The Python Mega Course is one of the top online Python courses with over 100,000 enrolled students and is targeted toward people with little or no previous programming experience. The course follows a modern-teaching approach where students learn by doing. You will start Python from scratch by first creating simple programs. Once you learn the basics you will then be guided on how to create 10 real-world complex applications in Python 3 through easy video explanations and support by the course instructor. Some of the applications you will build during the course are database web apps, desktop apps, web scraping scripts, webcam object detectors, web maps, and more. These programs are not only great examples to master Python, you can also use any of them as a portfolio once you have built them. By buying the course you will gain lifetime access to all its videos, coding exercises, quizzes, code notebooks, and the Q&A inside the course where you can ask your questions and get an answer the same day. On top of that you are covered by the Udemy 30-day money back guarantee, so you can easily return the course if you don't like it. If you don't know anything about Python, do not worry! In the first two sections, you will learn Python basics such as functions, loops, and conditionals. If you already know the basics, then the first two sections can serve as a refresher. The other 22 sections focus entirely on building real-world applications. The applications you will build cover a wide range of interesting topics: Web applications Desktop applications Database applications Web scraping Web mapping Data analysis Data visualization Computer vision Object-Oriented Programming Specifically, the 10 Python applications you will build are: A program that returns English-word definitions A program that blocks access to distracting websites A web map visualizing volcanoes and population data A portfolio website A desktop-graphical program with a database backend A webcam motion detector A web scraper of real estate data An interactive web graph A database web application A web service that converts addresses to geographic coordinates To consider yourself a professional programmer you need to know how to make professional programs and there's no other course that teaches you that, so join thousands of other students who have successfully applied their Python skills in the real world. Sign up and start learning Python today! What you’ll learn Go from a total beginner to an advanced-Python programmer Create 10 real-world Python programs (no useless programs) Solidify your skills with bonus practice activities throughout the course Create an app that translates English words Create a web-mapping app Create a portfolio website Create a desktop app for storing book information Create a webcam video app that detects objects Create a web scraper Create a data visualization app Create a database app Create a geocoding web app Create a website blocker Send automated emails Analyze and visualize data Use Python to schedule programs based on computer events. Learn OOP (Object-Oriented Programming) Learn GUIs (Graphical-User Interfaces) Are there any course requirements or prerequisites? A computer (Windows, Mac, or Linux). No prior knowledge of Python is required. No previous programming experience needed. Who this course is for: Those with no prior knowledge of Python. Those who know Python basics and want to master Python

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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1.Variables 2. Functions,3.Datatypes,4.conditions,5.Looping and user input,6 File Handling,8 Building a Text Generator,9 Data Analysis with Pandas,10 Numpy,11.Leaflet Webmaps with python and folium,12 Building a website Blocker,13 Building a Website with Python and Flask,14 Building GUI with Tkinter,15 Python for interacting with SQLite and PostGRE SQL,16 Building a desktop database application,17 OOPS,18 Python for image and video Processing,19 Webcam Motion Destector,20 Data Visualization on Browser,21 Webscraping,22 Scraping Real estate property data from web,23 Building a web based financial graph,24 Data Collector Web app with postgreSQL and Flask,25 Building a Geocoder web service

Moscow real estate data analysis, graphing and experiments

Bengaluru Land Price & Demand Analyzer is a Django app that predicts land prices and analyzes demand trends in Bengaluru. Users input property details to get price forecasts, and the analysis page displays interactive graphs of demand trends, combining machine learning and data visualization for real estate insights.

After last few years of slowdown, Real Estate Sector is set on the path of growth. Though the foreign investment started to flow with the formation of a stable government in May 2014, economic reforms and liberal FDI policies have further made the investment process easy. Riding on the hope of “Acche Din”, India saw a huge $2.2 billion (70% raise) foreign investment as per JLL reports. The World Bank predicts India to be the fastest growing economy in coming years with close to 8% GDP growth rate. The graph below gives a comparative analysis of the GDP performance of the world and developing Countries in South Asia. The future picture of Indian economy is bright and Real Estate sector is emerging as a lucrative investment option. Apart from private sector investments, several government projects like development of Smart Cities, affordable homes for EWS (economically weaker section of society) and housing for all by 2022 offer good potential for investors. The government is also working to enhance infrastructure across the country with number of highway, Metro and Airport projects. Developing affordable housing in major cities is a big challenge for government, as 10 million people migrate to cities every year and most of them are job seekers. The last decade has witnessed the migration of 91 Million people to cities and only 51 Million houses were made to accommodate them. The government has implemented several changes in the economy and FDI policy to tackle the issue. The time is right for investors to carry out some intelligent investments and below areten reasons to believe and participate in the growth of the Real Estate in India in 2016. 1. Special Treatment for NRIs on Investment and non-consideration of property leasing as real estate business will boost the foreign investment in Real Estate Sector. 2. The minimum requirements of 20,000 square meter development and capital of $5 Million have been removed. This step welcomes small investors and will promote construction and development in Tier II and Tier III cities. 3. The policy of bringing foreign investment within 6 months of the starting of a project has been done away with, leading to more flexibility and ease of business in the country. 4. Lower interest rates, affordable housing schemes, and easy installments will allure middle-class families to buy property, so we will see a rise in the demand of residential property in class B and C cities. 5. Foreign investor can now exit from a project in 3 years or earlier if the project gets completed. The easy exit policy gives flexibility in ease of doing business, saves time and would invite more FDI. 6. The growing IT and Service industry will need more workforce and office space increasing the demand for residential and commercial property. 7. The soaring E-commerce industry will continue to grow as the number of online shoppers is increasing rapidly, there will be more demand of warehouses in future. 8. Manufacturers with foreign investments are now allowed to directly sell their products using online or offline platforms without government approval. For this, they will need infrastructure, warehouse, and office space, leading to increased real estate requirement. 9. Flexibility to Single brand retail trading in E-commerce will again contribute to the success of Real Estate industry. 10. No government approval is needed for eligible investment in LLP (Limited Liability Partnership) and foreign investment through share Swap. This will enhance the ease of doing business,thus attracting more foreign investors. Keeping in mind the World Bank report and recent economic reforms, Real estate Sector in India holds a huge potential as a long term investment portfolio for individual investors. Besides, metro cities the Tier II and II cities are going to witness exponential growth rate in real estate in 2016. In short, Indian Real Estate business is going to undergo a policy and investment transformation in the coming years. The situation will be more favorable if the land acquisition and other land reform bills get approved in the parliament.

Исследовательский (графический) анализ данных о продаже недвижимости в регионе

Analyzes real estate offices using Graph Theory. Implements metrics like radius, diameter, eccentricity, and density with NetworkX to evaluate office distribution, relationships, and market gaps. Provides insights into real estate agent connectivity and facility availability.

knowledge graph driven real estate investment analysis

A graph-native, multi-agent voice orchestrator for real estate feasibility and spatial contagion analysis.

Cleaning and Importing NYC Real-estate data from [ACRIS] (http://www1.nyc.gov/site/finance/taxes/acris.page) into Neo4j for graph analysis.

Showcasing Big Data Analytical capabilities using PySpark across real estate prediction, NYC taxi graph analysis, and stock market streaming. Implemented ML, graph analytics, and streaming to deliver scalable solutions and actionable insights. Databricks implementation WIP

Showcased Big Data capabilities using PySpark on Databricks across real estate prediction, NYC taxi graph analysis, and stock market streaming. Implemented ML, graph analytics, and streaming to deliver scalable solutions and actionable insights. Includes notebooks, report, and presentation for practical applications in diverse domains.

This project is my FinTech homework, incorporating hvPlot, PyVix, Plot.ly, Mapbox and some analysis of real-estate data, graphing of results, etc.. It uses a Jupyter Labs Notebook and my work builds on coursework that I am involved in.

Performed a partial and time series analysis on 19 years of real estate data provided by the City of Washington, District of Columbia. Analyzed 8 different wards in the city and 160,000 residential houses to determine the best area for future investment. Tools used were Python, Numpy and Pandas. Visualized all data in graphs and charts using Matplotlib.

The purpose of this module is it identify non-linear trends in historical prices for real estate properties in UK and to calculate parameters of non-linear formula (y = const + x1 × coeff). In order to achevie it, this Python made algorithm perform date parsing, data structuring with Pandas, data visualization with Matplotlib and Seaborn, for removing data noise (outliers) and prepare data for the calculatios uses ECDF, Simple Moving Average (SMA) function. Afterwards data cleaned and statistical parameters for linear equation is determining by Statmodels library. So finally we have nice and clear data graph corresponding SMA, cleaned data and what is the most important the line build based on Regression Analysis equation. © Vytautas Bielinskas