[M] minor update README

README.md

@@ -15,7 +15,9 @@ Finding a real estate in a new city is still a challenge. We often arrive in a c
 
 ### Installation
 
-For installing Predihood, go in the `predihood/` directory and run in a terminal:
+For installing Predihood, clone or download this git repository.
+
+Go in the downloaded `predihood/` directory (which contains `setup.py`) and run in a terminal:
 
 ```
 python3 -m pip install -e . -r requirements.txt
@@ -23,7 +25,7 @@ python3 -m pip install -e . -r requirements.txt
 
 This command install dependencies, including [mongiris](https://gitlab.liris.cnrs.fr/fduchate/mongiris), a lightweight API which enables the querying of the MongoDB database containing information about French neighbourhoods.
 
-Next, to install the database, execute this command (from the MongoDB's executables directory if needed):
+Next, to install the database, run the MongoDB server and execute this command (from the MongoDB's executables directory if needed):
 
 ```
 ./mongorestore --archive=/path/to/dump-iris.bin
@@ -33,7 +35,7 @@ where `/path/to/` is the path to the dump file of the IRIS collection (provided
 
 ### Run Predihood
 
-For running *Predihood*, go in the `predihood/predihood/` directory and run in a terminal:
+For running *Predihood*, go in the `predihood/predihood/` directory (which contains `main.py`) and run in a terminal:
 
 ```
 python3 main.py
@@ -51,9 +53,9 @@ For the cartographic interface, an example would be:
 
 For the algorithmic interface, an example would be:
 
-1. Select an algorithm in the list.
-2. Configure your algorithm as desired by tuning available options.
-3. Click on "Train, test and evaluate" button. After execution, a table summarizes results for each environment variable and each list of indicators. 
+1. Choose an algorithm 
+2. Tune it as desired
+3. Click on "Train, test and evaluate" button. When computing accuracies is done, a table shows results for each environment variable and each list of indicators. 
 
 
 ## Tests