styling

README.md

@@ -4,27 +4,27 @@
 
 This data analysis and visualition project was triggered by a [data-upskilling](https://benjamin-dubreu.systeme.io/programme-data-upskilling) training course I recently followed to acquaint myself with tools commonly used by data scientists. With this project, I used and combined all the different concepts I learned in the class. The work was divided in four different routes:
 
-    1. Creating an API that generates data(*).
-    2. Querying that interface to extract the data required by the project.
-    3. Processing the data to address the client's needs.
-    4. Visualising the data in an online dynamic environment.
+1. Creating an API that generates data(*).
+2. Querying that interface to extract the data required by the project.
+3. Processing the data to address the client's needs.
+4. Visualising the data in an online dynamic environment.
 
 (*) In a real life situation, this step would be skipped as the client would provide the data.
 
-**Skills involved**: Python, SQL, Bash, Git, Airflow, Object-oriented programming, Unit testing, API development, Data extraction, Data analysis, Data visualisation, streamlit cloud
+    **Skills involved**: Python, SQL, Bash, Git, Airflow, Object-oriented programming, Unit testing, API development, Data extraction, Data analysis, Data visualisation, streamlit cloud
 
-**Languages and packages used**: fastapi, pandas, datetime, numpy.random, unittest, uvicorn, requests, duckdb, streamlit, venv
+    **Languages and packages used**: fastapi, pandas, datetime, numpy.random, unittest, uvicorn, requests, duckdb, streamlit, venv
 
 
 
 ## The general idea
 
 The client owns several stores in major European cities that he/she opened in the last decade. The client needs to know how many people frequent the different stores and detect long-term trends. The data available are sensor data that count how many people enter a store per hour. A store can have several doors, and thefore, several streams of sensor data. With his/her initial request, the client wants an interface where he/she can easily navigate the sensor data from the different stores. In particular, he/she wants the data at four different resolutions:
 
-    - Hourly data at the sensor level.
-    - Hourly data at the store level.
-    - Daily data at the sensor level.
-    - Daily data at the store level.
+- Hourly data at the sensor level.
+- Hourly data at the store level.
+- Daily data at the sensor level.
+- Daily data at the store level.