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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

The Hackathon Practice Guide by Analytics Vidhya

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Analytics Vidhya Last Updated : 26 Apr, 2018
5 min read

Introduction

A hackathon is a platform where you get the chance to apply your data science and machine learnin knowledge and techniques. It is a place where you can evaluate yourself by competing against, and learning from, fellow data science experts.

Here is an exclusive guide to help you prepare for participating in hackathons. This guide illustrates the list of important techniques which you should practice before stepping into the playing ground.

We’ll keep building this guide into a one place exhaustive resource for data science techniques and algorithms.

 

1. Framework of the Model Building Process

This is how the framework for model building works – you get data from multiple sources and then you perform the extraction and transformation operations. Once your data has been transformed, you apply your knowledge of predictive modeling and business understanding to build predictive models.

model building process

2. Hypothesis Generation

  • In your groups, list down all possible variables which might influence the independent variable (the variable to be predicted)
  • Download the dataset provided by Analytics Vidhya
  • Next, look at the dataset and see which variables are available

                                                   Make sure you always do this in this order

 

3.  Data Exploration and Feature Engineering

  • Import the data set
  • Variable identification
  • Univariate, Bivariate and Multivariate analysis
  • Identify and Treat missing and outlier values
  • Create new variables or transform existing variables

Guides:

 

Modelling Techniques

1) Logistic Regression

  • Logistic regression is a form of regression analysis in which the outcome variable is binary or dichotomous
  • Used when the focus on whether or not an event occurred, rather than when it occurred
  • Here, instead of modelling the outcome Y directly, the method models the log odds(Y) using the logistic function
  • Analysis of variance (ANOVA) and logistic regression all are special cases of General Linear Model (GLM)
  • The probability of success falls between 0 and 1 for all possible values of X

picture 1 picture 2

a) Logit Transformation

Picture3

b) Logit is directly related to Odds

  • The logistic model can be written as:

Picture4

  • This implies that the odds for success can be expressed as:

Picture5

  • This relationship is the key to interpreting the coefficients in a logistic regression model

Guides:

 

2) Decision Tree

  • Decision tree is a type of supervised learning algorithm
  • It works for both categorical and continuous input and output variables
  • It is a classification technique that split the population or sample into two or more homogeneous sets (or sub-populations) based on most significant splitter / differentiator in input variables

 

Decision Tree – Example

Picture6

Types of Decision Trees

  • Binary Variable Decision Tree: Decision Tree which has binary target variable then it called as Binary Variable Decision Tree. Example:- In above scenario of student problem, where the target variable was “Student will play cricket or not” i.e. YES or NO.
  • Continuous Variable Decision Tree: Decision Tree has continuous target variable then it is called as Continuous Variable Decision Tree.

 

Decision Tree – Terminology

 

Picture7

 

Decision Tree – Advantage and Disadvantages

Advantages:

  • Easy to understand
  • Useful in data exploration
  • Less Data Cleaning required
  • Data type is not a constraint

Disadvantages:

  • Overfit
  • Not fit for continuous variables
  • Not Sensitive to Skewed distributions

Guides:

 

 

3) Random Forest

  • “Random Forest“ is an algorithm to perform very intensive calculations.
  • Random forest is like a bootstrapping algorithm with Decision tree (CART) model.
  • Random forest gives much more accurate predictions when compared to simple CART/CHAID or regression models in many scenarios.
  • It captures the variance of several input variables at the same time and enables high number of observations to participate in the prediction.
  • A different subset of the training data and subset of variables are selected for each tree
  • Remaining training data are used to estimate error and variable importance

 

Random Forest – Advantages and Disadvantages

Advantages:

  • No need for pruning trees
  • Accuracy and variable importance generated automatically
  • Not very sensitive to outliers in training data
  • Easy to set parameters

Disadvantages:

  • Over fitting is not a problem
  • It is black box, rules behind model building can not be explained

Guides:

 

4) Support Vector Machine(SVM)

  • It is a classification technique.
  • Support Vectors are simply the coordinates of individual observation
  • Support Vector Machine is a frontier which best segregates the one class from other
  • Solving SVMs is a quadratic programming problem
  • Seen by many as the most successful current text classification method

 

Case Study 1

We have a population of 50% males and 50% females. Here, we want to create some set of rules which will guide the gender class for the rest of the population.Picture8

The blue circles in the plot represent females and the green squares represents male.

Males in our population have a higher average height.

Females in our population have longer scalp hairs.

 

Case Study 2

picture 10

 

Guides:

 

Text Mining:

Text mining is the analysis of data contained in natural language text. Text mining works by transposing words and phrases of unstructured data into numerical values which can then be linked with structured data in a database and analyzed with traditional data mining techniques.

Guides:

 

End Notes

In this guide we talked about various modelling techniques, text analytics and the various stages which are necessary for a perfect model building.

If you like what you just read & want to continue your analytics learning, subscribe to our emails, follow us on twitter or like our facebook page.

 

decision treefeature engineeringhypothesis generationlogistic regressionrandom forestregressionsvmd
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Responses From Readers

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Stella James
Stella James

Thanks for your article. I appreciate the fact that you are telling us the advantages and the disadvantages of random forest and the decision tree. It is very helpful especially when you need to take a decision.

123

RAKESH KUMAR
RAKESH KUMAR

Thanks Manish, You not only keep yourself busy with the knowledge sharing on Analytics Vidhya, but also keep motivate others to follow you. Kudos!! to the team.... for the kind of help & support you provide... Regards, Rakesh

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