📹Project Video available on YouTube - https://youtu.be/q-Omt6LgRLc

🖇️Connect with me on LinkedIn - https://www.linkedin.com/in/rohit-grewal

London Housing Price Dataset

The dataset contains housing market information for different areas of London over time. It includes details such as average house prices, the number of houses sold, and crime statistics. The data spans multiple years and is organized by date and geographic area.

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

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

Q. 1) Convert the Datatype of 'Date' column to Date-Time format.

Q. 2.A) Add a new column ''year'' in the dataframe, which contains years only.

Q. 2.B) Add a new column ''month'' as 2nd column in the dataframe, which contains month only.

Q. 3) Remove the columns 'year' and 'month' from the dataframe.

Q. 4) Show all the records where 'No. of Crimes' is 0. And, how many such records are there ?

Q. 5) What is the maximum & minimum 'average_price' per year in england ?

Q. 6) What is the Maximum & Minimum No. of Crimes recorded per area ?

Q. 7) Show the total count of records of each area, where average price is less than 100000.

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

1) Date – The month and year when the data was recorded.

2) Area – The London borough or area for which the housing and crime data is reported.

3) Average_price – The average house price in the given area during the specified month.

4) Code – The unique area code (e.g., government statistical code) corresponding to each borough or region.

5) Houses_sold – The number of houses sold in the given area during the specified month.

6) No_of_crimes – The number of crimes recorded in the given area during the specified month.

The global network copyright market is booming, projected to reach $180B+ by 2033 with a 14.5% CAGR. Learn about key drivers, trends, and challenges impacting Netflix, YouTube, Tencent, and other major players in this dynamic industry. Discover insights on regional market share and future growth.

As of October 2025, the mean purchase price of housing in Seoul, South Korea, amounted to around *** million South Korean won. The average price of apartments amounted to around **** billion won, while the price of detached houses was about **** billion South Korean won. Apartments in South Korea Among all housing types, apartments are the most expensive, costing more than *** billion South Korean won on average. Living in apartments is typical for Seoul, as an increasing number of citizens move towards the city, causing high population density. As of 2022, more than ** percent of all households were living in apartments, excluding alternative housing, such as officetels or goshiwons. Gangnam Style Based on the average selling price of apartments in Seoul, Gangnam is the most expensive area in Seoul to live in, with an average sales price of around **** billion South Korean won. The area became internationally known due to the viral YouTube hit Gangnam Style by South Korean artist PSY. Since Gangnam is known for its wealthy citizens, the song was inspired by their mannerisms.

In March 2024, YouTube.com was the leading video streaming website in the United States. During the measured period, the video platform accounted for over 75.02 percent of desktop traffic in the arts & entertainment - TV, movies, and streaming subcategory. Netflix was ranked second with a 5.23 percent market share.

Dubai, UAE Real Estate Market Dataset

👨‍💻 Author: Azhar Saleem

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Dataset Description

This comprehensive dataset provides an exhaustive snapshot of property listings for sale across the United Arab Emirates, including major cities like Dubai, Abu Dhabi, and Al Ain. Sourced from Bayut.com, this dataset serves as an invaluable resource for Data Scientists, Real Estate Analysts, Urban Planners, and Developers keen on exploring real estate market dynamics, price fluctuations, and development trends in the UAE.

Dataset Overview

The dataset contains over 41,000 entries, each representing a unique property for sale. It includes detailed information such as:

• Price: Listing price of the property in AED.
• Type: Specifies the property type, such as Apartment, Townhouse, etc.
• Beds: Number of bedrooms, with '0' indicating a studio flat.
• Baths: Number of bathrooms.
• Address: Full address of the property, providing insights into its precise location.
• Furnishing: Indicates whether the property is furnished or unfurnished.
• Completion Status: Current status of the property (Ready, Off-Plan).
• Building Name, Area Name, City: Provide contextual location details.
• Year of Completion: Year when the property was completed or is expected to be completed.
• Total Floors, Parking Spaces, Building Area: Key features of the property's building.
• Latitude, Longitude: Geographic coordinates for more refined location analysis.
• Purpose: Intended purpose of the listing, consistently noted as 'For Sale'.

Usage

This dataset is ideal for a variety of applications, including:

• Market Analysis: Analyze trends in property prices and types across different regions.
• Predictive Modeling: Develop machine learning models to predict property prices or to classify types of properties based on their features.
• Urban Development Studies: Examine property distribution and characteristics to inform urban planning and development strategies.
• Comparative Analysis: Compare properties across different cities and districts to identify investment opportunities or to study market behavior.

Data Accessibility

This dataset is publicly available and well-suited for anyone interested in conducting detailed analyses of the UAE real estate market, from academic researchers to industry professionals.

Feel free to dive into this dataset to unlock comprehensive insights into the vibrant and diverse property market of the UAE, supporting a wide range of real estate, economic, and geographic studies.

👨‍💻 Author: Azhar Saleem

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Dataset Overview

This dataset encompasses a comprehensive collection of property listings from Zameen.com, Pakistan's largest real estate website. It contains detailed information on properties for sale across Pakistan, making it a vital resource for data scientists, machine learning engineers, and analysts interested in the real estate market, economic trends, or geographical data analysis.

Columns Description

• url: The webpage URL for the property listing.
• title: The title of the property listing, describing key features.
• type: The type of property (e.g., House, Apartment).
• price: The listed price of the property in PKR.
• area: The total area of the property listed in local units (Marla, Kanal).
• city: The city in which the property is located.
• address: A more specific location or address within the city.
• bedrooms: The number of bedrooms in the property.
• baths: The number of bathrooms in the property.
• area_sqft: The area of the property in square feet.
• price_per_sqft: The price of the property per square foot.
• area_sqm: The area of the property in square meters.
• price_per_sqm: The price of the property per square meter.
• Latitude: Geographical latitude of the property.
• Longitude: Geographical longitude of the property.
• date_added: The date when the property was added to the website.

This dataset is ideal for conducting various types of analysis, such as market price predictions, trend analysis, and geographical data visualization, among others.

The global network copyright market is booming, projected to reach $119.47 billion by 2033, with a 13.9% CAGR. Learn about key drivers, trends, and regional market shares for video copyright protection across platforms like Netflix, YouTube, and Tencent. Explore the impact of AI, blockchain, and evolving regulations on this rapidly expanding sector.

This is not going to be an article or Op-Ed about Michael Jordan. Since 2009 we've been in the longest bull-market in history, that's 11 years and counting. However a few metrics like the stock market P/E, the call to put ratio and of course the Shiller P/E suggest a great crash is coming in-between the levels of 1929 and the dot.com bubble. Mean reversion historically is inevitable and the Fed's printing money experiment could end in disaster for the stock market in late 2021 or 2022. You can read Jeremy Grantham's Last Dance article here. You are likely well aware of Michael Burry's predicament as well. It's easier for you just to skim through two related videos on this topic of a stock market crash. Michael Burry's Warning see this YouTube. Jeremy Grantham's Warning See this YouTube. Typically when there is a major event in the world, there is a crash and then a bear market and a recovery that takes many many months. In March, 2020 that's not what we saw since the Fed did some astonishing things that means a liquidity sloth and the risk of a major inflation event. The pandemic represented the quickest decline of at least 30% in the history of the benchmark S&P 500, but the recovery was not correlated to anything but Fed intervention. Since the pandemic clearly isn't disappearing and many sectors such as travel, business travel, tourism and supply chain disruptions appear significantly disrupted - the so-called economic recovery isn't so great. And there's this little problem at the heart of global capitalism today, the stock market just keeps going up. Crashes and corrections typically occur frequently in a normal market. But the Fed liquidity and irresponsible printing of money is creating a scenario where normal behavior isn't occurring on the markets. According to data provided by market analytics firm Yardeni Research, the benchmark index has undergone 38 declines of at least 10% since the beginning of 1950. Since March, 2020 we've barely seen a down month. September, 2020 was flat-ish. The S&P 500 has more than doubled since those lows. Look at the angle of the curve: The S&P 500 was 735 at the low in 2009, so in this bull market alone it has gone up 6x in valuation. That's not a normal cycle and it could mean we are due for an epic correction. I have to agree with the analysts who claim that the long, long bull market since 2009 has finally matured into a fully-fledged epic bubble. There is a complacency, buy-the dip frenzy and general meme environment to what BigTech can do in such an environment. The weight of Apple, Amazon, Alphabet, Microsoft, Facebook, Nvidia and Tesla together in the S&P and Nasdaq is approach a ridiculous weighting. When these stocks are seen both as growth, value and companies with unbeatable moats the entire dynamics of the stock market begin to break down. Check out FANG during the pandemic. BigTech is Seen as Bullet-Proof me valuations and a hysterical speculative behavior leads to even higher highs, even as 2020 offered many younger people an on-ramp into investing for the first time. Some analysts at JP Morgan are even saying that until retail investors stop charging into stocks, markets probably don’t have too much to worry about. Hedge funds with payment for order flows can predict exactly how these retail investors are behaving and monetize them. PFOF might even have to be banned by the SEC. The risk-on market theoretically just keeps going up until the Fed raises interest rates, which could be in 2023! For some context, we're more than 1.4 years removed from the bear-market bottom of the coronavirus crash and haven't had even a 5% correction in nine months. This is the most over-priced the market has likely ever been. At the night of the dot-com bubble the S&P 500 was only 1,400. Today it is 4,500, not so many years after. Clearly something is not quite right if you look at history and the P/E ratios. A market pumped with liquidity produces higher earnings with historically low interest rates, it's an environment where dangerous things can occur. In late 1997, as the S&P 500 passed its previous 1929 peak of 21x earnings, that seemed like a lot, but nothing compared to today. For some context, the S&P 500 Shiller P/E closed last week at 38.58, which is nearly a two-decade high. It's also well over double the average Shiller P/E of 16.84, dating back 151 years. So the stock market is likely around 2x over-valued. Try to think rationally about what this means for valuations today and your favorite stock prices, what should they be in historical terms? The S&P 500 is up 31% in the past year. It will likely hit 5,000 before a correction given the amount of added liquidity to the system and the QE the Fed is using that's like a huge abuse of MMT, or Modern Monetary Theory. This has also lent to bubbles in the housing market, crypto and even commodities like Gold with long-term global GDP meeting many headwinds in the years ahead due to a...

Dubai, UAE Rental Properties Dataset

👨‍💻 Author: Azhar Saleem

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This dataset presents a comprehensive overview of rental property listings across multiple cities in the United Arab Emirates, including Abu Dhabi, Dubai, Sharjah, Ajman, Ras Al Khaimah, Umm Al Quwain, and Al Ain. Compiled from bayut.com, it is a valuable resource for Data Analysts, Data Scientists, and Researchers looking to explore real estate trends, rental pricing patterns, or urban development studies in the UAE.

Dataset Overview

Each entry in the dataset represents a rental property listing with details about the property's features, rental terms, and location specifics. This primary and unique dataset is designed for analysis and can be used to generate insights into the rental market dynamics of the UAE.

Columns Description

• Address: Full address of the property.
• Rent: The annual rent price in AED.
• Beds: Number of bedrooms in the property.
• Baths: Number of bathrooms in the property.
• Type: Type of property (e.g., Apartment, Villa, Penthouse).
• Area_in_sqft: Total area of the property in square feet.
• Rent_per_sqft: Rent price per square foot, calculated as Rent divided by Area_in_sqft.
• Rent_category: Categorization of the rent price (Low, Medium, High) based on thresholds.
• Frequency: Rental payment frequency, which is consistently 'Yearly'.
• Furnishing: Furnishing status of the property (Furnished, Unfurnished).
• Purpose: The purpose of the listing, typically 'For Rent'.
• Posted_date: The date the property was listed for rent.
• Age_of_listing_in_days: The number of days the listing has been active since it was posted.
• Location: A more specific location within the city where the property is located.
• City: City in which the property is situated.
• Latitude, Longitude: Geographic coordinates of the property.

Usage

This dataset is open for public use and is particularly suited for:

• Analyzing trends in the rental market.
• Studying the geographical distribution of rental properties.
• Comparing rental prices across different cities and property types.
• Developing machine learning models to predict rental prices or classify property types.

Feel free to explore this dataset and derive meaningful insights to understand the dynamics of the UAE rental market.

Explore the thriving Network Copyright market, projected to reach $368 million by 2033 with a strong CAGR of 15%. Discover key drivers, restraints, and growth opportunities in copyright protection for businesses and individuals in the digital age.

The U.S. home organization products market is valued at USD 11.79 Billion in 2023 and is projected to reach USD 16.15 Billion by 2030, registering a CAGR of 4.6% for the forecast period 2023-2030. Driving factor of the Home Organization Products

Rising awareness about benefits of home organization products

The benefits of Home Organization Products play a significant role in driving the Home Organization Products market. These benefits make organization products attractive to consumers and encourage them to invest in such products.

Benefits of home organization products:

Improved Efficiency

Products for home organizing make it easier for people to find and access goods. Time is saved, and the frustration of looking for items is reduced.

Maximized Space

They utilize vertical and covert storage options to maximize space, especially in tiny homes or flats.

Reduced Clutter

Stress and a sense of disorder can be brought on by clutter. Products for home organizing assist keep possessions orderly, reducing clutter and fostering a more relaxing environment.

Improved Aesthetics

Numerous organizational goods are made with aesthetics in mind, which contributes to making a house that is more aesthetically pleasant.

Increasing awareness about these products coupled with marketing strategies increasing adoption of home organization products worldwide. For instance, YouTube has become powerful platform in today’s world due to the growing penetration of internet in developing as well as developed countries. Many companies and youtubers are using these platforms to introduce, demonstrate the home organization products usability etc. In addition, DIY products videos becoming popular day by day among users.

For instance, IKEA has its YouTube channel with more than 271K subscribers assist its customers through its video of different products with real life use and appearance. 

(Source:www.youtube.com/@IKEA/videos)

Rising urbanization and growing residential construction in developing region

Rising urbanization and growing residential construction have a significant positive impact on the home organization products market. Urbanization often leads to smaller living spaces, such as apartments and condos, due to the high population density in cities. This limitation of space creates a need for efficient storage and organization solutions. Home organization products, such as compact shelving units, closet organizers, and space-saving furniture, become essential for urban dwellers who want to maximize their available living space.

According to the Statista; approximately a third of the total population in India lived in cities. The trend shows an increase of urbanization by almost 4 percent in the last decade, meaning people have moved away from rural areas to find work and make a living in the cities.

(Source:www.statista.com/statistics/271312/urbanization-in-india/#:~:text=Urbanization%20in%20India%202021&text=In%202021%2C%20approximately%20a%20third,a%20living%20in%20the%20cities.)

According to estimates from the World Population review, India’s population reached 1.4 billion people in the end of 2022. 

(Source:frontline.thehindu.com/news/explained-india-now-has-14-billion-people-what-does-it-mean-for-their-health/article66575826.ece)

According to the Statista; in 2022, approximately 65.2 percent of the total population in China lived in cities. The urbanization rate has increased steadily in China over the last decades.

(Source:www.statista.com/statistics/270162/urbanization-in-china/)

In 2015, Indian government has launched Pradhan Mantri Awas Yojana (Urban) Mission, which intends to provide housing for all in urban areas by year 2022. The Mission provides Central Assistance to the implementing agencies through States/Union Territories (UTs) and Central Nodal Agencies (CNAs) for providing houses to all eligible families/ beneficiaries against the validated demand for houses. This encourages the residential constructions in India. 

(Source:pmaymis.gov.in/)

The demand for efficient and space-saving organization solutions in urban areas is on the rise, driving the need for a wide range of home organization products.

Rising focus and implementation of minimalism in people's daily lifestyle

Restraining Factor of the Home Organization Products

Competitive market

The market for home or...