unit 2 added

unit 2/02_Data_Science_Process.md

@@ -0,0 +1,37 @@
+# Standard Process for Data Science (CRISP-DM)
+
+**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.
+
+It has **6 Phases**:
+
+## 1. Business Understanding
+**Goal**: Define what problem we are trying to solve.
+- **Example**: An online retailer wants to classify items as "High Demand" or "Low Demand".
+- **Questions**: Is item type related to demand? Can we predict demand accurately?
+
+## 2. Data Understanding
+**Goal**: Get to know the data.
+- **Example**: Looking at the inventory data (orders, item type).
+- **Insight**: Knowing if items are perishable (like milk) or non-perishable helps understand stock needs.
+
+## 3. Data Preparation
+**Goal**: Clean and format the data for the model.
+- **Steps**:
+  - Handle missing values.
+  - Convert categories to numbers (dummy encoding).
+  - Check for connections (correlation) between variables.
+
+## 4. Modeling
+**Goal**: Build the machine learning model.
+- We try to find a function that connects inputs (like number of orders) to the output (demand).
+- We might try different models to find the best one.
+
+## 5. Evaluation
+**Goal**: Check how good the model is.
+- We test the model on **unseen data** (data it hasn't seen before).
+- We compare the **predicted** values with the **actual** values.
+
+## 6. Deployment
+**Goal**: Use the model in the real world.
+- If the model is good, we put it to work.
+- **Example**: Create an app where the retailer enters item details and gets a demand prediction.