Machine Learning Project – Predict Forest Cover Part 1 - Projects Based Learning

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Machine Learning Project – Predict Forest Cover Part 1
Bhavesh
January 26, 2021
July 2, 2021
In this project, we will predict Forest Cover based on various attributes (cartographic variables) of the Forest. Hence, this is a classification problem.
Problem Statement or Business Problem
In this project, we’ll predict Forest Cover supported various attributes (cartographic variables) of the Forest. Hence, this is often a classification problem.
Attribute Information or Dataset Details:
Given is the attribute name, attribute type, the measurement unit, and a brief description. The forest cover type is the classification problem. The order of this listing corresponds to the order of numerals along the rows of the database.
Name
Horz Dist to nearest surface water features
Vertical_Distance_To_Hydrology
Vert Dist to nearest surface water features
Horizontal_Distance_To_Roadways
Horz Dist to nearest roadway
Hillshade_9am
Hillshade index at 9am, summer solstice
Hillshade_Noon
Hillshade index at noon, summer soltice
Hillshade_3pm
Hillshade index at 3pm, summer solstice
Horizontal_Distance_To_Fire_Points
Horz Dist to nearest wildfire ignition points
Wilderness_Area (4 binary columns)
Databricks Notebook
Introduction
Welcome to this project on predict Forest Cover in Apache Spark Machine Learning using Databricks platform community edition server which allows you to execute your spark code, free of cost on their server just by registering through email id.
In this project, we explore Apache Spark and Machine Learning on the Databricks platform.
I am a firm believer that the best way to learn is by doing. That’s why I haven’t included any purely theoretical lectures in this tutorial: you will learn everything on the way and be able to put it into practice straight away. Seeing the way each feature works will help you learn Apache Spark machine learning thoroughly by heart.
We’re going to look at how to set up a Spark Cluster and get started with that. And we’ll look at how we can then use that Spark Cluster to take data coming into that Spark Cluster, a process that data using a Machine Learning model, and generate some sort of output in the form of a prediction. That’s pretty much what we’re going to learn about the predictive model.
In this project, we will be performing prediction Forest Cover Type.
We will learn:
Preparing the Data for Processing.
Basics flow of data in Apache Spark, loading data, and working with data, this course shows you how Apache Spark is perfect for a Machine Learning job.
Learn the basics of Databricks notebook by enrolling in Free Community Edition Server
Define the Machine Learning Pipeline
Train a Machine Learning Model
Testing a Machine Learning Model
Evaluating a Machine Learning Model (i.e. Examine the Predicted and Actual Values)
The goal is to provide you with practical tools that will be beneficial for you in the future. While doing that, you’ll develop a model with a real use opportunity.
I am really excited you are here, I hope you are going to follow all the way to the end of the Project. It is fairly straight forward fairly easy to follow through the article we will show you step by step each line of code & we will explain what it does and why we are doing it.
Free Account creation in Databricks
Load Data in Dataframe using User-defined Schema
We are loading data into Databricks environment and creating dataframe with user defined schema.
%scala import org.apache.spark.sql.Encoders case class Forest( Elevation: Int, Aspect: Int, Slope: Int, Horizontal_Distance_To_Hydrology: Int, Vertical_Distance_To_Hydrology: Int, Horizontal_Distance_To_Roadways: Int, Hillshade_9am: Int, Hillshade_Noon: Int, Hillshade_3pm: Int, Horizontal_Distance_To_Fire_Points: Int, Wilderness_Area_0: Int, Wilderness_Area_1: Int, Wilderness_Area_2: Int, Wilderness_Area_3: Int, Soil_Type_0: Int, Soil_Type_1: Int, Soil_Type_2: Int, Soil_Type_3: Int, Soil_Type_4: Int, Soil_Type_5: Int, Soil_Type_6: Int, Soil_Type_7: Int, Soil_Type_8: Int, Soil_Type_9: Int, Soil_Type_10: Int, Soil_Type_11: Int, Soil_Type_12: Int, Soil_Type_13: Int, Soil_Type_14: Int, Soil_Type_15: Int, Soil_Type_16: Int, Soil_Type_17: Int, Soil_Type_18: Int, Soil_Type_19: Int, Soil_Type_20: Int, Soil_Type_21: Int, Soil_Type_22: Int, Soil_Type_23: Int, Soil_Type_24: Int, Soil_Type_25: Int, Soil_Type_26: Int, Soil_Type_27: Int, Soil_Type_28: Int, Soil_Type_29: Int, Soil_Type_30: Int, Soil_Type_31: Int, Soil_Type_32: Int, Soil_Type_33: Int, Soil_Type_34: Int, Soil_Type_35: Int, Soil_Type_36: Int, Soil_Type_37: Int, Soil_Type_38: Int, Soil_Type_39: Int, Cover_Type: Double ) val ForestSchema = Encoders.product[Forest].schema val ForestDF = spark.read.schema(ForestSchema).option("header", "true").csv("/FileStore/tables/covtype.data") display(ForestDF)
Print Schema of Dataframe
%scala ForestDF.printSchema() Output: root |-- Elevation: integer (nullable = true) |-- Aspect: integer (nullable = true) |-- Slope: integer (nullable = true) |-- Horizontal_Distance_To_Hydrology: integer (nullable = true) |-- Vertical_Distance_To_Hydrology: integer (nullable = true) |-- Horizontal_Distance_To_Roadways: integer (nullable = true) |-- Hillshade_9am: integer (nullable = true) |-- Hillshade_Noon: integer (nullable = true) |-- Hillshade_3pm: integer (nullable = true) |-- Horizontal_Distance_To_Fire_Points: integer (nullable = true) |-- Wilderness_Area_0: integer (nullable = true) |-- Wilderness_Area_1: integer (nullable = true) |-- Wilderness_Area_2: integer (nullable = true) |-- Wilderness_Area_3: integer (nullable = true) |-- Soil_Type_0: integer (nullable = true) |-- Soil_Type_1: integer (nullable = true) |-- Soil_Type_2: integer (nullable = true) |-- Soil_Type_3: integer (nullable = true) |-- Soil_Type_4: integer (nullable = true) |-- Soil_Type_5: integer (nullable = true) |-- Soil_Type_6: integer (nullable = true) |-- Soil_Type_7: integer (nullable = true) |-- Soil_Type_8: integer (nullable = true) |-- Soil_Type_9: integer (nullable = true) |-- Soil_Type_10: integer (nullable = true) |-- Soil_Type_11: integer (nullable = true) |-- Soil_Type_12: integer (nullable = true) |-- Soil_Type_13: integer (nullable = true) |-- Soil_Type_14: integer (nullable = true) |-- Soil_Type_15: integer (nullable = true) |-- Soil_Type_16: integer (nullable = true) |-- Soil_Type_17: integer (nullable = true) |-- Soil_Type_18: integer (nullable = true) |-- Soil_Type_19: integer (nullable = true) |-- Soil_Type_20: integer (nullable = true) |-- Soil_Type_21: integer (nullable = true) |-- Soil_Type_22: integer (nullable = true) |-- Soil_Type_23: integer (nullable = true) |-- Soil_Type_24: integer (nullable = true) |-- Soil_Type_25: integer (nullable = true) |-- Soil_Type_26: integer (nullable = true) |-- Soil_Type_27: integer (nullable = true) |-- Soil_Type_28: integer (nullable = true) |-- Soil_Type_29: integer (nullable = true) |-- Soil_Type_30: integer (nullable = true) |-- Soil_Type_31: integer (nullable = true) |-- Soil_Type_32: integer (nullable = true) |-- Soil_Type_33: integer (nullable = true) |-- Soil_Type_34: integer (nullable = true) |-- Soil_Type_35: integer (nullable = true) |-- Soil_Type_36: integer (nullable = true) |-- Soil_Type_37: integer (nullable = true) |-- Soil_Type_38: integer (nullable = true) |-- Soil_Type_39: integer (nullable = true) |-- Cover_Type: double (nullable = true)
Statistics of Data
%scala ForestDF.describe().show() Output: +-------+------------------+------------------+-----------------+--------------------------------+------------------------------+-------------------------------+------------------+------------------+-----------------+----------------------------------+-------------------+-------------------+------------------+-------------------+--------------------+--------------------+--------------------+-------------------+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+-------------------+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+-------------------+-------------------+-------------------+--------------------+--------------------+--------------------+--------------------+------------------+-------------------+-------------------+-------------------+-------------------+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+------------------+ |summary| Elevation| Aspect| Slope|Horizontal_Distance_To_Hydrology|Vertical_Distance_To_Hydrology|Horizontal_Distance_To_Roadways| Hillshade_9am| Hillshade_Noon| Hillshade_3pm|Horizontal_Distance_To_Fire_Points| Wilderness_Area_0| Wilderness_Area_1| Wilderness_Area_2| Wilderness_Area_3| Soil_Type_0| Soil_Type_1| Soil_Type_2| Soil_Type_3| Soil_Type_4| Soil_Type_5| Soil_Type_6| Soil_Type_7| Soil_Type_8| Soil_Type_9| Soil_Type_10| Soil_Type_11| Soil_Type_12| Soil_Type_13| Soil_Type_14| Soil_Type_15| Soil_Type_16| Soil_Type_17| Soil_Type_18| Soil_Type_19| Soil_Type_20| Soil_Type_21| Soil_Type_22| Soil_Type_23| Soil_Type_24| Soil_Type_25| Soil_Type_26| Soil_Type_27| Soil_Type_28| Soil_Type_29| Soil_Type_30| Soil_Type_31| Soil_Type_32| Soil_Type_33| Soil_Type_34| Soil_Type_35| Soil_Type_36| Soil_Type_37| Soil_Type_38| Soil_Type_39| Cover_Type| +-------+------------------+------------------+-----------------+--------------------------------+------------------------------+-------------------------------+------------------+------------------+-----------------+----------------------------------+-------------------+-------------------+------------------+-------------------+--------------------+--------------------+--------------------+-------------------+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+-------------------+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+-------------------+-------------------+-------------------+--------------------+--------------------+--------------------+--------------------+------------------+-------------------+-------------------+-------------------+-------------------+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+------------------+ | count| 581011| 581011| 581011| 581011| 581011| 581011| 581011| 581011| 581011| 581011| 581011| 581011| 581011| 581011| 581011| 581011| 581011| 581011| 581011| 581011| 581011| 581011| 581011| 581011| 581011| 581011| 581011| 581011| 581011| 581011| 581011| 581011| 581011| 581011| 581011| 581011| 581011| 581011| 581011| 581011| 581011| 581011| 581011| 581011| 581011| 581011| 581011| 581011| 581011| 581011| 581011| 581011| 581011| 581011| 581011| | mean|2959.3659259463248|155.65698756133705|14.10372264896878| 269.4282362984522| 46.41893526972811| 2350.149778575621|212.14603337974668|223.31870136709975|142.5282533377165| 1980.283827672798| 0.4488641351024335|0.05143448230756389|0.4360743600379339|0.06362702255206872|0.005216768701453157|0.012951562018619269|0.008301047656584815|0.02133522429007368|0.002748657082223...|0.011316481099325142|1.807194700272456...|3.080836679512091...|0.001974145067821...|0.056167611284467935|0.021359320219410647|0.05158422129701503|0.030001153162332556|0.001030961548060...|5.163413429349874...|0.004896637068833465|0.005889733585078...|0.003268440700778...|0.006920695133138615|0.015936014980783498|0.001442313484598...|0.05743953212589779|0.09939915079060466|0.03662237031656888|8.158193218372802E-4|0.004456025789528942|0.001869155661424...|0.001628196368054...|0.1983542480262852|0.05192672772116191|0.04417472302589796|0.09039243663200869|0.07771625666295474|0.002772753011560...|0.003254671598300...|2.048153993642117E-4|5.128990673154209E-4|0.026803279111755198|0.023762028601868122|0.015059955835603801| 2.05146546278814| | stddev| 279.9845693713207|111.91373308836437|7.488234089511352| 212.5495379798926| 58.29524998555078| 1559.2543428941767|26.769909322565002|19.768710885237958| 38.2745614961487| 1324.1843401788144|0.49737867777559513| 0.2208824581310452|0.4958971020355072|0.24408753982214487| 0.07203862129248234| 0.11306564934872727| 0.09073122082866608|0.14449937173580144| 0.05235557930431683| 0.10577541118661181|0.013441990979309393|0.017549623428915792| 0.0443875118715442| 0.23024530824660677| 0.14457916736636858|0.22118628712934196| 0.17059054504375418| 0.03209206193043992|0.002272310642369...| 0.06980450129313612| 0.07651832918151862| 0.05707682194397399| 0.08290241818588581| 0.12522813341396524| 0.03795043735261384| 0.2326807371872815|0.29919744933311637|0.18783299239704696|0.028550922290983532| 0.06660463391509971|0.043193345894996675| 0.04031808703838|0.3987607227329843|0.22187930818732113|0.20548330721437016| 0.2867434141297083| 0.267724790556452| 0.05258392921191433|0.056956863454754536|0.014309919722357935|0.022641485909296887| 0.16150822962417238| 0.15230704029877873| 0.12179153950820612|1.3965001604421121| | min| 1859| 0| 0| 0| -173| 0| 0| 0| 0| 0| 0| 0| 0| 0| 0| 0| 0| 0| 0| 0| 0| 0| 0| 0| 0| 0| 0| 0| 0| 0| 0| 0| 0| 0| 0| 0| 0| 0| 0| 0| 0| 0| 0| 0| 0| 0| 0| 0| 0| 0| 0| 0| 0| 0| 1.0| | max| 3858| 360| 66| 1397| 601| 7117| 254| 254| 254| 7173| 1| 1| 1| 1| 1| 1| 1| 1| 1| 1| 1| 1| 1| 1| 1| 1| 1| 1| 1| 1| 1| 1| 1| 1| 1| 1| 1| 1| 1| 1| 1| 1| 1| 1| 1| 1| 1| 1| 1| 1| 1| 1| 1| 1| 7.0| +-------+------------------+------------------+-----------------+--------------------------------+------------------------------+-------------------------------+------------------+------------------+-----------------+----------------------------------+-------------------+-------------------+------------------+-------------------+--------------------+--------------------+--------------------+-------------------+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+-------------------+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+-------------------+-------------------+-------------------+--------------------+--------------------+--------------------+--------------------+------------------+-------------------+-------------------+-------------------+-------------------+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+------------------+
Create Temporary View so we can perform Spark SQL on Data
%scala ForestDF.createOrReplaceTempView("ForestData")
Exploratory Data Analysis or EDA​
Hill shade Based on Cover Type
%sql select Hillshade_3pm, Hillshade_9am, Cover_Type from ForestData
Horizontal_Distance_To_Hydrology VS Vertical_Distance_To_Hydrology Based on Cover_Type
%sql select Horizontal_Distance_To_Hydrology, Vertical_Distance_To_Hydrology, Cover_Type from ForestData
Hill Shade based on Cover Type
%sql select Hillshade_3pm, Hillshade_Noon, Cover_Type from ForestData
Hillshade VS Aspect Based on Cover_Type
%sql select Hillshade_3pm, Aspect, Cover_Type from ForestData
Hillshade_Noon VS Slope Based on Cover_Type
%sql select Hillshade_Noon, Slope, Cover_Type from ForestData
Hillshade VS Aspect based on Cover_Type
%sql select Hillshade_9am, Aspect, Cover_Type from ForestData
Horizontal_Distance_To_Roadways VS Elevation based on Cover_Type
%sql select Horizontal_Distance_To_Roadways, Elevation, Cover_Type from ForestData