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Yako Nongedzo kuDhata Mining: Chii, Nei, & Sei?

Data Mining refers to discovering valuable knowledge out of huge clusters of data to infer patterns. Data Mining is the result of the proliferation of Computing Technology which has enabled to collect, store and process humongous data. The Pre-processing, Data Mining and Results Validation are the three steps which lead to Knowledge Discovery In Database.

When there exists plethora of data, targeting the data which will be relevant for you is of prime importance. Data mining can work only if the data available is huge enough for the patterns to be deduced and is concise enough for making it possible to be handled within a specific time limit. The source of data for pre-processing is Dhata Warehouse, uko data rakaunganidzwa kubva kunzvimbo dzakasiyana. Iyo data iriko inoitwa yekucheneswa kuitira kuti mhando irege kukanganiswa.

There are six phases according to CRISP-DM, which is the standard Data Mining Process:

1. Business Understanding – A framework is made keeping in mind the objectives of the business. Keeping in mind the problems in business, a date kuchera problem definition is framed.

2 .. Kunzwisisa Dhata- Dhata inoongororwa uchishandisa chinyakare chishandiso senge manhamba ekutsvaga zvivakwa, kurongeka, uye kukwana kwedata.

3. Kugadzirira kweData- Sezvo mamwe emabasa emigodhi achigamuchira dhata mune mamwe mafomati, inocheneswa nekushandurwa kuti ive yakakodzera kuidyisa kumathurusi ekuenzanisira.

4. Modeling- It’s the experimental phase in which various modeling techniques are applied as there are several techniques for the same date kuchera problem type.

5. Kuongorora- Iyo modhi inoongororwa kuti iongorore mhando yayo kuitira kuti igone kupedziswa kuti iyo modhi iyo yakagadzirirwa inoenderana nezvinodiwa kubva mumaonero ebhizinesi.

6. Kutumirwa - Ruzivo rwunowanikwa rwunoiswa mukugadzirwa uye rwakarongedzwa uye rwunoratidzwa nenzira inogona kunge iri yekumwe kushandiswa kumutengi.

Matekiniki eData Mining

Sangano- Muenzaniso unowanikwa nekuenzanisa izvo zvinhu zvinobatanidzwa mukutenga kumwechete panguva yebhizinesi. Iyi nzira inoshandiswa muMarket Basket Analysis kuongorora maitiro ekutenga evatengi.

Kupatsanura- Iyi nzira inoenderana ne Machine Learning. Iyo data inoongororwa nekuisa muzvikamu zvakasiyana. Semuenzaniso muExpress e-mail, mamwe maalgorithms anoshandiswa kuratidza iwo kunge ari pamutemo kana spam. Kana kana mukuru webhangi wechikwereti achida kuziva kuti ndeupi mutengi ane njodzi kana akachengeteka.

Kuunganidza- Iyo hunyanzvi yekuronga zvinhu zvakafanana. Iyo inoshandiswa muminda yakawanda senge machine learning, kuziva pateni, kuongorora kwemifananidzo, kudzosa ruzivo, bioinformatics, kudzvinyirira dhata, uye mifananidzo yemakomputa.

Kudzvinyirira-Iyi nzira inoshandiswa kufanotaura hukama pakati pezviviri kana kupfuura zvinoshanduka. Linear Regression inzira inoshandiswa zvakanyanya kumisikidza hukama pakati pechinhu chakatarisana neshanduko yakazvimirira. Semuenzaniso- regression basa rinogona kushandiswa kufanotaura kukosha kweimba zvichibva nenzvimbo, kuwanda kwemakamuri, nezvimwe.

Kukosha kweData Mining Nhasi

Data mining can help you to understand the behavior of your customers and earn substantial profits by reducing the churn rate. It’s importance can be seen in several fields. Nezveutano, E-commerce, Kushambadzira, Dzidzo, Kugadzira Injiniya, Kwevatengi Ukama Management, Banking, uye Bioinformatics kungodudza mashoma.

Ngationgororei kukosha kwayo mune mimwe minda:

Nezveutano 

Data Mining in Nezveutano helps in making more accurate diagnosis and reduce costs. It can help in analyzing the inefficiencies, give targeted treatment to patients, help in reducing medical errors, provide thorough documentation and improve patient care and satisfaction. A research from EMC2 and IDC states that nezveutano data is growing at an annual rate of 48 muzana. Kushandisa zvirinani kwedatha kunobatsira mukuita sarudzo dzakadzikiswa mumitengo yakaderera.

E-commerce

Data Mining in e-zvokutengeserana inobatsira mukunzwisisa maitiro emutengi. Nekuongorora maitiro ehunhu, marongero anochinjiswa zvinoenderana kuti anyengetedze mutengi kuti atenge zvimwe. Iwo mafomu ekushandisa kubva mukutsvaga kwechigadzirwa, kurudziro yechigadzirwa, kubiridzirwa kwekubiridzira, uye business intelligence.

dzidzo

Educational Data Mining in education helps in understanding the future learning behavior of students, what to teach, how to teach and advance in scientific knowledge about learning. It also helps in understanding the settings in which the students learn and the motivation behind the learning.

We at NewGenApps have an expertise in Data Mining. To know how it can supplement your business, get in touch.

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