minor edits

Author: Anushriya Jain

samples/04_gis_analysts_data_scientists/forecasting_air_temperature_in_california.ipynb

@@ -532,7 +532,7 @@
    "metadata": {},
    "source": [
     "### Converting into time series format<a class=\"anchor\" id=\"6\"></a>\n",
-    "The dataset is now transformed into a time series data format by creating a new index that will used by the model for processing the sequential data."
+    "The dataset is now transformed into a time series data format by creating a new index that will be used by the model for processing the sequential data."
    ]
   },
   {
@@ -1179,7 +1179,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Visualize the timeseries in batches, here the sequence length is mentioned which would be treated as the batch length"
+    "Next, we visualize the timeseries in batches. Here, we will pass the sequence length as the batch length."
    ]
   },
   {
@@ -2614,7 +2614,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "A considerably high r-square value of .91 indicates a high similarity between the forecasted values and the actual values. Furthermore, the RMSE error of 3.661 is quite low, indicating a good fit by model."
+    "A considerably high r-square value of .91 indicates a high similarity between the forecasted values and the actual values. Furthermore, the RMSE error of 3.661 is quite low, indicating a good fit by the model."
    ]
   },
   {