Data classification and clustering are essential techniques in data science that help categorize large datasets and interpret them to make informed decisions. Using Python, we can easily perform these tasks through various libraries like NumPy, scikit-learn, and pandas. Data classification implies dividing data into distinct categories based on similar characteristics or attributes, whereas clustering involves the grouping of data points that have certain similarities. By employing Python for data classification and clustering tasks we can visually interpret complex datasets using graph-based visualizations, determining correlations between different variables in our dataset. Additionally, Machine Learning models working together with python algorithms can enhance the process of accurate pattern detection within enormous amounts of data to discover business insights. Henceforth as an easier tool for solving sophisticated problems related to big sets of information, Python is undoubtedly a valuable skill set for any aspiring Data Scientist or Developer alike. In today’s post, I

Do you know how experts classify wines? Of course, the first parameter is the taste of the drink. But there are more than a dozen other parameters that determine a wine’s class. Wine composition can differ significantly depending on several factors, such as grape variety, growing region, climate, soil type and winemaking techniques. Different grape varieties have distinct qualities that affect the taste of wine. For example, Cabernet Sauvignon is known for its full-bodied flavor with hints of blackcurrant and violet whereas Chardonnay has a light-bodied nature with flavors of green apple and citrus fruits. The growing conditions in various regions have an impact on the acidity levels present in the grapes. While cooler regions produce acidic wines, warmer climates tend to produce sweeter ones. Winemakers control the fermentation process which also affects the complexity of wine. Whether they choose to age it in oak barrels or stainless-steel tanks will

Is it possible to predict real estate prices only by indirect parameters? Not by comparing prices with other houses in the neighborhood? Yes, that’s possible. There are a lot of different factors that can affect the price of a property, such as location, size, age, condition, the ecology of the area, the closeness of schools and highways, the crime rate and so on. If we have all this data, we can determine the price of houses with a high degree of certainty. However, when it comes to predicting these prices with accuracy, it’s not always a straightforward task. While real estate experts use various methods like comparative market analysis and appraisals to determine the value of a property based on features and area trends, there are many more variables at play than just these. Things like market volatility and economic changes can also have an impact on real estate prices

How much do you think waiters earn in tips? According to research, waiters in many countries can make a significant portion of their income from tips. The exact amount they earn depends on many factors such as location, popularity of the restaurant, quality of service and the overall value that customers perceive from tipping. Generally speaking, most waiters in casual establishments earn tips worth 10-15% of the total bill or more. In higher end restaurants, 15-20%, or even more, is typically expected by customers. Of course, this depends on individual experiences and some people tip more than others for various reasons. Overall, we can expect waiters to bring home a decent supplement to their wages just from tips alone! Many factors influence the size of the tip, such as the generosity of the customer, the presence of the lady, the number of people at the table, the day of the

Among all the items of luxury and well-being, diamonds have always stood in the foreground. These stones are a symbol of success and foresight. Today diamonds are a big business, and each year, millions of carats are purchased around the world for both industrial as well as personal use. According to estimates from 2020, over 200 million carats of diamonds were sold annually worldwide representing an extremely high value of almost $14 billion. The main markets contributing to these figures were led by the United States who purchased nearly 43 million carats at a personal cost of over $6 billion a year. India, Belgium and the United Arab Emirates followed close behind with all spending approximately the same amount yearly on diamond purchases. The diamond market is also a market with a high entry price and its own rules. If a person does not know how to evaluate diamonds correctly,

Did you know that the range of minimum and maximum prices of Google shares reaches half of their value? With so much volatility, there is a high probability of buying a stock and waiting for it to rise for several years. That’s why it’s important to analyze all of its parameters before buying a stock. And it’s not just about closing prices. Because stock price fluctuations within a day can be as much as 5 percent or more. Stock market analysis is a frequent situation in the Data Science industry. There are a huge number of methods and libraries for both analyzing and visualizing data, as well as building predictions using machine learning methods. In today’s post, I will show you how I analyzed Google stock prices over the past 12 months. Using the Python programming language and its many libraries, the following work was done: Exploratory data analysis; Visualizing

Probably every Instagram blogger strives to have as many followers, likes, comments, post impressions as possible. But what is behind all these terms? And how do they affect each other? The algorithms of Instagram (like any other major social network) are kept in the strictest secret. However, with some wit and experience with Python you can identify some regularities, understand which parameter affects the coverage of your posts more and which less. In addition, don’t forget the power of machine learning with Python. Using Instagram data skillfully, you can build predictive models and have a high probability of knowing in advance how much reach you can expect for your posts. In this post I will show you an example of how I do it. Links Google Colab (Python)

This is one of my first works on machine learning, but one of the most visual and easy to understand. In this study, I analyzed a simple dataset of Iris flowers, their properties and sizes. Then I built several prediction models for these parameters and chose the most optimal one. Instruments Google Collaboratory; Python and its libraries (Pandas, Scipy, Numpy, Matplotlib, Sklearn) Links https://colab.research.google.com/