What Is The Difference Between Data Mining And Machine Learning

What Is The Difference Between Data Mining And Machine Learning – Spina was the most important port of Athens and the main partner in the northern Adriatic. Founded around 530 BC on an ancient branch of the Po River, it flourished for three centuries. Today, the archaeological site is about 12 kilometers from the sea, but it was once located at the mouth of the Delta, at the confluence of one of the main tributaries of the Po River with the dense secondary watercourses of the Apennines.

The settlement was discovered in the early 1960s during the reclamation works of the Valli di Comacchio. Despite decades of archaeological excavations, we only know a small part of the site.

What Is The Difference Between Data Mining And Machine Learning

Recent studies have revealed a rational urban model with orthogonal axes oriented north-south. The main arteries consist of wide waterways lined with long rows of columns and possibly covered by bridges and walkways. This regular network of canals created a Greek-style floor plan with standard rectangular blocks reminiscent of Etruscan settlements near Marzabotta and Forcello (Mantua) or settlements in Magna Graecia.

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Goods arrived in Spino from all over the Mediterranean. Wines, oils and treasures from Greece, creams and perfumes from the Middle East, amber from the Baltic Sea, building materials and daily life in the surrounding areas. into wine, transported in amphorae and consumed in the most valuable glass factories.

For nearly two centuries, Athens supplied Etruria with wine from Padania and the Alps, as well as with ceramic patterns and black glass. Fine exotic products such as wines, oils, creams, perfumes and spices came from the Aegean, Eastern Greece and Egypt, as evidenced by special vases and amphorae, not to mention glass and alabaster cream.

Greek and Oriental marble were also imported, while volcanic stone was used to make grinding wheels. Even the most common stones of the Apennines and the Alps were used for construction, weights, tools and tombstones.

From the mid-4th century BC trade routes shifted to the Italian peninsula, increasing trade with Greater Greece and Etruria.

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Coins are never accepted in Spina. Transactions took place through barter, printing units and bronze packages. Trading on Spina took place without currency, but with a complex weighing system. Bronze pieces (aes rudes), as well as rectangular pieces of thin ingots, were also used for transactions. The use of its proto-currency is indicated by its standard weight values, as in other modern trading cities in northern Italy.

Many stone weights have numerical markings that are seen as units of weight. The Etruscans knew different standards of weight, such as a light pound of 287 grams and a heavy pound of 358 grams. The latter is widespread in Spina, Padania and the interior of Etruria.

Thus, different systems of trade weight and regulation coexisted in Spina, which is another proof of its commercial and cultural openness.

Exports consisted mainly of raw materials, including grain and especially wheat grown throughout the hinterland. Wood, hides, livestock, especially pigs, were also obtained from the plains and nearby forests.

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Salt production was another traditional activity along the Adriatic coast, and it was extracted from seawater using complex methods. Salt was the basis of agriculture and dairying, food preservation and clothing tanning.

The most documented craft is the production of ceramics. Spina, which was located mostly in remote areas, had several clay workshops that could supply thousands of vessels to the local market. Traces of production include production remains, kiln tools and figurative seals, graffiti and letters used as trademarks.

The table also shows a typical mix of onion cultures. Archaeological finds speak of a diverse cuisine open to exotic eating habits. In the port of Spina, different culinary practices met and merged every day. Beginning in the 5th century BC, new cooking tools were imported from the Greek world.

Scientific analyzes show that a variety of vegetables, legumes and fruits were eaten on Spina. Harvests were also grown in nearby forests in addition to imports. Olive oil and wine, as well as vinegar, honey and spices were also necessary at the Spinetic table.

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Soups and stews of vegetables, legumes and meat were prepared in the kitchens with bread and bread. There is roasted meat from domestic and wild animals, as well as slices of fish, which are seasoned with sophisticated sauces.

Spina’s buildings are built from light materials, mainly wood, clay and marsh grass, in accordance with the lagoon environment and the use of natural resources. Architectural solutions were highly specialized. The older houses resembled log structures, where horizontal logs were joined at the corners with notches, but the newer houses have a technique with long rows of mud wall supports.

The roofs were built from plant material, and brick was used to a limited extent. Special colored plaster was used to insulate the walls of buildings from moisture. Domestic context. Excavations present a picture from 2,500 years ago.

Recent excavations have documented the history of the house between the end of the 5th century and the middle of the 3rd century BC. The building was surrounded by canals on three sides, reinforced with vertical columns and equipped with wooden corridors. Around 400 BC, the walls of the house were built with horizontal wooden beams and then plastered. Smaller rooms housed daily activities including spinning, weaving and cooking. A smaller channel separates the space protected by the roof of the boat.

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Inside the main room, next to the hearth, at the bottom of the pit, four valuable gold plates depicting three soldiers and a female figure were buried. They can be part of a small wooden box, hidden in a safe place.

The myth of the lost city. Spina disappears in 3 BC after only three centuries of existence. Several reasons are not yet fully understood. During 4 BC, political and commercial arrangements change in northern Italy as the Gauls invade, Syracuse expands, and Greek influence declines.

Even the environment was changing, as evidenced by ancient literary sources, which wrote about the gradual shifting of the coast during the Empire, it was already 15 kilometers further east compared to Etruscan times.

A military attack by the Celts is also possible. In fact, the upper floors revealed traces of fire and several clay bullets, incendiary projectiles fired by the besiegers.

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After the destruction of the city during the Roman period, only a scattered settlement survived. The former glory was now just a memory, hidden by mud and water. The myth of the lost city was born. Data science projects generally fall into two types: building machine learning models for specific purposes and mining data to discover new patterns and values ​​(R&D, so to speak). The first has a specific business purpose, such as creating a chatbot to help customer support. The latter has none and is looking for new business values.

This article explains a general data mining and exploratory data analysis (EDA) workflow based on the standard cross-industry data mining process, which is explained later.

Data mining is a set of processes and activities that can be used to find patterns and values ​​in a large set of data. Data mining includes extensive processes such as data collection, pre-processing, transformation, modeling and reporting. Its main goal is to find something new, patterns and values ​​that can be used in business.

Since data mining has many features, a data scientist must have extensive knowledge such as machine learning algorithms, data warehouse, Python/R programming pre-installation, visualization using specific tools.

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In this article, I will just explain the actual workflow of data mining so that you can understand the big picture of data mining as a first step. I’m sure if you clearly understand this workflow, it’s easy to understand what kind of knowledge and skills you need to collect data.

CRISP-DM (Cross-Industry Standard Process for Data Mining) is an open standard process model for data mining defined by ESPRIT, a European Union initiative managed by the Directorate-General for Industry in 1999. As the name suggests, CRISP-DM aims to define a standard data mining process that can be used across industries. CRISP-DM divides data mining processes into six steps.

As you can see in the figure, the process flows do not move strictly in one direction, but back and forth between stages. It starts with understanding the business and the data, then moves on to data preparation and modeling. After completing the modeling, he evaluates the results and decides whether to return to business understanding or implementation.

CRISP-DM is so well defined that it can be used as a load map in a data mining project.

What Is Data Extraction? Examples + Automation Tips

EDA (Exploratory Data Analysis) is one of the data analysis methods that can be used to summarize the characteristics of a data set with statistical numbers and graphs.

EDA was defined by the American mathematician John Tukey in 1961 based on statistical theory. With the rapid growth of machine learning and artificial intelligence technology, EDA is gaining a lot of attention as one of the best theories for data analysis.

EDA is usually performed before analyzing the underlying data to understand the current state (characters) of the data set. you may face problems

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