38 lines
1.5 KiB
Markdown
38 lines
1.5 KiB
Markdown
# Standard Process for Data Science (CRISP-DM)
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**CRISP-DM** stands for **Cr**oss **I**ndustry **S**tandard **P**rocess for **D**ata **M**ining. It is a standard way to do data mining projects.
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It has **6 Phases**:
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## 1. Business Understanding
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**Goal**: Define what problem we are trying to solve.
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- **Example**: An online retailer wants to classify items as "High Demand" or "Low Demand".
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- **Questions**: Is item type related to demand? Can we predict demand accurately?
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## 2. Data Understanding
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**Goal**: Get to know the data.
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- **Example**: Looking at the inventory data (orders, item type).
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- **Insight**: Knowing if items are perishable (like milk) or non-perishable helps understand stock needs.
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## 3. Data Preparation
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**Goal**: Clean and format the data for the model.
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- **Steps**:
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- Handle missing values.
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- Convert categories to numbers (dummy encoding).
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- Check for connections (correlation) between variables.
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## 4. Modeling
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**Goal**: Build the machine learning model.
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- We try to find a function that connects inputs (like number of orders) to the output (demand).
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- We might try different models to find the best one.
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## 5. Evaluation
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**Goal**: Check how good the model is.
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- We test the model on **unseen data** (data it hasn't seen before).
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- We compare the **predicted** values with the **actual** values.
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## 6. Deployment
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**Goal**: Use the model in the real world.
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- If the model is good, we put it to work.
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- **Example**: Create an app where the retailer enters item details and gets a demand prediction.
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