As of February 2025, the state of Uttar Pradesh in India has the highest average monthly salary of about ** thousand Indian rupees. In contrast, Lakshadweep has the lowest average monthly salary of ***** thousand rupees during the same year.

In 2024, the average monthly salary was **** thousand Indian rupees in Mumbai city of India. The average monthly salary in the capital city of Delhi was around **** thousand Indian rupees. In comparison, the average monthly salary was over ** thousand Indian rupees in Madurai during the same year.

Wages in India increased to 21103 INR/Month in the second quarter of 2024 from 21036 INR/Month in the first quarter of 2024. This dataset provides the latest reported value for - India Average Daily Real Wage Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

According to the survey conducted on work life aspects of young Indians in 2020, over ** percent of millennials earning more than 100 thousand Indian rupees per month expected their income to rise by more than ** percent. Furthermore, only ten percent of respondents earning the same income expected their salary to increase by ** percent.

In 2022, the average monthly salary of men in the regular salaried class was over ** thousand Indian rupees. This was over **** thousand rupees more than their female counterparts. This suggests the existence of a gender pay gap among the salaried class.

India Savings Deposits: Small: Receipts: DE: Monthly Income Scheme data was reported at 5,270.000 INR mn in Feb 2018. This records a decrease from the previous number of 6,350.000 INR mn for Jan 2018. India Savings Deposits: Small: Receipts: DE: Monthly Income Scheme data is updated monthly, averaging 18,710.000 INR mn from Apr 1997 (Median) to Feb 2018, with 251 observations. The data reached an all-time high of 63,360.000 INR mn in Mar 2010 and a record low of -4,230.000 INR mn in Apr 2017. India Savings Deposits: Small: Receipts: DE: Monthly Income Scheme data remains active status in CEIC and is reported by Reserve Bank of India. The data is categorized under Global Database’s India – Table IN.KAG001: Saving Deposits.

Dataset Description: Indian Urban Affordability and Economic Productivity (221 Cities) About the Dataset

This dataset represents the comprehensive 221-city version developed and utilized in the research paper “Predicting Urban Affordability and Economic Productivity in India: A Data-Driven KNN and Random Forest Framework with Insights from Selected Major Cities.”

It builds upon the author’s earlier 70-city affordability dataset and significantly expands its scope.

The dataset provides a unified framework to study how urban affordability, digital readiness, and GDP specialization jointly influence economic livability and productivity across different city tiers.

Data Provenance and Construction

Primary Source: Extended web-scraped affordability data originally compiled from LivingCost.org and other verified open-data platforms.

Cleaning & Standardization: City names normalized (e.g., “Bengaluru” → “Bangalore”), and all numeric fields standardized to INR using a consistent USD→INR conversion rate for comparability.

Features Included

Each record (row) corresponds to one city and contains the following metrics:

Cost of Living (INR)

Monthly Rent (INR)

Monthly After-Tax Salary (INR)

Income After Rent (INR)

Affordability Ratio (“Months Covered”)

Intended Applications

This dataset can be used for:

🧮 Cross-city affordability and livability analysis

🤖 Machine Learning model development (affordability or salary prediction)

🌆 Urban economics and policy simulation studies

📈 Correlation and regression-based research in ICT and GDP domains

📊 Dashboard and visualization projects (Power BI, Tableau, SAP SAC, etc.)

It is designed for use by researchers, policymakers, educators, and data analysts seeking a reliable, structured, and multi-domain dataset on Indian urban dynamics.

Data Quality and Transparency

✅ Uniform currency and value scaling

✅ Reproducible preprocessing (Python-based pipelines with Scikit-Learn)

✅ Missing values imputed using KNN-based methodology

✅ Verified against baseline datasets used in prior research

✅ Released under Creative Commons Attribution 4.0 International (CC BY 4.0) license

Significance

This dataset forms the empirical backbone of the author’s second research paper, providing the quantitative base for the KNN baseline model and the Random Forest multi-output regressor used to predict salary and affordability across Indian cities.

It enables city-level insight generation for policymakers and supports reproducible, data-driven research in urban economics, digital inclusion, and sustainable development.

Future Extensions

An upcoming enhancement will include:

Complete AQI integration for all 221 cities to examine the affordability–environment linkage.

Time-series extension for multi-year trend analysis.

Inclusion of healthcare, safety, and green infrastructure indicators for a broader livability framework.

A additional file used in my paper on T30 cities of India with justification is also attached.

In a survey conducted from September 2022 to June 2023 in rural India, it was found that cultivation constituted the highest share of farmer/ agricultural household income in 2022, whereas wage labor constituted ** percent of income.

This dataset contains 30,000 records representing job market and salary trends across India’s top companies, popular tech and non-tech roles, multiple cities, and experience levels. It is designed to help researchers, analysts, and machine-learning practitioners analyze salary patterns, hiring demand, geographic trends, remote-work adoption, and career progression dynamics in the Indian job ecosystem.

Each row represents a job-related snapshot tied to a specific date, making the dataset suitable for trend analysis, forecasting, and workforce analytics. Salaries have been generated using realistic ranges for each job category in India, and demand indicators reflect hiring trends seen across major urban centers such as Bangalore, Hyderabad, Pune, and Delhi.

This dataset is useful for: Salary prediction (regression modeling) Job market demand forecasting Skill-driven salary analysis Remote work adoption analytics City-wise hiring and compensation gaps Experience-level salary progression Time-series trend analysis Workforce planning and HR analytics ML projects using categorical + time-series data

All company names, job roles, cities, and experience ranges are based on real Indian labor market dynamics, making the dataset relevant, practical, and highly useful for exploratory analysis and machine learning.

COLUMN DESCRIPTIONS

Record_Date The date of the job market observation. Useful for time-series trend modeling.

Company_Name The Indian company offering the job role (e.g., TCS, Infosys, Reliance, Accenture India, Amazon India).

Job_Role The position or designation (e.g., Software Engineer, Data Analyst, Product Manager).

Experience_Level Experience bracket required for the role (e.g., 1–3 years, 5–8 years).

City Location of the job such as Bangalore, Pune, or Hyderabad.

Salary_INR Annual salary offered for that job role (in Indian Rupees).

Demand_Index A 0–100 indicator representing hiring demand for that role/location.

Remote_Option_Flag 1 if the job supports remote/hybrid work, 0 if on-site only.

Salary_Trend_Pct Month-to-month salary percentage change for similar roles.

India Savings Deposits: Small: Outstanding: DE: Monthly Income Scheme data was reported at 1,808,010.000 INR mn in Feb 2018. This records an increase from the previous number of 1,802,740.000 INR mn for Jan 2018. India Savings Deposits: Small: Outstanding: DE: Monthly Income Scheme data is updated monthly, averaging 1,808,230.000 INR mn from Apr 1997 (Median) to Feb 2018, with 251 observations. The data reached an all-time high of 2,192,340.000 INR mn in Jan 2011 and a record low of 101,000.000 INR mn in Apr 1997. India Savings Deposits: Small: Outstanding: DE: Monthly Income Scheme data remains active status in CEIC and is reported by Reserve Bank of India. The data is categorized under Global Database’s India – Table IN.KAG001: Saving Deposits.

Tourism plays an important role as a foreign exchange earner for the country. This dataset contains the monthly foreign exchange earnings of India through tourism sector

1) Data Introduction • The TaxTruth: Tax Perception Dataset from India Dataset is an original collection of information from 250 Indian citizens, including their monthly income, taxes paid, occupation type, government benefits, GST usage behavior, billionaire taxation awareness, presence of surrounding billionaires, and emotional response to the tax system.

2) Data Utilization (1) TaxTruth: Tax Perception Dataset from India Dataset has characteristics that: • The dataset includes numerical data (monthly income, tax payments), categorical data (occupation, government benefits, use of GST, etc.), and emotional and cognitive responses such as "feel exploited by the tax system." • It is designed to analyze citizens' tax perceptions and emotional responses together as well as economic indicators. (2) TaxTruth: Tax Perception Dataset from India Dataset can be used to: • Public Policy Analysis: The direction of policy improvement can be derived by analyzing the correlation between citizens' emotional response to the tax system, the actual tax burden, and whether they

India's average salary is ₹31.1L CTC (₹181,167/month take-home). Updated October 2025 with real professional data.

In 2022, government or private services accounted for the highest source of income at *** thousand Indian rupees in rural households, while livestock rearing was valued at over **** thousand Indian rupees.

This statistic shows the share of monthly income among workers in India according to gender in the year 2015. As seen in the statistic, a high majority, that is, ***** percent of female workers earned up to ***** Indian rupees per month. Overall, it was seen that ** percent of male and ** percent of female workers in India earned less than 10,000 rupees a month during the measured time period.

dataset contains detailed financial and demographic data for 20,000 individuals, focusing on income, expenses, and potential savings across various categories. The data aims to provide insights into personal financial management and spending patterns.

• Income & Demographics:
  • Income: Monthly income in currency units.
  • Age: Age of the individual.
  • Dependents: Number of dependents supported by the individual.
  • Occupation: Type of employment or job role.
  • City_Tier: A categorical variable representing the living area tier (e.g., Tier 1, Tier 2).
• Monthly Expenses:
  • Categories like Rent, Loan_Repayment, Insurance, Groceries, Transport, Eating_Out, Entertainment, Utilities, Healthcare, Education, and Miscellaneous record various monthly expenses.
• Financial Goals & Savings:
  • Desired_Savings_Percentage and Desired_Savings: Targets for monthly savings.
  • Disposable_Income: Income remaining after all expenses are accounted for.
• Potential Savings:
  • Includes estimates of potential savings across different spending areas such as Groceries, Transport, Eating_Out, Entertainment, Utilities, Healthcare, Education, and Miscellaneous.

Government Revenues in India increased to 1730216 INR Tens of Million in September from 1282709 INR Tens of Million in August of 2025. This dataset provides - India Government Revenues- actual values, historical data, forecast, chart, statistics, economic calendar and news.

About This Dataset

This dataset is the original 70-city version used in my first published research paper: “A Data-Driven Survey on Cost of Living and Salary Affordability in Indian Cities” (IJRASET, 2025) Link: https://www.ijraset.com/best-journal/a-datadriven-survey-on-cost-of-livingsalary-affordability-in-indian-cities

It was created using web-scraping techniques from LivingCost.org and converted to INR using a consistent USD→INR exchange rate. This dataset forms the foundational base for affordability analysis, exploratory data analysis (EDA), and benchmarking cost-of-living patterns across India.

The dataset includes 70+ Indian cities, with fields covering living cost, rent, salary, affordability ratio (“months covered”), and derived financial indicators. It is clean, structured, and suitable for beginner to intermediate analytics projects.

Why This Dataset?

This dataset is ideal for:

EDA practice for college & school projects

Correlation and regression analysis

Basic ML tasks (predicting salary, affordability, rent, etc.)

Urban economics mini-projects

Dashboard creation (PowerBI, Tableau)

Data cleaning and preprocessing assignments

It is designed to be simple enough for students but structured enough for real-world analysis.

Features Included

Each row represents a city/state-level affordability profile with:

Cost of living (USD & INR)

Rent for a single person (USD & INR)

Monthly after-tax salary (USD & INR)

Income after rent

“Months Covered” affordability ratio

Source URLs for verification

Exchange rate used

This makes the dataset both transparent and reliable for academic usage.

Data Quality

Web-scraped directly from LivingCost.org

Cleaned and standardized

Currency converted uniformly

Non-city entries flagged

Fully reproducible from the source

This dataset served as the master input for my peer-reviewed paper and has been validated through statistical analysis.

Intended Audience

Students (school, undergraduate, postgraduate)

Data science beginners

Educators needing real datasets for teaching

Analysts looking for quick EDA practice

Researchers exploring affordability or urban economics

Note

A more comprehensive 200+ city enhanced dataset (used in my second paper) will be uploaded soon, including ICT metrics, GDP, and extended affordability indicators.

Comprehensive YouTube channel statistics for The Indian Mukbanger, featuring 3,620,000 subscribers and 2,758,969,983 total views. This dataset includes detailed performance metrics such as subscriber growth, video views, engagement rates, and estimated revenue. The channel operates in the Food category and is based in IN. Track 1,022 videos with daily and monthly performance data, including view counts, subscriber changes, and earnings estimates. Analyze growth trends, engagement patterns, and compare performance against similar channels in the same category.

Comprehensive YouTube channel statistics for Indian Food Talk, featuring 721,000 subscribers and 435,758,842 total views. This dataset includes detailed performance metrics such as subscriber growth, video views, engagement rates, and estimated revenue. The channel operates in the Food category and is based in IN. Track 846 videos with daily and monthly performance data, including view counts, subscriber changes, and earnings estimates. Analyze growth trends, engagement patterns, and compare performance against similar channels in the same category.