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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

How to Predict the Election Result With a Coin?

jalil
jalil Last Updated : 13 Oct, 2022
10 min read

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

Photo by Clay Banks on Unsplash

Introduction

Predicting events and happenings has been an interesting skill for humans since the dawn of civilization and remains so today. This ability has been so attractive to many that they have selected the career of “prophecy” for themselves, attracting many followers and revenue in the process. Some of those people’s names, such as “Nostradamus,” are still heard, and some people are still looking for the realization or non-realization of his predictions after centuries.

Photo by Jen Theodore on Unsplash

Forecasting what will happen in the future is considered a critical business skill. In most cases, a company’s level of insight and understanding about the future is directly linked to its level of validity, success, and the rate at which it moves through the stages of growth. So, a company makes plans based on what will happen in the future; due to this, most businesses today pay more attention to data science and spend a lot of money to make their businesses more data-driven. Many companies also employ many data scientists and set up departments for data science.

It is evident that today’s forecasts, developed with data science, are significantly different from those made centuries ago. Forecasting and getting insights with the help of data science is a scientific process based on the analysis and a close look at a data set.

Let’s leave the business world and move to the more yellow world of elections. Guess who will win an election is one of the most interesting things people do. Every year, before an election, several statistical and research agencies start collecting and analyzing data to determine who will win before voting. As a result, Politicians usually pay statistics organizations large sums of cash to produce real-time reports on how the public perceives them. This way, the candidate can use intelligence to lead his campaign.

Main Problem with Collecting Data 

Most enterprises and institutions that collect data have a big problem when they try to predict elections: they have a lot of wrong information. This problem will lead to wrong conclusions in the end. This problem was one of the most obvious during the 2016 presidential election in the US. In that year, Donald Trump and Hillary Clinton were in the race. Most polls and reputable news sites at the time said Hillary would win the election easily.

Source: NYTimes

Most people believed this prediction because, on the one hand, there was Trump, who had no political experience and had campaigned for election by making very harsh and aggressive statements. On the other hand, Clinton has been involved in politics for more than 30 years and has served as Secretary of State. With all of these explanations, it didn’t seem so unusual that Clinton beat Trump. Many reliable news sources, like the New York Times, said that Clinton’s chances of beating Trump were 85 to 15.

The Whole World is Impressed!

The whole world was amazed as the votes were being counted. Even the most optimistic statistical groups didn’t think Trump had a chance, but he won the electoral votes of key states one by one and quickly got the 270 electoral votes he needed to become president.

At the end of the vote, Trump won with 306 votes to Clinton’s 232. This was one of the most surprising results in US electoral history. This failure shocked a lot of data scientists. Many papers have been written up to this point, and many different parts of this situation have been examined.

Photo by Library of Congress on Unsplash

One of the main reasons why many pre-election polls give different results is that people don’t say who their favorite candidate is for various reasons. So, they either didn’t vote or said they would vote for someone else. However, on election day, when they went to the polls, they voted for their ideal candidate, who wouldn’t say his name. If most people do this, it’s clear that the poll results will change, and a statistical disaster like what happened in 2016 will happen again.

A Summary of What the Problem is!

In this article, we are not going to find out why the predictions for the 2016 elections were completely mistaken. In the future, we might publish a more in-depth article about the statistical reasons and factors that led to this. In this article, we’ll learn about a game based on math and statistics.

As was already said, one of the most important factors in predicting mistakes is the amount of wrong information that gets into the survey. Now, if we can change how we collect data to make it much more accurate, our prediction of how the election will turn out will probably be right. We’ll look at examples of this type of data tracking in the following sections.

Imaginary Forest

In a faraway forest where 200 animals live, there is an election to choose the forest leader. “Ms. Tree” and “Mr. Pig” are the two candidates in this election. “Ms. Tree” has been teaching forest residents for a long time. It thinks everyone in the forest and neighboring forests should live peacefully and work together.

On the other hand, “Mr. Pig” is a violent person who loves fighting. He thinks that a lot of the resources should be put into the military and, if possible, used to attack neighboring forests and steal their resources. A secret group that works in the forest has given us the job of finding out who will win the election before it happens. Our lives are at risk if we are wrong about what will happen!

Photo by Sergei A on Unsplash

As a result, we decided to survey every forest member to predict the election’s true winner. When we ask people who their favorite candidate is, Based on the atmosphere in the forest, the following things will happen:

  • If a person wants to vote for “Ms. Tree,” he selects her as his preferred candidate.
  • If someone wants to vote for “Mr. Pig”, they probably will give us the name of the “Ms. Tree” (We don’t know how often this happens, but we do know that it does happen sometimes.)

As a result, the most important thing for us is to get accurate data from forest people. So, we should use a strategy that lets us find out people’s real favorite candidate without making them say it out loud. In other words, we need to find a middle ground between our language and theirs so that they can answer our questions in that middle-ground language and we can understand them. While we get accurate information from a person, his or her personal opinion is kept private and does not become public. But what should be done to solve this problem?

A Coin and an Endless Amount of Possibilities

All you have to do to solve this problem is use a coin as a guide. Each coin sample space can be either “heads” or “tails.”

Photo by Steve Smith on Unsplash

As a consequence, it’s enough to go to each of the forest’s residents and, by giving him a coin, ask him to play the following game in his home (without our presence) and tell us of the outcome; the rules of the game are as follows:

  • If the coin lands on “heads,” mention the name of the candidate you want to vote for.
  • If the coin lands on “tails,” the coin is thrown, call out the name of the “Mr. Pig.”

As was already said, the biggest problem with tracking data about the election was that many people who were willing to vote for the “Mr. Pig” didn’t come out and say so for various reasons.

This strategy solves the problem since it is unknown if a person voted for “Mr. Pig” because of desire or because the coin “tails” appeared. People may safely tell us the outcome of the game since we don’t know the outcome of the tap or the line. The following was the outcome of the data collection:
  • Total number of votes: 200
  • Total Number of votes for “Mr. Pig”: 130
  • Total Number of votes for “Ms. Tree”: 70

It’s now time to use your math skills. We know that the likelihood of receiving a “heads” or a “tails” When you flip a coin is equal to 1/2 (one half). So, if we flip a coin 200 times, We can almost be sure that 100 heads and 100 tails would come up.

We have, in fact:

The second rule of the game was that if the coin came up “tails,” you had to call out the name of the “Mr. Pig.” As a result, 100 individuals have announced his name without necessarily wanting to vote for “Mr. Pig.”

As a result, we must deduct 100 votes from the 130 total votes for “Mr. Pig” since there was no power in these 100 votes, and the people declared the name “Mr. Pig” only to comply with the game’s rules.

As a result, just 30 people voted for “Mr. Pig.” This is even though the 70 votes cast for “Ms. Tree” were all valid since the game’s rules provide that anybody who receives “heads” after flipping a coin should vote for his favorite candidate.

As a result of using this rule, we will have:

  • Total Number of votes for “Mr. Pig”: 30
  • Total Number of votes for “Ms. Tree”: 70

Due to the big difference in votes between “Ms. Tree” and “Mr. Pig,” it is safe to predict that “Ms. Tree” will win on election day. We got the information we needed from them and predicted the election outcomes using this approach without violating people’s privacy.

Some might question if there is a situation in which someone wants to vote for the “Mr. Pig,” but since the “tails” side of the coin has come, his vote is effectively ignored. 

This question has these answers;

  • First, we are aiming to predict the winner of the election; it is not our objective to calculate how many votes each candidate will get, and we simply do not have the ability to do so properly.
  • Second, This situation works just as well the other way around. In that case, some people voted for “Ms. Tree,” but their votes were not counted because the “tail” side of the coin had come. So, these factors won’t have much of an effect on the outcome of our forecast and our prognosis is correct.

In the rest of this article, we’ll discuss how this approach can be used in product management.

Product Launches and Surveys

Your company just launched a new product. You are head of product management for this product, and after some time has passed, you create a survey with two answer choices to get more information and insight into user views.

To get more people to fill out the survey and fill it out, the company will give $10 to each person who responds to the survey. But if you give people this incentive, they might be more likely to choose options with positive meanings and avoid ones with negative ones.

But if people didn’t get these ten dollars, they might not participate as much and not read the questions correctly. So, a plan should be put in place that gets people more motivated and makes them more accurate. So, you decide as a product manager to make the following game.

As we’ve already said, each question’s answer has two choices: one with a positive concept load and the other with a negative concept load. Now, you put a “+” sign next to options with a positive conceptual load and a “-” sign next to options with a critical conceptual load. At the start of the survey, you ask the participant to hold a coin and answer the questions in this way;

  • Before reading the question, flip a coin.
  • If a “tails” coin is flipped, pick the positive answer (+) without reading the question.
  • If the “heads” coin shows up, read the question to the end and pick the answer that best shows your point of view.

     

You have therefore made it less likely that people will be forced to make a positive choice. You can now keep a closer eye on the survey results. For example, one of the questions that two thousand participants responded to was as follows:
  • Total participant count: 2000
  • 1300 people voted for the choice with a good connotation
  • 700 people voted for the choice with a negative and critical connotation.

We proceed as before, knowing that in two thousand coin tosses, the “tails” coin and the “heads” coin will appear about a thousand times. We have, in fact;

We removed 1,000 from the 1,300 positive votes since, according to the guidelines, if the “tails” coin shows up, folks choose the positive choice (without even reading the question). We will now have;

  • 300 people voted for the choice with a positive connotation.

     

  • 700 people voted for the choice with a negative and critical connotation.

So, as a whole, we can say that directly opposed to what was shown at the beginning, most people had a negative perception of that question, and as product managers, we need to find out why.

So, by adding a financial incentive, the number of people who took the survey climbed. On the other hand, by making this simple game, the answers became much more accurate, which means that in the future, more realistic decisions can be made based on the survey results.

Conclusion

According to the topics we discussed, one of the most significant factors to consider while running a questionnaire and collecting data is the accuracy of the data. If the data isn’t collected correctly, it’s clear that its conclusions will be misleading.

Most of the time, the following things cause survey questions to get the wrong answers:
  • The questions are about private things, and the person doesn’t feel safe answering them.
  • One of the alternatives doesn’t have a good reputation, which makes people not want to choose it because they don’t want to be judged.
  • Putting a person in a situation where he has to choose between two options
In general, it is the job of the people who creates the survey and the person who collects the data to detect such situations. To solve the problems, the following steps should be taken:
  • Creating a language between the person asking the question and the person answering it so that the person answering can confidently choose the right answer.
  • Minimizing the area for judgment and providing a secure atmosphere in which the responder may choose the preferred alternative.

By taking the above steps, the quality of data tracking will greatly improve, making it more likely that conclusions and assessments based on this data are proper.

Please feel free to share your thoughts or reading tips in the comments.

Follow me on LinkedIn, and Twitter.

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

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jalil
jalil

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