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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
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Multivariate Analysis A Comprehensive Guide to Data Exploration The Data Science behind IPL
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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

A tribute to Sachin! Qlikview dashboard for his glorious test career

sunil
sunil Last Updated : 28 May, 2017
4 min read

Sachin has inspired an entire generation of cricketers in India and abroad. He is one of the few “Universal God” in India, i.e. people follow him irrespective of their age, gender, caste or profession. He can bring the entire nation to a stop while he is in his “zone”. (For non-cricket followers, Cricket is more than a religion in India and Sachin has been probably the most popular cricketer in India’s history).

Today was the launch of his autobiography – Playing it my way. While I plan to grab a copy and read shortly, I thought of paying my tribute to my inspiration “Sachin Ramesh Tendulkar”. So, I have created a dashboard in Qlikview to summarize Sachin’s test career and his achievements.

 

Before we start – a glimpse of Sachin-mania

Sachin has been the most complete batsman in the world cricket. Tendulkar holds about every batting record of the game – be it most runs or hundreds in Tests and ODIs. His stance and stroke making depicts the perfect balance, precision in movement and a wow factor for thousands of fans in anticipation. I can not forget his classical straight drive, back-foot punch and dancing down the track to hit sixes. He held an entire army of strokes in his kitty and would unleash them at the right moment, in the right form. He currently holds 100 centuries including both formats of the game – Test and ODI.

Tendulkar started his test career in Nov 1989 – when i was 5 years old. I started playing cricket watching Sachin bat (and bowl sometimes too). I have been crazy (like a lot of other fans) to watch him bat all these years. So as a small tribute, I have developed a qlikview dashboard to visualize Sachin’s test performance while discussing different visualization methods of Qlikview.

Let’s look at different tabs of this dashboard and what information is available in them

Sachin_Overview_Qlikview

Above is overview of Sachin’s performance summary over the years and against different teams. Here, we have various information about Sachin’s test career:

  1. On his Test debut, Sachin Tendulkar was the third youngest debutant (16years 205days) in test cricket
  2. He has played 200 test matches, 15,921 test runs and 51 test hundreds. These are highest compared to any other player on the earth.
  3. Sachin was first player to reach 12,000, 13,000, 14000 and 15,000 test runs.
  4. Here, we can easily see that he has scored 1,562 runs in 2010 which is best year of his career and 58 runs in year 1995 (worst year in his career)
  5. He scored (3,630) highest against Australia (the team which dominated the world of cricket for most of this time) and lowest (820) against Bangladesh (due to low number of matches).

 

Now lets move to Batting summary of Sachin’s test career:

12_Sachin_Batting_Qlikview

  1. He made a strong comeback between 2003 and 2004. His batting average increased from a meager 17 to 91.5
  2. He is the fourth highest runs scorer in a single calendar year in Test History. He has scored 1562 runs in 2010 and is behind Mohammad Yousuf (1780 runs in 2006), Viv Richards (1710 runs in 1976) and Graeme Smith (1656 runs in 2008).
  3. He scored highest number of 150+ score in Test Cricket (20)
  4. He has the world record for 8,844 runs in Tests away from home.
  5. When I select Away as selection in the Block chart, I can see that Sachin has scored 29 test hundreds away from home – which is a world record.
  6. He scored 7 centuries in year 2010 alone – more than what a lot of batsman manage to score in their entire career.
  7. In Runs Vs. Country Chart (Block chart), you can see that block size is representing the runs scored against country, where as the color of block represents the average. Sachin has better average against Bangladesh, Zimbabwe and Australia (136, 76.5, 55.5) where as lowest average against Pakistan, South Africa and New Zealand (42.3, 42.5, 46.9). Sachin’s lowest average is better than most of the leading batsman in world – no wonder he was referred to as a run machine.
  8. Sachin played highest number of matches against Australia (39) and England (32).

 

Now let’s look at the Sachin’s dismissals

(I know the ardent fans would be shouting that these stats include a few wrong decisions as well)

Sachin_Dismissal_Qlikview

  1. As Sachin has scored highest score against  Australia, he also got highest dismissals by them
  2. He was an awesome runner within 22 yards and has only been dismissed 9 times as run out
  3. He was dismissed highest number of times by McGrath, Gillespie and Murali (6 times) – Oh! How much I loved these battles
  4. England comes out to be a strong opponent, as Sachin got dismissed second highest number of times (49) by England and he has low average against them. Well done England!

 

Let’s look at the methods we used to visualize this information

  1. Represent images, headings and summary in text boxes
  2. Highlight maximum and minimum value of line chart, Bar chart and pivot tables
  3. Use color of block chart to represent batting average
  4. Sorting in bar chart based on expression
  5. Use of scroll bar in column chart
  6. Various formatting options for different objects like list box, column, Pie and block chart.

 

End Note:-

In this article, we looked at  various visualization methods to represent information in Qlikview dashboard while discussing about test match performance of the legend Sachin Ramesh Tendulkar. In next article, we will discuss details about these methods, specifically:

  • How to highlight maximum and minimum values of column and line charts?
  • How to show another expression by color of the section of block?
  • Sorting of dimension based on expression
  • How to use scroll bar in column chart and many other layout or visualization options.

Have you found this article interesting or you have better visualization methods to represent similar information. If so, I’d love to hear your thoughts through comments below as it also benefit someone while designing dashboard.

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sunil
sunil

Sunil Ray is Chief Content Officer at Analytics Vidhya, India's largest Analytics community. I am deeply passionate about understanding and explaining concepts from first principles. In my current role, I am responsible for creating top notch content for Analytics Vidhya including its courses, conferences, blogs and Competitions.

I thrive in fast paced environment and love building and scaling products which unleash huge value for customers using data and technology. Over the last 6 years, I have built the content team and created multiple data products at Analytics Vidhya.

Prior to Analytics Vidhya, I have 7+ years of experience working with several insurance companies like Max Life, Max Bupa, Birla Sun Life & Aviva Life Insurance in different data roles.

Industry exposure: Insurance, and EdTech

Major capabilities: Content Development, Product Management, Analytics, Growth Strategy.

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Responses From Readers

Clear

Venu
Venu

Irrelevant post on this blog...

123

Kunal Jain
Kunal Jain

Venu, Sorry to disappoint you on this post. I personally think that the topic was interesting and would have presented a nice way to get people excited on building some dashboard on a subject they love - which it did to some extent. Thanks for your feedback - highly appreciated. Regards, Kunal

123
Naveen
Naveen

Please share the data source. I would like to try this in Qlikview.

123

Sunny
Sunny

I cannot find the link to the dashboard.. Looks Interesting.

123

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