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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

Drawdata – Draw A Customized Data Using Python

Mayur
Mayur Last Updated : 28 Apr, 2021
4 min read
This article was published as a part of the Data Science Blogathon.

Overview

  • Introduction
  • Need of data in machine learning
  • Introduction to Drawdata
  • Installation
  • Importing important modules
  • Draw varieties of plots
  • End Notes

Introduction

Drawdata

“ Data is the new oil ”

   — Clive Humby

When we say data is everything, will it? if you belonging to the technical field then you definitely said “yes”, because if there is a river then there is always find water.  Worlds need more data to perform fundamental tasks. Let’s take a brief intro on data:

Data can be defined as the units of information, either it was numeric or collected from any type of observation. in simple words, we can say that the collection of facts, numbers, measurements, or observations can refer as data. Most of us are confused about data and information, let’s solve this data is an individual unit but the information is the collection or group of data.

We are belonging to the data science community and we definitely knew the meaning of what Machine Learning is,  But you know why there is a need for data to create a dataset for training ML models. If not then will discuss-

 

Need of data in machine learning

We know that machine learning data analysis uses algorithms to continuously improve itself over time, but it is quite important that the quality of data is also necessary for performing algorithms on ML models for better accuracy.

” You just need to understand the data to truly understand how machine learning works “

For building the machine learning model we need a collection of data, for that we create a group of data called a dataset. Datasets are the collection of cases that all share a common attribute. Creating the ML models helps to understand between data points.

In any data science project life cycle, you probably notice EDA, feature selection, model building but you unnoticed how data will create, we generally used a dataset that is already created.  we do not probably want to use so many complex datasets for machine learning models, rather then we want to use a simple dataset. Have you have any knowledge on how data is generated, if not then don’t worry we will discuss below:

Drawdata

This is a beautiful open-source python library that allows generating data by just drawing it, In simple words, we can say that we only have to draw data, and then it will generate automatically. It is very easy to use and also has a user-friendly environment. It allows users to create varieties of datasets which have different shapes and sizes which is used for machine learning model.

This library is only used in the jupyter notebook, so let’s discuss the techniques of Drawdata here below:

Drawdata let's go

Using Drawdata library into jupyter you need to install it first:

Installation

We install two libraries drawdata and pandas, you only need to execute the following code in the command prompt:

pip install drawdata
pip install pandas
Drawdata installation

Importing important modules

Import pandas as pd
form drawdata import draw_scatter
from drawdata import draw_line
from drawdata import draw_histogram

After importing the library we will now take understanding how data is being drawn:

Draw varieties of plots

1. Scatter draw

scatter_plot = draw_scatter()
scatter_plot
scatter draw Drawdata

In this Gif, you can see that there are certain options at the top which is used to draw the scatter pattern into the draw section.

2. Line draw

line_plot = draw_line()
line_plot
line draw

As here we draw lines of different colors to represent the variety of data into the dataset.

3. Histogram draw

hist_draw = draw_histogram()
hist_draw
Histogram draw

In the above image, there is a variety of data in the form of a histogram, by this, we can draw fake data to visualize histogram.

This is the way to create different datasets using the library drawdata, but how we save this data into a dataframe to create a machine learning model. see below:

You have to click on  the above options which formate you wish to store data, see below:

format

After this, you can use pandas to read the clipboard to get your drawn data into a dataframe.

df = pd.read_clipboard(sep=",")
df
clipboard

So, this is the resultant dataset that we have created using the drawdata library.

Endnote

This article was suggested by my friend, it was pretty much an amazing library that I have an interest in. Now it’s time to build your own dataset by drawing.

Thank You.

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

blogathondrawdata
Mayur
Mayur

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