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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

Exploring Suicide Data in India with Python Code

A
Alifia Ghantiwala Last Updated : 15 Mar, 2021
4 min read
This article was published as a part of the Data Science Blogathon.

Introduction

The key step in solving any machine learning problem is knowing the underlying data. You need to know what to do more than how to do, isn’t it? Data exploration includes visualizing your data, cleaning it, more often than not helps you to make an initial level strategy for the problem at hand.

In this article, we would be exploring suicides in India. We would be using Matplotlib and Seaborn libraries to graphically represent the data and note down our observations.

Order of content:-

1) About the data

2) Data preprocessing

3) Data exploration and visualization

 

About the data

The dataset for this assignment is available on Kaggle

Context This data set contains yearly suicide details of all the states/u.t of India by various parameters from 2001 to 2012.

Parameters:

a) Suicide causes b) Education status c) By means adopted d) Professional profile e) Social status

 

Data preprocessing

After importing standard libraries and loading the data in a pandas DataFrame, we have a look at the data

suicide data head

The data does not contain any NULL values.

Suicie data NULL values

As part of data cleaning, we replace some states which are mentioned separately in the dataset like A & N Islands (Ut), A & N Islands, as both these entries are for Andaman and Nicobar Islands and there is no point in having separate entries for them. Similarly, other multiple entries for states and type codes have been replaced as in the below screenshot.

Suicide data cleaning

 

Data exploration and visualization 

A quick look at the gender-wise distribution of people committing suicide tells us that we have more males committing suicide than women.

Suicide Data exploration and visualization 

Next, we look at the state-wise suicide numbers to find out that the states with the highest number of suicides are Maharashtra, West Bengal, Andhra Pradesh, Tamil Nadu, and Karnataka.

Groupby code
groupby solution

We see a gradual increase in the number of suicides every year, however,  in 2012 we have a dip in the numbers as compared to 2011.

suicide data barplot
barplot output

Exploring the type code feature gives us the below graph

type code

The type code gives us information on the basis of

a) Suicide causes b) Education status c) By means adopted d) Professional profile e) Social status

Let’s look at each of them one by one.

 

Social Status

We have 3349 people in the data who have their social status mentioned out of which 70.2%(highest) suicides are of people who were married and 1%(lowest) suicides of divorcees.

Social Status

On visualizing the professional profile type code we see the following results.

Out of the 17782 people whose profession is known to us through the data housewives, people involved in farming and agricultural activities, private service employees are the majority of people to suicide.

housewives, people involved in farming and agricultural activities, private service employees are the majority of people to suicide

We next look at the means that are adopted by people to commit suicide.

means that are adopted

 

Observation

Hanging, Consuming Insecticides and poison, self-immolation are the top means adopted by people who suicide.

Looking at the education type code, a trend that can be observed is with higher education there is a fall in the number of suicides.

Observation

Now let us understand the top causes of people who suicide by looking at the causes type code.

causes type code

The top causes of suicide that data reveals are-

1) Family Problems 2) Prolonged Illness 3) Mental Illness 4) Love affairs 5) Bankruptcy or sudden change in economic status.

I personally enjoyed this visualization and exploration assignment on a real-world dataset, let me know if you did too in the comments. The full code for my work can be found here.

A tiny bit about myself:-

I am Alifia Ghantiwala, a computer engineer who likes finding insights from data. Currently, I work as a Software Engineer with BNP Paribas India Solutions, you can connect with me on LinkedIn

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

A
Alifia Ghantiwala

I investigate data on a daily basis to find insights! I write so that I can understand more clearly. Have completed my graduation in Computer Engineering, and won my first public data science competition last month, March 2021, hosted on Kaggle by Google Developers.

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