installation steps chnaged to code

Author: Supratim Banik

samples/04_gis_analysts_data_scientists/cloud_detector_part1_cloudless_sentinel_&_unsupervised.ipynb.ipynb renamed to samples/04_gis_analysts_data_scientists/cloud_detector_part1_cloudless_sentinel_&_unsupervised.ipynb

@@ -52,7 +52,7 @@
    "source": [
     "This notebook describes the different ways cloud masks can be generated from satellite imagery, in this instance using sentinel imagery.\n",
     "\n",
-    "Cloud presence causes problems for remote sensing analysis of surface properties, for instance in analyses like land use and land cover classification, image compositing, or change detection . In cases of single scene image processing, it is relatively easy to manually filter out clouds; however, for studies that use a larger number of images, an automated approach for removing or masking out clouds is necessary.\n",
+    "Cloud presence causes problems for remote sensing analysis of surface properties, for instance in analyses like land use and land cover classification, image compositing, or change detection. In cases of single scene image processing, it is relatively easy to manually filter out clouds; however, for studies that use a larger number of images, an automated approach for removing or masking out clouds is necessary.\n",
     "\n",
     "In parts 1 and 2 of this notebook series, we will demonstrate three methods of cloud mask extraction:\n",
     "\n",
@@ -113,8 +113,16 @@
    "id": "1a7afe18",
    "metadata": {},
    "source": [
-    "The Sentinel cloudless package can be installed using the following commands:\n",
-    "\n",
+    "The Sentinel cloudless package can be installed using the following commands:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "855756a4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
     "pip install sentinelhub --upgrade\n",
     "\n",
     "pip install s2cloudless"
@@ -1491,10 +1499,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a2056d93",
+   "id": "2b2774fb",
    "metadata": {},
    "source": [
-    "Here the imagery name is 'rgbnir_iowa.tif', following which the name of the 4 bands of blue, green, red, and near infrared bands would be 'small_rgb_iowa3','small_rgb_iowa3_1', 'small_rgb_iowa3_2', 'small_rgb_iowa3_3' respectively. These bands will be used for defining the preprocessors."
+    "Here, the imagery name is rgbnir_iowa.tif, and the name of the 4 bands of blue, green, red, and near infrared are small_rgb_iowa3, small_rgb_iowa3_1, small_rgb_iowa3_2, small_rgb_iowa3_3 respectively. These bands will be used for defining the preprocessors."
    ]
   },
   {
@@ -1523,7 +1531,7 @@
    "id": "72c94843",
    "metadata": {},
    "source": [
-    "This is a four band imagery so the preprocessors are first defined for scaling it."
+    "This is a four band imagery, so the preprocessors are first defined for scaling."
    ]
   },
   {
@@ -1657,7 +1665,7 @@
    "id": "bf3f990e",
    "metadata": {},
    "source": [
-    "Once the data is prepared, an unsupervised model of k-means clustering from scikit-learn can be used here for clustering the pixels into clouds and no clouds. Here, the clustering model is passed inside an MLModel, with number of clusters set as three representing no clouds, medium clouds and dense clouds."
+    "Once the data is prepared, an unsupervised model of k-means clustering from scikit-learn can be used for clustering the pixels into areas of clouds and no clouds. The clustering model is passed inside an MLModel, with the number of clusters set as three for the classes of no clouds, medium clouds, and dense clouds."
    ]
   },
   {
@@ -1885,7 +1893,7 @@
    "source": [
     "## Conclusion<a class=\"anchor\" id=\"19\"></a>\n",
     "\n",
-    "In this sample notebook two methods has been described that can be used to create cloud masks from satellite images. A  third method will be described in the second part of the notebook series.\n",
+    "In this sample notebook, two methods were described to create cloud masks from satellite images. A third method will be described in the second part of this notebook series.\n",
     "\n",
     "The cloudless sentinel package performed well in detecting clouds and provides flexibility to the user to detect different intensities of clouds by changing the model's initialization parameters. However, the core caveat of this model is that it can only be used on Sentinel imagery.\n",
     "\n",