Merge pull request #111 from kvishvanathan/main

Fix a typo and done some amendments to the code

3-Web-App/1-Web-App/README.md

@@ -167,7 +167,7 @@ Now you can build a Flask app to call your model and return similar results, but
         css/
         templates/
     notebook.ipynb
-    ufo-model.pk1
+    ufo-model.pkl
     ``` 
 
    ✅ Refer to the solution folder for a view of the finished app

4-Classification/4-Applied/README.md

@@ -31,7 +31,7 @@ First, train a classification model using the cleaned cuisines dataset we used.
 1. Start by importing useful libraries:
 
     ```python
-    pip install skl2onnx
+    !pip install skl2onnx
     import pandas as pd 
     ```
 

5-Clustering/1-Visualize/README.md

@@ -104,7 +104,7 @@ Clustering as a technique is greatly aided by proper visualization, so let's get
 1. Import the `Seaborn` package for good data visualization.
 
     ```python
-    pip install seaborn
+    !pip install seaborn
     ```
 
 1. Append the song data from _nigerian-songs.csv_. Load up a dataframe with some data about the songs. Get ready to explore this data by importing the libraries and dumping out the data: