dataset. So for better imputation, I am using the KNearestNeighbors algorithm from the fancyimpute package. But before doing that, I want to remove some of the null values:

  • rows containing name columns as null, because this makes no sense for imputing those using KNN.
  • rows containing developer, percent_positive, num_reviews, and multiplayer as null are the main features. Without those values, KNN will unable to find a pattern for replacing those values.

I also want to drop some columns. release_date and achievements neither matter for searching for a game nor determining the genre. and for now, I am removing the original_price column for simplicity. If you have previously seen the table, the original_price column contains mixed values. Below is the image of 50 unique values of the original_price column.

 

Data Preprocessing 3

Just see the image. Some of the games are free and some of them are paid. But there is no way that I can change this column data type. So, I am dropping this column.

data.drop(['release_date', 'achievements', 'original_price'], axis=1, inplace=True)

Now, Let’s drop the null values.

After dropping all the unnecessary rows, now the size of the dataset is (40748, 6). As this is a huge dataset, I have to take a small part of this. I took two parts from the data. One for training and one for testing. After that, I encode the categorical variables and impute them.

If everything is fine, the first 5 rows for train and test will look like this after encoding.

Data Preprocessing
Data Preprocessing 2

Now it’s the time for imputation. but before that, we have to install fancyimpute. You can install this by running the below command.

pip install fancyimpute

After the successful installation, we can use the KNN algorithm from fancyimpute.

Now, if you want to verify that there are no null values in the dataset, just run the below code.

print(data1.isnull().sum())
print(data2.isnull().sum())

You will get the below output for both:

Data Preprocessing 5

Time for Modelling

After imputing all the values and verifying them, now it is time for modeling. According to the problem we have to classify using clustering techniques. I am using K-Means Clustering here. before feeding this data into the model, we have to encode it again.

Applying PCA

Now I am going to extract the important features using Principal Component Analysis (PCA). Without this, K-Means works very badly in clustering the data.

Here I am taking two components for PCA. You can try with different numbers.

Determining the Number of Clusters

Now there is a question that how to determine the number of clusters in K-means. there are two methods for this – silhouette score and elbow method. I am using the Elbow method here. We have to plot a graph on different inertias based on different cluster numbers in this method. we have to choose a cluster number from where there is minimal change in values. as this graph seems to be a human’s elbow, so this method name is the elbow method.

If you run the above code, you will get the below output.

Number of Clusters

From this graph, we can see that 2 is the desired cluster number.

Now we are ready for clustering😁.

If the code is running successfully, you can see the below plot.

Number of Clusters 2

after training the model on 2 clusters and predicting on the training set, this is the result of the machine clustering the games into two sections. though I don’t include the price column here, the machine still clusters them into two parts. Let’s see the results in the testing set.

If you run the above code, you can see the below plot:

plot

The games are also divided into the two clusters, but the data points are missing for cluster 2. The testing set may contain only those data points related to cluster 1.

Conclusion on Video Game Clustering

From the above two plots, we can clearly say that the model successfully makes the distinction between the two categories.

The Video Game Clustering Model can be improved by

  • considering the original_price column. 
  • With limited computational resources, I have to take a sample of the data and have to work on them. If I can take the whole data for model training, this model can be much more accurate in clustering the data.
  •  There is also scope for feature engineering like making a new column positive_review using positive_percent and num_reviews column.

So, that’s all for today. I hope you like this article. If you want to know about me and want to read my other articles, visit here. For now, goodbye👋.

The media shown in this article is not owned by Analytics Vidhya and is used at the Author’s discretion.

blogathonPCAprincipal component analysispythonVideo Game Clustering
Subhradeep
Subhradeep

Recently pursuing M.Tech in Artificial Intelligence and love to do anything about Data Science, Machine Learning, and AI. I also like to share my knowledge through Blogs. Ask me anything about Data Science, Machine Learning, and AI at [email protected].

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