Update readme

README.md

@@ -7,5 +7,61 @@ sdk: docker
 app_port: 7860
 pinned: false
 ---
+## ML API
+A FastAPI enpoint serving a fitted sklearn pipeline with an ordinal logistic regression model using the 
+<a href="https://pypi.org/project/mord/">Mord Python Package</a> to predict customer's "small quantity order importance ranking (1-10)." 
+
+#### Pipeline Steps
+1. Column Transformer<br>
+        a. Standard Scaling for numerical variables<br>
+        b. One-hot-encoding for categorical variables
+2. Feature Selection<br>
+        a. Lasso Regression
+3. Model <br>
+        a. Mord Ordinal Logistic Regression
+
+The fitted pipeline is then serialized with joblib, served with Fast API (Uvicorn), containarized with Docker, and finally deployed to HuggingFace Spaces. 
+
+Prediction requests can be sent to https://dkondic-ml-api.hf.space/predict as a list of dictionaries where each dictionary is an instance to predict. Thus, prediction is possible for single instance or batch of instances. Please see <a href="https://dkondic-ml-api.hf.space/">ML API Docs</a> for more indormation.<br> 
+
+#### Request Body
+```
+[
+  {
+    "CUST_NBR": "string",
+    "MENU_TYP_DESC": "string",
+    "PYR_SEG_CD": "string",
+    "DIV_NBR": "string",
+    "WKLY_ORDERS": 0,
+    "PERC_EB": 0,
+    "AVG_WKLY_SALES": 0,
+    "AVG_WKLY_CASES": 0
+  }
+]
+```
+#### Resonse Body
+```
+{
+  "prediction": [
+    0
+  ]
+}
+```
+#### Prediction xample using Python requests
+```py
+import requests
+
+data = [
+        {"CUST_NBR":"1111",
+         "MENU_TYP_DESC":"MEXICAN",
+         "PYR_SEG_CD":"Education",
+         "DIV_NBR":"20",
+         "WKLY_ORDERS": 15,
+         "PERC_EB":0.80,
+         "AVG_WKLY_SALES":2656.04,
+         "AVG_WKLY_CASES":67.00}]
+
+response = requests.post("https://dkondic-ml-api.hf.space/predict", json=data)
+print(response.json())
+```
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference