India GDP Analysis Problem Description - I NITI Aayog: Background NITI Aayog (National Institution for Transforming India) is a policy think tank of the Government of India; it provides strategic inputs to the central and the state governments to achieve various development goals. In the past, NITI Aayog has played an important role in initiatives such as Digital India, Atal Innovation Mission and various agricultural reforms and have designed various policies in education, skill development, water management, healthcare, etc. NITI Aayog was established to replace the Planning Commission of India, which used to follow a top-down model for policy making, i.e., it typically designed policies at the central level (such as the 5-year plans). On the other hand, NITI Aayog designs policies specific to the different states or segments of the economy. Finance Minister, Arun Jaitley, made the following observation on the necessity of creating NITI Aayog, "The 65-year-old Planning Commission had become a redundant organisation. It was relevant in a command economy structure, but not any longer. India is a diversified country and its states are in various phases of economic development along with their own strengths and weaknesses. In this context, a ‘one size fits all’ approach to economic planning is obsolete...". Project Brief We are working as the chief data scientist at NITI Aayog, reporting to the CEO. The CEO has initiated a project wherein the NITI Aayog will provide top-level recommendations to the Chief Ministers (CMs) of various states, which will help them prioritise areas of development for their respective states. Since different states are in different phases of development, the recommendations should be specific to the states. The overall goal of this project is to help the CMs focus on areas that will foster economic development for their respective states. Since the most common measure of economic development is the GDP, we will analyse the GDP of the various states of India and suggest ways to improve it. Understanding GDP Gross domestic product (GDP) at current prices is the GDP at the market value of goods and services produced in a country during a year. In other words, GDP measures the 'monetary value of final goods and services produced by a country/state in a given period of time'. GDP can be broadly divided into goods and services produced by three sectors: the primary sector (agriculture), the secondary sector (industry), and the tertiary sector (services). It is also known as nominal GDP. More technically, (real) GDP takes into account the price change that may have occurred due to inflation. This means that the real GDP is nominal GDP adjusted for inflation. We will use the nominal GDP for this exercise. Also, we will consider the financial year 2015-16 as the base year, as most of the data required for this exercise is available for the aforementioned period. Per Capita GDP and Income Total GDP divided by the population gives the per capita GDP, which roughly measures the average value of goods and services produced per person. The per capita income is closely related to the per capita GDP (though they are not the same). In general, the per capita income increases when the per capita GDP increases, and vice-versa. For instance, in the financial year 2015-16, the per capita income of India was ₹93,293, whereas the per capita GDP of India was $1717, which roughly amounts to ₹1,11,605. Problem Description - II Data The data is sourced from https://data.gov.in/, an Open Government Data (OGD) platform of India. The download instructions are provided in the next segment. The data for GDP analysis of the Indian states is divided into two parts: Data I-A: This dataset consists of the GSDP (Gross State Domestic Product) data for the states and union territories. Data I-B: This dataset contains the distribution of GSDP among three sectors: the primary sector (agriculture), the secondary sector (industry) and the tertiary sector (services) along with taxes and subsidies. There is separate dataset for each of the states. We are expected to read the dataset for the available states and join these (in Python) if needed. There are two parts to this project. In the first part, we will analyse and compare the GDPs of various Indian states (both total and per capita). The GDP of a state is referred to as the GSDP (Gross State Domestic Product). Then, we will divide the states into four categories based on the GDP per capita, and for each of these four categories, we will analyse the sectors that contribute the most to the GDP (such as agriculture, real estate, manufacturing, etc.). In the second part, we will analyse whether GDP per capita is related to dropout rates in schools and colleges. Part-I: GDP Analysis of the Indian States For each of the following steps of analysis, choose an appropriate type of plot for comparing the data. Also, ensure that the plots are in increasing or decreasing order for better comparison. For example, if we make a bar plot to compare the GDPs of the states, ensure that the bars are in either increasing or decreasing order of GDP. Part I-A: For the analysis below, use the Data I-A. First, we need to load the data in Python properly and then clean it. This also involves the treatment of missing values, we can choose to drop the row or column as well. Remember this will affect our next analysis and results drastically. Plot a graph for rows " % Growth over previous year" for all the states (not union territories) whose data is available, use as much data as possible for this exercise. Use the best fit line to represent the growth for each state. Draw a similar line graph for the nation as well. How will we compare the growth rates of any two states? Which states have been growing consistently fast, and which ones have been struggling? Rank top 3 fastest and 3 slowest-growing states. What is the Nation's growth rate? What has been the growth rate of my home state, and how does it compare to the national growth rate? Plot the total GDP of the states for the year 2015-16: Which Plot will we use for this? Why? (Remeber to plot the graph in a way such as it is easier to read and compare) Identify the top 5 and the bottom 5 states based on total GDP. What insights can we draw from this graph? What states are performing poorly? (Remember: this will not be solely based on total GDP) Part I-B: For the analysis below, use Data I-B. We can also use Data I-B along with Data I-A if required. Also, perform the analysis only for the duration 2014-15. Filter out the union territories (Delhi, Chandigarh, Andaman and Nicobar Islands, etc.) for further analysis, as they are governed directly by the central, not state governments. Plot the GDP per capita for all the states. Identify the top 5 and the bottom 5 states based on the GDP per capita. Find the ratio of the highest per capita GDP to the lowest per capita GDP. Plot the percentage contribution of the primary, secondary and tertiary sectors as a percentage of the total GDP for all the states. Which plot will we use here? Why? Why is (Primary + Secondary + Tertiary) not equal to total GDP? Can we draw any insight from this? Find correlation of percentile of the state (% of states with lower per capita GDP) and %contribution of Primary sector to total GDP. Categorise the states into four groups based on the GDP per capita (C1, C2, C3, C4, where C1 would have the highest per capita GDP and C4, the lowest). The quantile values are (0.20,0.5, 0.85, 1), i.e., the states lying between the 85th and the 100th percentile are in C1; those between the 50th and the 85th percentiles are in C2, and so on. Note: Categorisation into four groups will simplify the subsequent analysis, as otherwise, comparing the data of all the states would become quite exhaustive. For each category (C1, C2, C3, C4): Find the top 3/4/5 sub-sectors (such as agriculture, forestry and fishing, crops, manufacturing etc., not primary, secondary and tertiary) that contribute to approximately 80% of the GSDP of each category. Note-I: The nomenclature for this project is as follows: primary, secondary and tertiary are named 'sectors', while agriculture, manufacturing etc. are named 'sub-sectors'. Note-II: If the top 3 sub-sectors contribute to, say, 79% of the GDP of some category, we can report "These top 3 sub-sectors contribute to approximately 80% of the GDP". This is to simplify the analysis and make the results consumable. (Remember, the CEO has to present the report to the CMs, and CMs have limited time; so, the analysis needs to be sharp and concise.) Plot the contribution of the sub-sectors as a percentage of the GSDP of each category. Now that we have summarised the data in the form of plots, tables, etc., try to draw non-obvious insights from it. Think about questions such as: How does the GDP distribution of the top states (C1) differ from the others? Which sub-sectors seem to be correlated with high GDP? Which sub-sectors do the various categories need to focus on? Ask other such relevant questions, which we think are important, and note we insights for category separately. More insights are welcome and will be awarded accordingly. Finally, provide at least two recommendations for each category to improve the per capita GDP. Part-II: GDP and Education Dropout Rates In Part-I, we would have noticed that (one) way to increase per capita GDP is by shifting the distribution of GDP towards the secondary and tertiary sectors, i.e., the manufacturing and services industries. But these industries can thrive only when there is an availability of educated and skilled labour. In this part of the analysis, we will investigate whether there is any relationship between per capita GDP with dropout rates in education. Data Data II: This section will require the dropout rate dataset apart from the dataset that we used in Part-1 of the case study. Download instructions are provided in the next segment. Part-II: GDP and Education Analyse if there is any correlation of GDP per capita with dropout rates in education (primary, upper primary and secondary) for the year 2014-2015 for each state. Choose an appropriate plot to conduct this analysis. Is there any correlation between dropout rate and %contribution of each sector (Primary, Secondary and Tertiary) to the total GDP? We have the total population of each state from the data in part I. Is there any correlation between dropout rates and population? What is the expected trend and what is the observation? Write down the key insights we draw from this data: Form at least one reasonable hypothesis for the observations from the data About GDP analysis for India in the year for 2015-16 and recommendation for the individual states for increasing the GDP by focusing on various factor. Topics python statistical-analysis data-analysis gdp-analysis Resources Readme Stars 0 stars Watchers 1 watching Forks 0 forks Releases No releases published Packages No packages published Languages Jupyter Notebook 100.0% Footer © 2022 GitHub, Inc. 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The healthcare sector in India is intended to grow at a compounded Annual Growth Rate (CAGR) of 15 % to touch $158.2 billion in 2017 from $78.6 billion in 2012. Also healthcare spending in India was figured 5 % of gross domestic product (GDP) in 2013 and is anticipated to remain at that level through 2016. Total health care spending in local-currency terms is protruded to rise at an annual rate of over 12 %, from an estimated $96.3 billion in 2013 to $195.7 billion in 2018. While this speedy growth rate will bring back high inflation, it will also be repelled by increasing public and private expenditures on health. The healthcare industry has come a long way from the situations when patients who could afford it had to go overseas; nowadays patients from many countries are constellating to India for their medical emergencies. We know medical services in India delivered via public and private sector. However the government funds apportioned to healthcare segment have always been low in relation to the population of the country simply we can say public health care system is uneven, with underfunded and overfilled hospitals and poor rural coverage. Compromised funding by the Indian government has been ascribed to historic failures on the part of the Ministry of Health and Family Welfare (MHFW) to pay out its apportioned budget to the greatest extent. This is in spite of increasing demand, due in part, to developing incidence of age and lifestyle linked chronic disorders leading from urbanization, changing food habits and lifestyles, getting up obesity levels and far-flung tobacco products. These days India’s health care sector finds close to 50 % spend on in-patient beds for lifestyle diseases mainly urban and semi urban regions. Furthermore, various reports shows that India has world’s highest numbers of diabetes patients and has led in the mushrooming of multi specialty hospitals to battle with lifestyle disorders. The government’s low expending on health care puts much of the load on patients, as showed by the country’s out of pocket expending rate, one of the world’s highest. According to the World Health Organization (WHO) report, just 33 % of Indian health care expenditures in 2012 came from government sources. Of the leftover was out of pocket. At the same time the private sector of Indian healthcare segment, these facilities are run for profit. Medical facilities run by charitable organizations also provide medical services totally free or at minimal charges depending on the financial status of the patients. Looking at Indian healthcare market in a Pan India perspective the statistics for India’s health infrastructure are very low that of other developed countries. The United States of America has 1 bed for every 350 patients while the ratio for Japan is 1 for 85. In contrast, India has 1 bed for every 1,050 patients. To meet bed availability to the criteria of more developed countries, India requires 100,000 beds this decade, at an investment of $50 billion. Also, India’s expenditure on health care information technology is also very low. Indian hospitals will require increasing their IT spending to a great extent to provide bettered and patient-centric service. On the other side of the these facts if look at the trends of the Indian healthcare segment, over the last 40 years India has progressed a huge health infrastructure and workforce at primary, secondary and tertiary care in public, charitable and private medical centers. At present private segment range from those put up by multi specialty/ specialty corporate hospitals, nursing homes, poly clinics and clinics run by qualified medical professionals. The major portion of the private hospitals is small medical care establishments with 85% of them having less than 25 beds capacity. Private tertiary care hospitals, furnishing specialty and multispecialty medical services, account for only 1 - 2% of the total number of institutions, while corporate medical facilities make up less than 1%. The private medical services account for 82% of all out patient (OP) and 52% of inpatient (IP) services at all India level. In the recent past India is getting a favoured medical care destination for many countries due to low cost and good quality medical procedures giving rise to the scope for medical tourism. This leading more hospitals in the private segment advancing their medical facilities to land a share of this business. According to recent reports, India has a possibility to attract 1 million health tourists per annum, which could contribute $ 5 billion. The go on of this would be advance of medical facilities, in terms of new equipments, diagnostic procedures, equipments etc. Also health insurance which was absent earlier has currently going up in impressive manner. Employment scenario Medical segment in India provides direct employment to over 10 million professionals, and opportunities going to be increase in very impressive manner; the employment opportunities are not just limited to doctors and nurses. This profession would need a good number of paramedical professionals and more importantly a large number of mid and senior level managers and with expertise across various specialties’. According to the National Skill Development Corporation(NSDC), "By 2022, India would need 74 lakh medical service workforce” Besides, the size of the healthcare sector is anticipated to grow to Rs 9.64 lakh crore by 2017. With many and different medical services, there are over 10 lakh allied health professionals in India in the areas of nursing associates, medical assistants, medical equipment operators, optometrists, physiotherapists, dieticians, dental assistants, and many other which is still short of the current necessitate. Also there is a significant gap in the availability of medical practitioners and it is a trend that is likely to continue for next few years. Currently India’s ratio of 0.7 doctors & 1.5 nurses per 1,000 people is dramatically lower than the WHO average of 2.5 doctors and nurses per 1,000 people. Moreover, there is an acute shortage of paramedical and administrative professionals. There are over 7,50,000 registered Ayurveda, Yoga, Unani, Siddha and Homoeopathy practitioners in the country. These numbers, when joined with the total number of physicians shaped in allopathy, satisfy to an extent, the total requirement of medical practitioners required in the country," NSDC predictions”. To conclude, "India has become one of the most favoured or affordable destinations for patients looking for best healthcare care at cost much lower than that of other countries”. India can further leverage its status as a fairly priced and quality medical care provider, as result providing to a greater proportion of world population. "Therefore, there is urgency for both qualitative and quantitative skill development programs in the medical segment also policy designers and industry players need to concentrate on advancing technical skills of the clinical and non clinical medical professionals for progressed medical care services".

End-to-end Healthcare Analytics project using 50,000+ Indian healthcare records. Built 5 interactive Power BI dashboards covering hospital resources, doctor specialization, patient visits, disease trends, treatment cost, and demographics. Data cleaning & modeling performed in MySQL + Power Query with custom DAX KPIs.

Will use LSTM/DeepL to predict future confirmed cases of corona virus. This video is about code_rundown of analysis/prediction build for Corona-virus(Covid-19). At first , will use some data preprocessing and analysis on the dataset obtained from kaggle, doing trend analysis with various plots,bar/graphs,etc with world countries effected info in world map during this analysis is it proven that Covid-19 is pandemic which is what WHO also declared recently. and also china took some good measures so their rate of confirmed cases is deceased drastically compared to month earlier which are good signs,also saw Indian cases ,all these are subject to 11/03/20 as it is the latest date in the dataset(record). Then in later part of video will predict for coming few dates how will be the confirmed rate/case w.r.t to world and obtain a graph plot with actual result to obtain error and accuracy perks. Following are some of the precautions prescribed by WHO:: 1)Maintain social distancing 2)Wash your hands frequently 3)Avoid touching eyes, nose and mouth 4)Practice respiratory hygiene 5)If you have fever, cough and difficulty breathing, seek medical care early 6)Stay informed and follow advice given by your healthcare provider Want code for (.this) contact me @ ::(cvrrocket@gmail.com) @#social: Instagram(CVRROCKET):/cvrrocket Facebook:https://www.facebook.com/cvrrocket YouTube: https://www.youtube.com/cvrohitcvrr GitHub:https://github.com/rohitchatla Bussiness_Email: cvrrocket@gmail.com bgm @ CVRROCKET.MUSIC tuned by ~ Tone.js automated voice ~ voice API'S #Coronavirus #Covid-19

Market analysis of Indian Healthcare industry and its future trend in Indian market. Devised a go-to-market strategy for a healthcare startup based on cost and feasibility and plan to establish its presence in this industry.

This project analyzes the pricing and market competitiveness of Indian pharmaceutical products using SQL. It explores trends in drug formulations, manufacturers, and therapeutic classes to identify pricing gaps, assess affordability, and provide insights for healthcare decision-making and pharma market strategy.

Overview: The Indian Liver Patient Dashboard in Excel presents a comprehensive and interactive summary of liver-related health data. It offers a clear and insightful view of patient demographics, liver function metrics, and associated risk factors, helping healthcare professionals or analysts monitor and analyze trends for informed decision-making.