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Reading list

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 Train your Mind for Analytical Thinking

Tavish Srivastava
Tavish Srivastava Last Updated : 25 Jun, 2019
5 min read

Introductionbrain-exercise

I recently started going to the GYM. Quite a big achievement for me to be going to the gym regularly for more than a month. What always made me irregular was a lack of motivation to go to the gym every day. However, I made some key changes this time. For starters, I paid a personal trainer and tightly followed a diet schedule. In around 20 days I realized I was able to lift 3 times the weight and 1.5 times the repetitions of the same exercise. Did my muscles become thrice as strong as before? No, what my trainer played on was “the muscle memory”. Say, I lifted 10kg on a particular exercise yesterday and struggled to make 10 repetition, but somehow did it. Today, my muscle already knows I was able to complete 10 repetition on 10 kg. This time I struggle far less, because I am already prepared for what is coming.

Must Go Through:

I realized that same logic applies with the power to think. If you make calculations on daily basis, your calculations become more reflexive and accurate. An average working person in weekday spends 25-30% of his time sleeping, 40-60% of his time working , 10% of time eating and 15-25% idle. In this busy world more than 50% of our idle time is spent on road. You can use this particular time to develop sharper reflexes on numbers. This article will illustrates some engaging methods that I use in this idle time to sharpen my brain reflexes.

 

Some examples :

Driving alone to office, sitting in a cab to airport ,and travelling in trains, metro or bus are boring . I, however, engage myself in small puzzle solving which not only engages me, but also sharpens my brain reflexes. Here I will take some puzzles which I solve everyday while on the way to the office:

1. Escape cops :

By far “the most interesting one” . Everyday, I am just in time to leave for office. If the traffic is heavy, it becomes sometimes inevitable to cross signal just after it turns red. But on some blind turns, you find the traffic police waiting for the next meat. Here’s what I did to predict the number of police standing on the blind turns. I took two attributes to predict whether I will find cops or not. These attributes were :

a. Day of the week

b. Pattern of cops on previous junctions

cops

Using the above decision tree, I find a particular node, where I found almost zero probability to find a cop on blind junction. Till date, the algorithm works fantastically, but I am still figuring out better attributes to follow.

Must Read: Learn the art of structured thinking and analyzing

 

2. Time to office :

Here’s an interesting one again. It takes me 35 mins to reach office. But in case I get late for 2 mins at any particular road, I am almost able to calculate the exact time I can expect to reach the office. It’s simple but accurate. I have calculated the time it takes to cover each segment of the route and a factor in different scenarios of traffic at each segment. In total I have 7 check points at a difference of 5 mins. each in case of light traffic. Looking at the traffic in first segment gives me reasonable information to find the right multiplier for each of the segments.

time

Till date I have been able to predict the time to office within first 5 mins of drive in a confidence interval of +/- 3 mins.

 

3. How fast is the other vehicles :

This is the most addictive one. I always know my own vehicle’s speed and can judge the distance of approach of other vehicles in 10 seconds. Hence, I am able to calculate the relative velocity of other vehicle and, finally, the absolute velocity of the other vehicle.

carss

4. Sizing of services we use:

Whenever I take an auto-rickshaw, taxi or any other services, I try to calculate the total sizing of that business model and the profit individual players make in the process. I have had the most interesting conversation with the drivers, who always had some new insight on ground realities which I missed to incorporate while thinking of the business model. You can read my article on sizing problems here (https://www.analyticsvidhya.com/blog/2014/01/tips-crack-guess-estimate-case-study/). Even though the article focuses more on interview approach, you can leverage same framework to do the sizing of services on daily basis. Not only will you find it interesting but also you will improve your analytical skills.

Must Read: 5 habits of highly successful analysts

 

Potential benefits of implementing such practices:

 Three basic benefits which I have realized by implementing such practices are as follows:

1.  Power to innovate in problem statement and its solution:

To bring an out of box solution, you always need an out of the box problem statement. As an analyst, I continuously feel the need to find fact based problems which can create significant impact. We are surrounded by facts, and to search for the right facts to build up implementable solution is what it takes to be a successful analyst.

When we do a regular search of such interesting problems, our reflexes to look at imperfections sharpens. We are more capable of  to think of new business cases which can become impact full projects.

2. Sharpen the reflexes to calculate faster:

Practice makes man perfect. It does so in two ways. First, your brain tends to retain some frequent calculations. Say, 1 million * 1000 = 1 billion. You don’t need to calculate the number of zeros because it gradually becomes very intuitive. Imagine thousands of such combinations right on the tip of your head. Engaging free time to make meaningful calculations for sure makes your calculative reflexes sharper.

3. Think about the same problem in many angles and choose the most effective one:

The puzzles can be very simple, but thinking the same puzzle with different methods and then comparison of different answers not only is interesting in nature but also helps you build on your evaluative skills. We gradually start to implement the same on complex scenarios.

 

End Notes

 Most of my experiences which I shared in this article were implemented while I was not driving.Do try this practice and let us know of your exciting routine problems. Try to be innovative while defining a new problem. The more challenging is the problem more interesting will be your after thoughts.

Did you find the article useful? Share with us any other problem statements you can think of.Also share with us other techniques you use to keep your brain in its front foot.  Do let us know your thoughts about this article in the box below.

 

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Tavish Srivastava
Tavish Srivastava

Tavish Srivastava, co-founder and Chief Strategy Officer of Analytics Vidhya, is an IIT Madras graduate and a passionate data-science professional with 8+ years of diverse experience in markets including the US, India and Singapore, domains including Digital Acquisitions, Customer Servicing and Customer Management, and industry including Retail Banking, Credit Cards and Insurance. He is fascinated by the idea of artificial intelligence inspired by human intelligence and enjoys every discussion, theory or even movie related to this idea.

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Responses From Readers

Clear

solomon
solomon

This is amazing.

123

RJ
RJ

Very interesting !! an nicely written . How did you come up with Response and total values ? Was its measurement over 12 days ?

123

Tavish Srivastava
Tavish Srivastava

Thanks Rj. I assume you are talking about the cop problem.The data was measure over a period of 2 months. Also the time of the day in each event stays constant, hence , is not a sigmnificant variable in the model. Otherwise my hypothesis is that it will be very significant if the event were occuring randomly at any time of the day.

123

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