Dataset Card for Dataset Name

Includes Images for different Indian Cities.

  Dataset Details

Each city has 2500 images

  Dataset Description

This dataset contains 2500 images per Cities of popular indian Cities, City included are Ahmendabad, Mumbai, Delhi, Koklakta and A state Kerala.

Curated by: Divax Shah and Team

  Dataset Sources

Google

Demo: here

arXiv : https://arxiv.org/abs/2403.10912

Context

I was looking for a dataset that will help me to map all the Major Indian Cities using Geopandas but I couldn't find non. This dataset help me to achieve what I was looking for. This data can be used for choropleth map, foilage map using Geopandas. There was only state value(lat & long), which I found in existing datasets. So I found this dataset.

Content

This contains all the Major Cities and their respective Latitude and Longitude Values along with the rounded-off population and the exact population

Acknowledgements

Thanks to Simple Maps for making all this data available in one place, you can find the original dataset here:- https://simplemaps.com/data/in-cities

Inspiration

You can use this dataset for plotting various features about the Major Indian Cities with the help of Geopandas.

The "Indian IT Cities Used Car Dataset 2023" is a comprehensive collection of data that offers valuable insights into the used car market across major Information Technology (IT) cities in India. This dataset provides a wealth of information on a wide range of used car listings, encompassing details such as car models, variants, pricing, fuel types, dealer locations, warranty information, colors, kilometers driven, body styles, transmission types, ownership history, manufacture dates, model years, dealer names, CNG kit availability, and quality scores.

Researchers, data enthusiasts, and industry professionals can leverage this dataset for in-depth analysis, market research, and predictive modeling within the Indian used car sector, focusing on the unique dynamics of IT-driven cities. With data sourced from the year 2023, this dataset is a valuable resource for anyone seeking to explore the nuances of the used car market within the thriving IT hubs of India.

This dataset is about countries per year in India. It has 64 rows. It features 4 columns: country, capital city, and urban land area.

About this dataset

Context

The dataset tabulates the median household income in Indian Village. It can be utilized to understand the trend in median household income and to analyze the income distribution in Indian Village by household type, size, and across various income brackets.

Content

The dataset will have the following datasets when applicable

Please note: The 2020 1-Year ACS estimates data was not reported by the Census Bureau due to the impact on survey collection and analysis caused by COVID-19. Consequently, median household income data for 2020 is unavailable for large cities (population 65,000 and above).

• Indian Village, IN Median Household Income Trends (2010-2021, in 2022 inflation-adjusted dollars)
• Median Household Income Variation by Family Size in Indian Village, IN: Comparative analysis across 7 household sizes
• Income Distribution by Quintile: Mean Household Income in Indian Village, IN
• Indian Village, IN households by income brackets: family, non-family, and total, in 2022 inflation-adjusted dollars

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Interested in deeper insights and visual analysis?

Explore our comprehensive data analysis and visual representations for a deeper understanding of Indian Village median household income. You can refer the same here

The dataset contains air quality information for various cities across India. It includes parameters such as Air Quality Index (AQI), concentrations of particulate matter (PM2.5 and PM10), ozone (O3), nitrogen dioxide (NO2), sulfur dioxide (SO2), carbon monoxide (CO), as well as geographical coordinates and time stamps. This dataset enables analysis and comparison of air quality levels among different cities, aiding in understanding environmental health impacts and informing policy decisions.

This dataset is about countries per year in India. It has 64 rows. It features 4 columns: country, capital city, and death rate.

The table provides the figures for capital outlay for the different states in Rs.Billion from 1980-81 to 2016-17(B.E.)

About this dataset

Context

The dataset tabulates the population of Columbia City by race. It includes the population of Columbia City across racial categories (excluding ethnicity) as identified by the Census Bureau. The dataset can be utilized to understand the population distribution of Columbia City across relevant racial categories.

Key observations

The percent distribution of Columbia City population by race (across all racial categories recognized by the U.S. Census Bureau): 92.54% are white, 0.39% are Black or African American, 0.37% are American Indian and Alaska Native, 1% are some other race and 5.70% are multiracial.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2018-2022 5-Year Estimates.

Racial categories include:

• White
• Black or African American
• American Indian and Alaska Native
• Asian
• Native Hawaiian and Other Pacific Islander
• Some other race
• Two or more races (multiracial)

Variables / Data Columns

• Race: This column displays the racial categories (excluding ethnicity) for the Columbia City
• Population: The population of the racial category (excluding ethnicity) in the Columbia City is shown in this column.
• % of Total Population: This column displays the percentage distribution of each race as a proportion of Columbia City total population. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Columbia City Population by Race & Ethnicity. You can refer the same here

The Indian road, unlike other geographies, demands a constant need for observation and prediction, a demand that can challenge even the most skilled drivers.

Building a high performing AI solution that can handle this challenge requires access to large amount of annotated data and building this on your own is immensely time consuming. We are here to help!

Get access to feeds with

A Million 2D bounding box annotations -150K+ Images (and adding more) -City, Highway & Suburban roads -Day, night and twilight lighting conditions -1080p and 720p high resolution images -Classes include: Bicycle, Car, Motorcycle, Bus, Truck, Traffic light, Traffic signs, People, Dog, Cow, Barricade

About this dataset

Context

The dataset tabulates the population of Jersey City by race. It includes the population of Jersey City across racial categories (excluding ethnicity) as identified by the Census Bureau. The dataset can be utilized to understand the population distribution of Jersey City across relevant racial categories.

Key observations

The percent distribution of Jersey City population by race (across all racial categories recognized by the U.S. Census Bureau): 28.73% are white, 21.45% are Black or African American, 0.75% are American Indian and Alaska Native, 25.78% are Asian, 9.64% are some other race and 13.64% are multiracial.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Racial categories include:

• White
• Black or African American
• American Indian and Alaska Native
• Asian
• Native Hawaiian and Other Pacific Islander
• Some other race
• Two or more races (multiracial)

Variables / Data Columns

• Race: This column displays the racial categories (excluding ethnicity) for the Jersey City
• Population: The population of the racial category (excluding ethnicity) in the Jersey City is shown in this column.
• % of Total Population: This column displays the percentage distribution of each race as a proportion of Jersey City total population. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Jersey City Population by Race & Ethnicity. You can refer the same here

The dataset pertains to the details of capital expenditure for all states taken together. The time period covered is from 1990-91(A) to 2009-10 (BE).

This dataset is about countries per year in India. It has 64 rows. It features 4 columns: country, capital city, and unemployment.

India All States: Capital Expenditures: Plan data was reported at 4,338,480.200 INR mn in 2017. This records an increase from the previous number of 4,178,258.900 INR mn for 2016. India All States: Capital Expenditures: Plan data is updated yearly, averaging 481,333.300 INR mn from Mar 1991 (Median) to 2017, with 27 observations. The data reached an all-time high of 4,338,480.200 INR mn in 2017 and a record low of 130,522.400 INR mn in 1991. India All States: Capital Expenditures: Plan data remains active status in CEIC and is reported by Reserve Bank of India. The data is categorized under India Premium Database’s Government and Public Finance – Table IN.FJ001: State Finances: Capital Expenditure: All States and Union Territories.

Imports of Capital Goods in India increased to 273188.40 INR Million in August from 255295.80 INR Million in July of 2014. This dataset includes a chart with historical data for India Imports of Capital Goods.

This dataset is about countries in India. It has 1 row. It features 5 columns: currency, capital city, continent, and net migration.

India All States: Capital Expenditures: Non Plan data was reported at 41,627,408.600 INR mn in 2017. This records an increase from the previous number of 40,890,376.300 INR mn for 2016. India All States: Capital Expenditures: Non Plan data is updated yearly, averaging 8,993,010.600 INR mn from Mar 1991 (Median) to 2017, with 27 observations. The data reached an all-time high of 49,916,918.700 INR mn in 2015 and a record low of 62,600.600 INR mn in 1991. India All States: Capital Expenditures: Non Plan data remains active status in CEIC and is reported by Reserve Bank of India. The data is categorized under India Premium Database’s Government and Public Finance – Table IN.FJ001: State Finances: Capital Expenditure: All States and Union Territories.

Success.ai’s Company Financial Data for Banking & Capital Markets Professionals in the Middle East offers a reliable and comprehensive dataset designed to connect businesses with key stakeholders in the financial sector. Covering banking executives, capital markets professionals, and financial advisors, this dataset provides verified contact details, decision-maker profiles, and firmographic insights tailored for the Middle Eastern market.

With access to over 170 million verified professional profiles and 30 million company profiles, Success.ai ensures your outreach and strategic initiatives are powered by accurate, continuously updated, and AI-validated data. Backed by our Best Price Guarantee, this solution empowers your organization to build meaningful connections in the region’s thriving financial industry.

Why Choose Success.ai’s Company Financial Data?

1. Verified Contact Data for Financial Professionals

   • Access verified email addresses, direct phone numbers, and LinkedIn profiles of banking executives, capital markets advisors, and financial consultants.
   • AI-driven validation ensures 99% accuracy, enabling confident communication and minimizing data inefficiencies.

2. Targeted Insights for the Middle East Financial Sector

   • Includes profiles from major Middle Eastern financial hubs such as Dubai, Riyadh, Abu Dhabi, and Doha, covering diverse institutions like banks, investment firms, and regulatory bodies.
   • Gain insights into region-specific financial trends, regulatory frameworks, and market opportunities.

3. Continuously Updated Datasets

   • Real-time updates reflect changes in leadership, market activities, and organizational structures.
   • Stay ahead of emerging opportunities and align your strategies with evolving market dynamics.

4. Ethical and Compliant

   • Adheres to GDPR, CCPA, and other global privacy regulations, ensuring responsible data usage and compliance with legal standards.

Data Highlights:

• 170M+ Verified Professional Profiles: Engage with decision-makers and professionals in banking, investment management, and capital markets across the Middle East.
• 30M Company Profiles: Access detailed firmographic data, including organization sizes, revenue ranges, and geographic footprints.
• Leadership Contact Information: Connect directly with CEOs, CFOs, risk managers, and regulatory professionals driving financial strategies.
• Decision-Maker Insights: Understand key decision-makers’ roles and responsibilities to tailor your outreach effectively.

Key Features of the Dataset:

1. Decision-Maker Profiles in Banking & Capital Markets

   • Identify and connect with executives, portfolio managers, and analysts shaping investment strategies and financial operations.
   • Target professionals responsible for compliance, risk management, and operational efficiency.

2. Advanced Filters for Precision Targeting

   • Filter institutions by segment (retail banking, investment banking, private equity), geographic location, revenue size, or workforce composition.
   • Tailor campaigns to align with specific financial needs, such as digital transformation, customer retention, or risk mitigation.

3. Firmographic and Leadership Insights

   • Access detailed firmographic data, including company hierarchies, financial health indicators, and service specializations.
   • Gain a deeper understanding of organizational structures and market positioning.

4. AI-Driven Enrichment

   • Profiles enriched with actionable data allow for personalized messaging, highlight unique value propositions, and enhance engagement outcomes.

Strategic Use Cases:

1. Sales and Lead Generation

   • Offer financial technology solutions, consulting services, or compliance tools to banking institutions and investment firms.
   • Build relationships with decision-makers responsible for vendor selection and financial strategy implementation.

2. Market Research and Competitive Analysis

   • Analyze trends in Middle Eastern banking and capital markets to guide product development and market entry strategies.
   • Benchmark against competitors to identify market gaps, emerging niches, and growth opportunities.

3. Partnership Development and Vendor Evaluation

   • Connect with financial institutions seeking strategic partnerships or evaluating service providers for operational improvements.
   • Foster alliances that drive mutual growth and innovation.

4. Recruitment and Talent Solutions

   • Engage HR professionals and hiring managers seeking top talent in finance, compliance, or risk management.
   • Provide staffing solutions, training programs, or workforce optimization tools tailored to the financial sector.

Why Choose Success.ai?

1. Best Price Guarantee
   • Access premium-quality financial data at competitive prices, ensuring strong ROI for your outreach, marketing, and partners...

📹Project Video available on YouTube - https://youtu.be/gu3Ot78j_Gc

Airlines Flights Dataset for Different Cities

The Flights Booking Dataset of various Airlines is a scraped datewise from a famous website in a structured format. The dataset contains the records of flight travel details between the cities in India. Here, multiple features are present like Source & Destination City, Arrival & Departure Time, Duration & Price of the flight etc.

This data is available as a CSV file. We are going to analyze this data set using the Pandas DataFrame.

This analyse will be helpful for those working in Airlines, Travel domain.

Using this dataset, we answered multiple questions with Python in our Project.

Q.1. What are the airlines in the dataset, accompanied by their frequencies?

Q.2. Show Bar Graphs representing the Departure Time & Arrival Time.

Q.3. Show Bar Graphs representing the Source City & Destination City.

Q.4. Does price varies with airlines ?

Q.5. Does ticket price change based on the departure time and arrival time?

Q.6. How the price changes with change in Source and Destination?

Q.7. How is the price affected when tickets are bought in just 1 or 2 days before departure?

Q.8. How does the ticket price vary between Economy and Business class?

Q.9. What will be the Average Price of Vistara airline for a flight from Delhi to Hyderabad in Business Class ?

These are the main Features/Columns available in the dataset :

1) Airline: The name of the airline company is stored in the airline column. It is a categorical feature having 6 different airlines.

2) Flight: Flight stores information regarding the plane's flight code. It is a categorical feature.

3) Source City: City from which the flight takes off. It is a categorical feature having 6 unique cities.

4) Departure Time: This is a derived categorical feature obtained created by grouping time periods into bins. It stores information about the departure time and have 6 unique time labels.

5) Stops: A categorical feature with 3 distinct values that stores the number of stops between the source and destination cities.

6) Arrival Time: This is a derived categorical feature created by grouping time intervals into bins. It has six distinct time labels and keeps information about the arrival time.

7) Destination City: City where the flight will land. It is a categorical feature having 6 unique cities.

8) Class: A categorical feature that contains information on seat class; it has two distinct values: Business and Economy.

9) Duration: A continuous feature that displays the overall amount of time it takes to travel between cities in hours.

10) Days Left: This is a derived characteristic that is calculated by subtracting the trip date by the booking date.

11) Price: Target variable stores information of the ticket price.

Imports of Capital Goods CMLV in India increased to 3310916.01 INR Million in March from 2909801.67 INR Million in February of 2014. This dataset includes a chart with historical data for India Imports of Capital Goods CMLV.