Context

This is a Covid 19 data set for India. The data set is updated frequently and is analysed using tableau. Click on the link to visit the tableau story. Click each of the caption in the story to unveil its content.

https://public.tableau.com/profile/ambili.nair#!/vizhome/COVID19Indiastory/Indiastory?publish=yes

The first Covid 19 case in India was reported on 30th January 2020 in South Indian state of Kerala on a medical student who was pursuing the studies at Wuhan University, China. Two more students were found to be infected in Kerala in the consecutive days. The Kerala government was successful in containing the disease with its proactive measures back then. The second outbreak of Covid 19 in India started in the first week of March from various parts of India in various people who visited the foreign countries and in some of the tourists from different countries.

The tableau story consists of the following data analysis : 1. State-wise number of infected and number of death count in India map. Hover the mouse on each state in the India map to know the count. 2. Click on the next caption to know the state-wise number of confirmed, active, recovered and deceased cases in the form of bar chart. 3. The next caption takes you to the bar chart which shows the number of cases getting confirmed in India each day starting from January 30, 2020. 4. Next caption takes us to an analysis of the Mortality rate and the Recovery rate (in percentage) of each of the Indian state. We get an idea how hard each of the state is hit by the pandemic. 5. Next caption gives a detailed analysis of the state Kerala which has the mortality rate of 0.806% and the recovery rate of 74.4% as of now. Hover the mouse to know the count in each district. Don't forget to have a look at the line graph of 'number of active cases' in Kerala. It looks almost flattened ! As everyday we hear the increasing number of cases and deaths across the country, this graph may make you feel better...! 6. Finally the caption takes you to the statistics from the topmost district of Kerala - Kasaragod. The total number of cases reported is 179 at Kasaragod. The active number of cases is just 12 as of now... !!! Have a look at the statistics from Kasaragod and the story of 'Kasaragod model' as some of the national media in India call it !!!

Content

This data set consists of the following data: 1. state-wise statistics - Confirmed, Active, Recovered, Deceased cases 2. day-wise count of infected and deceased from various states 3. Statistics from Kerala - day-wise count of confirmed, Active, Recovered, Deceased cases 4. Statistics from Kasaragod district, Kerala - day-wise count of confirmed, Active, Recovered, Deceased cases 5. Count of confirmed cases from various districts of India

Acknowledgements

Ministry of Health and Family Welfare - India covid19india.org Wikipedia page - Covid 19 Pandemic India Govt. of Kerala dashboard - official Kerala Covid 19 statistics

Inspiration

Inspiration

Your data will be in front of the world's largest data science community. What questions do you want to see answered?

Number and percentage of deaths, by month and place of residence, 1991 to most recent year.

Vital Statistics: Death Rate: per 1000 Population: Andhra Pradesh: Urban data was reported at 4.900 NA in 2020. This records an increase from the previous number of 4.800 NA for 2019. Vital Statistics: Death Rate: per 1000 Population: Andhra Pradesh: Urban data is updated yearly, averaging 5.400 NA from Dec 1997 (Median) to 2020, with 23 observations. The data reached an all-time high of 6.100 NA in 1998 and a record low of 4.800 NA in 2019. Vital Statistics: Death Rate: per 1000 Population: Andhra Pradesh: Urban data remains active status in CEIC and is reported by Office of the Registrar General & Census Commissioner, India. The data is categorized under India Premium Database’s Demographic – Table IN.GAH003: Vital Statistics: Death Rate: by States.

India marks one COVID-19 death every 5 minutes

https://ichef.bbci.co.uk/news/976/cpsprodpb/11C98/production/_118165827_gettyimages-1232465340.jpg" alt="">

Content

People across India scrambled for life-saving oxygen supplies on Friday and patients lay dying outside hospitals as the capital recorded the equivalent of one death from COVID-19 every five minutes.

For the second day running, the country’s overnight infection total was higher than ever recorded anywhere in the world since the pandemic began last year, at 332,730.

India’s second wave has hit with such ferocity that hospitals are running out of oxygen, beds, and anti-viral drugs. Many patients have been turned away because there was no space for them, doctors in Delhi said.

https://s.yimg.com/ny/api/res/1.2/XhVWo4SOloJoXaQLrxxUIQ--/YXBwaWQ9aGlnaGxhbmRlcjt3PTk2MA--/https://s.yimg.com/os/creatr-uploaded-images/2021-04/8aa568f0-a3e0-11eb-8ff6-6b9a188e374a" alt="">

Mass cremations have been taking place as the crematoriums have run out of space. Ambulance sirens sounded throughout the day in the deserted streets of the capital, one of India’s worst-hit cities, where a lockdown is in place to try and stem the transmission of the virus. source

Dataset

The dataset consists of the tweets made with the #IndiaWantsOxygen hashtag covering the tweets from the past week. The dataset totally consists of 25,440 tweets and will be updated on a daily basis.

The description of the features is given below | No |Columns | Descriptions | | -- | -- | -- | | 1 | user_name | The name of the user, as they’ve defined it. | | 2 | user_location | The user-defined location for this account’s profile. | | 3 | user_description | The user-defined UTF-8 string describing their account. | | 4 | user_created | Time and date, when the account was created. | | 5 | user_followers | The number of followers an account currently has. | | 6 | user_friends | The number of friends an account currently has. | | 7 | user_favourites | The number of favorites an account currently has | | 8 | user_verified | When true, indicates that the user has a verified account | | 9 | date | UTC time and date when the Tweet was created | | 10 | text | The actual UTF-8 text of the Tweet | | 11 | hashtags | All the other hashtags posted in the tweet along with #IndiaWantsOxygen | | 12 | source | Utility used to post the Tweet, Tweets from the Twitter website have a source value - web | | 13 | is_retweet | Indicates whether this Tweet has been Retweeted by the authenticating user. |

Acknowledgements

https://globalnews.ca/news/7785122/india-covid-19-hospitals-record/ Image courtesy: BBC and Reuters

Inspiration

The past few days have been really depressing after seeing these incidents. These tweets are the voice of the indians requesting help and people all over the globe asking their own countries to support India by providing oxygen tanks.

And I strongly believe that this is not just some data, but the pure emotions of people and their call for help. And I hope we as data scientists could contribute on this front by providing valuable information and insights.

This dataset provides values for CORONAVIRUS DEATHS reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

Context

This dataset contains information on suicides which happened in India during 2015.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F4208638%2Ffab2e99b439f9780daf358511060f514%2FWorld-Suicide-Prevention-Day.jpg?generation=1598114750200382&alt=media" alt="">

The singular age-old social precept of 'Lok Kya Kahenge?' (loosely translated: "What will people say?") suppresses the much-needed psychological care in India. It's high time that we understand why suicides happen and what are the reasons behind it. This dataset aims to spread awareness about suicides in India.

Content

I acquired this dataset from here. Have a look at the website.

This dataset contains 9 files in .csv format. You can find a description for each column. Let me summarize it here as well.

1. Cause-wise distribution of suicides in Central Armed Police Force (CAPF) during 2015.
2. Economic Status-wise distribution of suicides during 2015.
3. Educational Status-wise distribution of suicides during 2015.
4. Farmer or Cultivators distribution of suicides during 2015.
5. Profession-wise distribution of suicides during 2015.
6. Social status-wise distribution of suicides during 2015.
7. Cause-wise distribution of suicides during 2015.
8. Suicides by Agricultural labourers during 2015.
9. Suicides by means adopted during 2015.

Inspiration

We now have plenty of data to explore to draw some conclusions about suicides which happened in India during 2015. Let's start by answering these questions: - What are the top 5 states where Farmers' suicides occurred the most? - What's the top reason that agricultural labourers committed suicide? - Which Profession has the most suicides? What could be the reason? - How many Transgender suicides have occurred in different categories?

I hope these questions interest you in starting to explore this dataset.

Acknowledgements

I thank the Indian Government for making it public under their Open Government Data (OGD) Platform India. Please use this dataset strictly for educational purposes. Thank you.

In 2022, the number of deaths due to road accidents in India among victims between 25 to 35 years amounted to nearly **** thousand, the most compared to other age groups. That year, there were over 169 thousand accidental fatalities across the south Asian country. Over-speeding was the leading contributor of accidents. Combined, state and national highways recorded around 258 thousand road accidents in 2022. This number had dropped significantly in 2016, before increasing again in recent years.

Accident demographics

The Indian subcontinent ranked first in terms of road accident deaths according to the World Road Statistics which comprised of *** countries. A majority of victims were two-wheeler commuters. Additionally, pedestrians made up a high share of victims as well, reflecting the lack of infrastructure, be it improper footpaths and the lack of foot-over bridges or negligence of traffic rules. About ** percent of the road accidents in India accounted for about *** percent of the global road traffic accidents.

Accident prevention

Poor enforcement of fines, in addition to mild punishments and corruption encourages drivers, especially among young Indians, to engage in rash driving. Accident awareness programs were initiated by the government among the motorists, along with the National Road Safety Policy to encourage safe transport, strict enforcement of safety laws and fines and establishment of road safety database.

Conflict and disaster population movement (flows) data for India.

The IDU (Internal Displacement Updates) dataset, provided by the Internal Displacement Monitoring Centre (IDMC), offers timely event data and provisional information on new internal displacements caused by conflicts and disasters. Representing the most recent available information over a 180-day time period, the IDU is updated daily and focuses on "flows" (new displacements).

Internally displaced persons (IDPs) are defined according to the 1998 Guiding Principles as people or groups of people who have been forced or obliged to flee or to leave their homes or places of habitual residence, in particular as a result of armed conflict, or to avoid the effects of armed conflict, situations of generalized violence, violations of human rights, or natural or human-made disasters and who have not crossed an international border. The IDMC's event data, sourced from the IDU, provides initial assessments of these internal displacements, reflecting continually updated provisional information from various sources.

While the IDU offers early insights, the more thoroughly validated and curated "stock" (Total number of people leaving on internal displacement) and "flow" (population movements) estimates are available in the annual Global Internal Displacement Database (GIDD). Both datasets are accessible via API, with specific guidance on data access, structure, and limitations, including important preprocessing considerations for the IDU to ensure accurate analysis and avoid double-counting. For further detailed information and complete API specifications, users are encouraged to consult the official documentation at https://www.internal-displacement.org/database/api-documentation/.

The IDMC's Event data, sourced from the Internal Displacement Updates (IDU), offers initial assessments of internal displacements reported within the last 180 days. This dataset provides provisional information that is continually updated on a daily basis, reflecting the availability of data on new displacements arising from conflicts and disasters. The finalized, carefully curated, and validated estimates are then made accessible through the Global Internal Displacement Database (GIDD). The IDU dataset comprises preliminary estimates aggregated from various publishers or sources.

Over *** thousand deaths due to suicides were recorded in India in 2022. Furthermore, majority of suicides were reported in the state of Tamil Nadu, followed by Rajasthan. The number of suicides that year had increased from the previous year. Some of the causes for suicides in the country were due to professional problems, abuse, violence, family problems, financial loss, sense of isolation and mental disorders. Depressive disorders and suicide As of 2015, over ****** million people worldwide suffered from some kind of depressive disorder. Furthermore, over ** percent of the total population in India suffer from different forms of mental disorders as of 2017. There exists a positive correlation between the number of suicide mortality rates and people with select mental disorders as opposed to those without. Risk factors for mental disorders Every ******* person in India suffers from some form of mental disorder. Today, depressive disorders are regarded as the leading contributor not only to disease burden and morbidity worldwide, but even suicide if not addressed. In 2022, the leading cause for suicide deaths in India was due to family problems. The second leading cause was due to illness. Some of the risk factors, relative to developing mental disorders including depressive and anxiety disorders, include bullying victimization, poverty, unemployment, childhood sexual abuse and intimate partner violence.

The dataset contains year-, region- and fractile-class-wise compiled All India data on number of persons (per thousand) among the households with different percentage levels of calorie consumption. The dataset presents the data by division of households by the level of their income (mpce fractile-class) and by percentage of calorie intake such as 70 percent, 80 percent, etc., to the actual standard requirement of 2700 kilocalories every day. Among these households with different percentages of calorie intake, the dataset presents the per thousand distribution of persons. The dataset has been compiled from Table No. 5R, 7R, 7A-R and 7-A of NSS 55th, 61st, 66th and 68th reports, respectively, from the year 2000 to 2012.

Data collected between 2014 and 2016 from self-identified lesbian, gay, bisexual, trans and queer (LGBTQ) individuals in India and the UK. This data was collected at specific workshops held in India and the UK, and via the project's website (see Related Resources). The study used a 7 phase mixed methods design: 1. Project planning and research design, including formally establishing the advisory group and meeting 1, setting milestones and setting in place all agreements/ethical approvals 2. Literature review exploring key measures used to rate and assess LGBTQ 'friendliness'/inclusion nationally, supra-nationally and internationally 3. A spatial assessment of LGBTQ liveabilities that includes, but moves beyond, the measures identified in phase 2, applying these at a local scale e.g. policy indicators and place based cultural indicators 4. Twenty focus groups (80 participants, sample targeting marginalised LGBTQ people), coupled with online qualitative questionnaires (150), and shorter SMS text questionnaires (200)/App responses (200) to identify add to the liveability index created in phase 3 and what makes life un/liveable for a range of LGBTQ people and how this varies spatially 5. Participants in the data collection will be invited to reconfigure place through UK/India street theatre performances. These will be video recorded, edited into one short video and widely distributed. Data will be collected by observing interactions; on the spot audience surveys; reflections on the event 6. The research will analyse the data sets as they are collected. At the end of the data collection phase time will be taken to look across all 4 data sets to create a liveability index 7. Research dissemination will be targeted at community and academic audiences, including end of project conferences in India/UK, collating policy/community reports, academic outputs. The impact plan details the short (transnational support systems; empowerment of participants), medium (policy changes, inform practice) and long-term (changing perceptions of LGBTQ people) social impacts and how these will be achieved.The main research objective is to move beyond exclusion/inclusion of Lesbian, Gay, Bisexual, Trans, Queer (LGBTQ) communities in UK and India creating a liveability model that can be adapted globally. Whilst work has been done to explore the implications of Equalities legislation, including contesting the normalisations of neo-liberalisms, there has yet to be an investigation into what might make every day spaces liveable for LGBTQ people. This project addresses social exclusion, not only through identifying exclusions, but also by exploring how life might become liveable in everyday places in two very different contexts. In 2013 the Marriage (Same Sex) Act passed in the UK, and in India the Delhi High Court's reading down Indian Penal Code 377 in 2009 to decriminalize sexual acts between consenting same-sex people was overturned by the Supreme Court. Yet bullying, mental health and safety continue to be crucial to understanding British LGBTQ lives, in contrast the overturned the revoke of Penal Code 377 2013, this has resulted in increased visibilities of LGBTQ people. These different contexts are used to explore liveable lives as more than lives that are just 'bearable' and moves beyond norms of happiness and wellbeing. This research refuses to be fixed to understanding social liberations through the exclusion/inclusion, in place/out of place dichotomies. Using commonplace to move beyond 'in place' towards being common to the place itself. Place can then be shared in common as well as collectively made in ways that do not necessarily impose normative agendas/regulatory conditionalities. Social liberations are examined in the transformation of everyday encounters without conforming to hegemonies or making 'normal' our own. Whilst the focus is sexual and gender liberations, the project will enable considerations of others social differences. It will show how places produce differential liveabilities both where legislative change has been achieved and where it has just been repealed. Thus, the project offers academic and policy insights into safety, difference and vibrant and fair societies. Mixed-methods data generation via: a) Project workshops in the UK (including free writing; collage-making; timeline creation; local, national and global mapmaking; recorded individual interviews; recorded group discussions). b) Project workshops in India (including free writing; collage-making; timeline creation; individual written questionnaires; recorded group discussions). c) Individual In-Depth Interviews (IDIs) in India. d) Online surveys for registered members of Liveable Lives website. e) Bulletin board discussions for registered members of Liveable Lives website.

Introduction

Welcome to the Hindi General Conversation Speech Dataset — a rich, linguistically diverse corpus purpose-built to accelerate the development of Hindi speech technologies. This dataset is designed to train and fine-tune ASR systems, spoken language understanding models, and generative voice AI tailored to real-world Hindi communication.

Curated by FutureBeeAI, this 30 hours dataset offers unscripted, spontaneous two-speaker conversations across a wide array of real-life topics. It enables researchers, AI developers, and voice-first product teams to build robust, production-grade Hindi speech models that understand and respond to authentic Indian accents and dialects.

Speech Data

The dataset comprises 30 hours of high-quality audio, featuring natural, free-flowing dialogue between native speakers of Hindi. These sessions range from informal daily talks to deeper, topic-specific discussions, ensuring variability and context richness for diverse use cases.

Topic Diversity

The dataset spans a wide variety of everyday and domain-relevant themes. This topic diversity ensures the resulting models are adaptable to broad speech contexts.

Transcription

Each audio file is paired with a human-verified, verbatim transcription available in JSON format.

These transcriptions are production-ready, enabling seamless integration into ASR model pipelines or conversational AI workflows.

Metadata

The dataset comes with granular metadata for both speakers and recordings:

Such metadata helps developers fine-tune model training and supports use-case-specific filtering or demographic analysis.

Usage and Applications

This dataset is a versatile resource for multiple Hindi speech and language AI applications:

Analysis of ‘COVID-19 India Time Series’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/ravichaubey1506/covid19-india on 28 January 2022.

--- Dataset description provided by original source is as follows ---

Context

Coronavirus disease (COVID-19) is an infectious disease caused by a newly discovered coronavirus. Most people who fall sick with COVID-19 will experience mild to moderate symptoms and recover without special treatment.

Content

COVID-19 cases at daily level is present in covid_time_series.csv COVID-19 cases for different states till 1 may 2020 is present in covid_india_states.csv

Acknowledgements

Thanks to Indian Ministry of Health & Family Welfare for making the data available to general public.

Thanks to covid19india.org for making the individual level details and testing details available to general public.

Thanks to Wikipedia for population information.

Inspiration

Forecast for next 15 days and some EDA on Spread of Corona Virus

--- Original source retains full ownership of the source dataset ---

🔗 Check out my notebook here: Link

This dataset includes malnutrition indicators and some of the features that might impact malnutrition. The detailed description of the dataset is given below:

• Percentage-of-underweight-children-data: Percentage of children aged 5 years or below who are underweight by country.

• Prevalence of Underweight among Female Adults (Age Standardized Estimate): Percentage of female adults whos BMI is less than 18.

• GDP per capita (constant 2015 US$): GDP per capita is gross domestic product divided by midyear population. GDP is the sum of gross value added by all resident producers in the economy plus any product taxes and minus any subsidies not included in the value of the products. It is calculated without making deductions for depreciation of fabricated assets or for depletion and degradation of natural resources. Data are in constant 2015 U.S. dollars.

• Domestic general government health expenditure (% of GDP): Public expenditure on health from domestic sources as a share of the economy as measured by GDP.

• Maternal mortality ratio (modeled estimate, per 100,000 live births): Maternal mortality ratio is the number of women who die from pregnancy-related causes while pregnant or within 42 days of pregnancy termination per 100,000 live births. The data are estimated with a regression model using information on the proportion of maternal deaths among non-AIDS deaths in women ages 15-49, fertility, birth attendants, and GDP measured using purchasing power parities (PPPs).

• Mean-age-at-first-birth-of-women-aged-20-50-data: Average age at which women of age 20-50 years have their first child.

• School enrollment, secondary, female (% gross): Gross enrollment ratio is the ratio of total enrollment, regardless of age, to the population of the age group that officially corresponds to the level of education shown. Secondary education completes the provision of basic education that began at the primary level, and aims at laying the foundations for lifelong learning and human development, by offering more subject- or skill-oriented instruction using more specialized teachers.

Unemployment Rate in India remained unchanged at 5.60 percent in June. This dataset provides - India Unemployment Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Globalization started in india in 1991 is done. Today we are in the age of globalization living. Globalization means market access, trade growth, agricultural development, job creation means the country fulfilling the objective of accelerating overall development is a new experiment made by the world. Of india from the very beginning, the experiment of globalization in context opposition, as well as support from many intellectuals, is great is done in proportion. Globalization in india today accepted for more than 25 years. Favorable for india as well as in some areas the opposite effect can be seen in globalizationdue to this industry and trade sector in the country, you see the prosperity. Here is the information the field of technology has developed a lot and at present, this sector is also ahead in terms of employing the agricultural sector in the country was ahead. She is currently considering employment in the country has been declining since 2014 and as a result, adverse effects on poverty in the country are living. Poverty is an issue related to purchasing power and the purchasing power of people in the country decreases every day. This is happening because the high level of employment in the country is declining.

Crude Oil rose to 70.07 USD/Bbl on July 31, 2025, up 0.09% from the previous day. Over the past month, Crude Oil's price has risen 7.05%, but it is still 8.18% lower than a year ago, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. Crude Oil - values, historical data, forecasts and news - updated on July of 2025.

Analysis of ‘Agriculture Crop Production In India’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/srinivas1/agricuture-crops-production-in-india on 28 January 2022.

--- Dataset description provided by original source is as follows ---

Context

Agricuture Production in India from 2001-2014

Content

This Dataset Describes the Agricuture Crops Cultivation/Production in india. This is from https://data.gov.in/ fully Licensed

Acknowledgements

This Dataset can solves the problems of various crops Cultivation/production in india.

Columns

crop:string, crop name Variety:string,crop subsidary name state: string,Crops Cultivation/production Place Quantity:Integer,no of Quintals/Hectars production:Integer,no of years Production Season:DateTime,medium(no of days),long(no of days) Unit:String , Tons Cost:Integer, cost of cutivation and Production Recommended Zone:String ,place(State,Mandal,Village)

Inspiration

Across The Globe India Is The Second Largest Country having People more than 1.3 Billion. Many People Are Dependent On The Agricuture And it is the Main Resource. In Agricuturce Cultivation/Production Having More Problems. I want to solve the Big problem in india and usefull to many more people

--- Original source retains full ownership of the source dataset ---

Analysis of ‘Education in India’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/rajanand/education-in-india on 28 January 2022.

--- Dataset description provided by original source is as follows ---

Would you like to learn SQL to analyze datasets like this? Get your FREE pdf copy of daily SQL learning plan.

Context

When India got independence from British in 1947 the literacy rate was 12.2% and as per the recent census 2011 it is 74.0%. Although it looks an accomplishment, still many people are there without access to education.

It would be interesting to know the current status of the Indian education system.

Content

This dataset contains district and state wise Indian primary and secondary school education data for 2015-16.

Granularity: Annual

List of files:

1. 2015_16_Districtwise.csv ( 680 observations and 819 variables )
2. 2015_16_Statewise_Elementary.csv ( 36 observations and 816 variables )
3. 2015_16_Statewise_Secondary.csv ( 36 observations and 630 variables )

Acknowledgements

Ministry of Human Resource Development (DISE) has shared the dataset here and also published some reports.

Source of Banner image.

Inspiration

This dataset provides the complete information about primary and secondary education. There are many inferences can be made from this dataset. There are few things I would like to understand from this dataset.

1. Drop out ratio in primary and secondary education. (Govt. has made law that every child under age 14 should get free compulsary education.)
2. Various factors affecting examination results of the students.
3. What are all the factors that makes the difference (in literacy rate) between Kerala and Bihar?
4. What could be done to improve the female literacy rate and literacy rate in rural area?

Become a SQL developer in 8 weeks.

Would you like to learn SQL to analyze datasets like this? Get your FREE pdf copy of daily SQL learning plan.

If you have any question, you may contact me via an email or LinkedIn message.

--- Original source retains full ownership of the source dataset ---

Introduction

Welcome to the Kannada Language In-car Speech Dataset, a comprehensive collection of audio recordings designed to facilitate the development of speech recognition models specifically tailored for in-car environments. This dataset aims to support research and innovation in automotive speech technology, enabling seamless and robust voice interactions within vehicles for drivers and co-passengers.

Speech Data

This dataset comprises over 5,000 high-quality audio recordings collected from various in-car environments. These recordings include scripted wake words and command-type prompts.

Dataset Diversity

Apart from participant diversity, the dataset is diverse in terms of different wake words, voice commands, and recording environments.

Metadata

The dataset provides comprehensive metadata for each audio recording and participant: