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SpaceX Falcon 9 First Stage Landing Prediction

Assignment: Exploring and Preparing Data

Estimated time needed: 70 minutes

In this assignment, we will predict if the Falcon 9 first stage will land successfully. SpaceX advertises Falcon 9 rocket launches on its website with a cost of 62 million dollars; other providers cost upward of 165 million dollars each, much of the savings is due to the fact that SpaceX can reuse the first stage.

In this lab, you will perform Exploratory Data Analysis and Feature Engineering.

Falcon 9 first stage will land successfully

Several examples of an unsuccessful landing are shown here:

Most unsuccessful landings are planned. Space X performs a controlled landing in the oceans.

Objectives

Perform exploratory Data Analysis and Feature Engineering using Pandas and Matplotlib

  • Exploratory Data Analysis
  • Preparing Data Feature Engineering

Import Libraries and Define Auxiliary Functions

We will import the following libraries the lab

# andas is a software library written for the Python programming language for data manipulation and analysis.
import pandas as pd
#NumPy is a library for the Python programming language, adding support for large, multi-dimensional arrays and matrices, along with a large collection of high-level mathematical functions to operate on these arrays
import numpy as np
# Matplotlib is a plotting library for python and pyplot gives us a MatLab like plotting framework. We will use this in our plotter function to plot data.
import matplotlib.pyplot as plt
#Seaborn is a Python data visualization library based on matplotlib. It provides a high-level interface for drawing attractive and informative statistical graphics
import seaborn as sns

Exploratory Data Analysis

First, let’s read the SpaceX dataset into a Pandas dataframe and print its summary

df=pd.read_csv("https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBM-DS0321EN-SkillsNetwork/datasets/dataset_part_2.csv")

# If you were unable to complete the previous lab correctly you can uncomment and load this csv

# df = pd.read_csv('https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-DS0701EN-SkillsNetwork/api/dataset_part_2.csv')

df.head(5)
FlightNumber Date BoosterVersion PayloadMass Orbit LaunchSite Outcome Flights GridFins Reused Legs LandingPad Block ReusedCount Serial Longitude Latitude Class
0 1 2010-06-04 Falcon 9 6104.959412 LEO CCAFS SLC 40 None None 1 False False False NaN 1.0 0 B0003 -80.577366 28.561857 0
1 2 2012-05-22 Falcon 9 525.000000 LEO CCAFS SLC 40 None None 1 False False False NaN 1.0 0 B0005 -80.577366 28.561857 0
2 3 2013-03-01 Falcon 9 677.000000 ISS CCAFS SLC 40 None None 1 False False False NaN 1.0 0 B0007 -80.577366 28.561857 0
3 4 2013-09-29 Falcon 9 500.000000 PO VAFB SLC 4E False Ocean 1 False False False NaN 1.0 0 B1003 -120.610829 34.632093 0
4 5 2013-12-03 Falcon 9 3170.000000 GTO CCAFS SLC 40 None None 1 False False False NaN 1.0 0 B1004 -80.577366 28.561857 0

First, let’s try to see how the FlightNumber (indicating the continuous launch attempts.) and Payload variables would affect the launch outcome.

We can plot out the FlightNumber vs. PayloadMassand overlay the outcome of the launch. We see that as the flight number increases, the first stage is more likely to land successfully. The payload mass is also important; it seems the more massive the payload, the less likely the first stage will return.

sns.catplot(y="PayloadMass", x="FlightNumber", hue="Class", data=df, aspect = 4)
plt.xlabel("Flight Number",fontsize=12)
plt.ylabel("Pay load Mass (kg)",fontsize=12)
plt.show()

We see that different launch sites have different success rates. CCAFS LC-40, has a success rate of 60 %, while KSC LC-39A and VAFB SLC 4E has a success rate of 77%.

Next, let’s drill down to each site visualize its detailed launch records.

TASK 1: Visualize the relationship between Flight Number and Launch Site

Use the function catplot to plot FlightNumber vs LaunchSite, set the parameter x parameter to FlightNumber,set the y to Launch Site and set the parameter hue to ‘class’

# Plot a scatter point chart with x axis to be Flight Number and y axis to be the launch site, and hue to be the class value
sns.catplot(x="FlightNumber", y="LaunchSite", hue="Class", data=df, aspect = 3)
plt.xlabel("Flight Number",fontsize=12)
plt.ylabel("Launch Site",fontsize=12)
plt.show()

Now try to explain the patterns you found in the Flight Number vs. Launch Site scatter point plots.

TASK 2: Visualize the relationship between Payload and Launch Site

We also want to observe if there is any relationship between launch sites and their payload mass.

# Plot a scatter point chart with x axis to be Pay Load Mass (kg) and y axis to be the launch site, and hue to be the class value
sns.catplot(x="PayloadMass", y="LaunchSite", hue="Class", data=df, aspect = 3)
plt.xlabel("PayloadMass",fontsize=12)
plt.ylabel("Launch Site",fontsize=12)
plt.show()

Now if you observe Payload Vs. Launch Site scatter point chart you will find for the VAFB-SLC launchsite there are no rockets launched for heavypayload mass(greater than 10000).

TASK 3: Visualize the relationship between success rate of each orbit type

Next, we want to visually check if there are any relationship between success rate and orbit type.

Let’s create a bar chart for the sucess rate of each orbit

# HINT use groupby method on Orbit column and get the mean of Class column

success_rate = df[['Orbit','Class']].groupby('Orbit').mean().sort_values('Class')
print(success_rate, '\n')

success_rate.plot(kind='barh', figsize=(15, 10))

plt.title('Relationship between success rate of each orbit type', fontsize=15)
plt.xlabel('Success rate', fontsize=12)
plt.ylabel('Orbit', fontsize=12)

plt.show()
          Class
Orbit          
SO     0.000000
GTO    0.518519
ISS    0.619048
MEO    0.666667
PO     0.666667
LEO    0.714286
VLEO   0.857143
ES-L1  1.000000
GEO    1.000000
HEO    1.000000
SSO    1.000000

Analyze the ploted bar chart try to find which orbits have high sucess rate.

TASK 4: Visualize the relationship between FlightNumber and Orbit type

For each orbit, we want to see if there is any relationship between FlightNumber and Orbit type.

# Plot a scatter point chart with x axis to be FlightNumber and y axis to be the Orbit, and hue to be the class value
sns.catplot(x="FlightNumber", y="Orbit", hue="Class", data=df, aspect = 3)
plt.xlabel("FlightNumber",fontsize=12)
plt.ylabel("Orbit",fontsize=12)
plt.show()

You should see that in the LEO orbit the Success appears related to the number of flights; on the other hand, there seems to be no relationship between flight number when in GTO orbit.

TASK 5: Visualize the relationship between Payload and Orbit type

Similarly, we can plot the Payload vs. Orbit scatter point charts to reveal the relationship between Payload and Orbit type

# Plot a scatter point chart with x axis to be Payload and y axis to be the Orbit, and hue to be the class value
sns.catplot(x="PayloadMass", y="Orbit", hue="Class", data=df, aspect = 3)
plt.xlabel("PayloadMass",fontsize=12)
plt.ylabel("Orbit",fontsize=12)
plt.show()

With heavy payloads the successful landing or positive landing rate are more for Polar,LEO and ISS.

However for GTO we cannot distinguish this well as both positive landing rate and negative landing(unsuccessful mission) are both there here.

TASK 6: Visualize the launch success yearly trend

You can plot a line chart with x axis to be Year and y axis to be average success rate, to get the average launch success trend.

The function will help you get the year from the date:

# A function to Extract years from the date 
year=[]
def Extract_year(date):
    for i in df["Date"]:
        year.append(i.split("-")[0])
    return year

Extract_year(df.Date)
df['Year'] = year
df.head()
FlightNumber Date BoosterVersion PayloadMass Orbit LaunchSite Outcome Flights GridFins Reused Legs LandingPad Block ReusedCount Serial Longitude Latitude Class Year
0 1 2010-06-04 Falcon 9 6104.959412 LEO CCAFS SLC 40 None None 1 False False False NaN 1.0 0 B0003 -80.577366 28.561857 0 2010
1 2 2012-05-22 Falcon 9 525.000000 LEO CCAFS SLC 40 None None 1 False False False NaN 1.0 0 B0005 -80.577366 28.561857 0 2012
2 3 2013-03-01 Falcon 9 677.000000 ISS CCAFS SLC 40 None None 1 False False False NaN 1.0 0 B0007 -80.577366 28.561857 0 2013
3 4 2013-09-29 Falcon 9 500.000000 PO VAFB SLC 4E False Ocean 1 False False False NaN 1.0 0 B1003 -120.610829 34.632093 0 2013
4 5 2013-12-03 Falcon 9 3170.000000 GTO CCAFS SLC 40 None None 1 False False False NaN 1.0 0 B1004 -80.577366 28.561857 0 2013 
# Plot a line chart with x axis to be the extracted year and y axis to be the success rate
plt.figure(figsize=(15,10))
sns.lineplot(x='Year', y='Class', data=df)

plt.xlabel('Year', fontsize=12)
plt.ylabel('Success rate', fontsize=12)

plt.show()

you can observe that the sucess rate since 2013 kept increasing till 2020

Features Engineering

By now, you should obtain some preliminary insights about how each important variable would affect the success rate, we will select the features that will be used in success prediction in the future module.

features = df[['FlightNumber', 'PayloadMass', 'Orbit', 'LaunchSite', 'Flights', 'GridFins', 'Reused', 'Legs', 'LandingPad', 'Block', 'ReusedCount', 'Serial']]
features.head()
FlightNumber PayloadMass Orbit LaunchSite Flights GridFins Reused Legs LandingPad Block ReusedCount Serial
0 1 6104.959412 LEO CCAFS SLC 40 1 False False False NaN 1.0 0 B0003
1 2 525.000000 LEO CCAFS SLC 40 1 False False False NaN 1.0 0 B0005
2 3 677.000000 ISS CCAFS SLC 40 1 False False False NaN 1.0 0 B0007
3 4 500.000000 PO VAFB SLC 4E 1 False False False NaN 1.0 0 B1003
4 5 3170.000000 GTO CCAFS SLC 40 1 False False False NaN 1.0 0 B1004

TASK 7: Create dummy variables to categorical columns

Use the function get_dummies and features dataframe to apply OneHotEncoder to the column Orbits, LaunchSite, LandingPad, and Serial. Assign the value to the variable features_one_hot, display the results using the method head. Your result dataframe must include all features including the encoded ones.

# HINT: Use get_dummies() function on the categorical columns
features_one_hot = pd.get_dummies(features, columns=['Orbit', 'LaunchSite', 'LandingPad', 'Serial'])

features_one_hot.head()
FlightNumber PayloadMass Flights GridFins Reused Legs Block ReusedCount Orbit_ES-L1 Orbit_GEO ... Serial_B1048 Serial_B1049 Serial_B1050 Serial_B1051 Serial_B1054 Serial_B1056 Serial_B1058 Serial_B1059 Serial_B1060 Serial_B1062
0 1 6104.959412 1 False False False 1.0 0 0 0 ... 0 0 0 0 0 0 0 0 0 0
1 2 525.000000 1 False False False 1.0 0 0 0 ... 0 0 0 0 0 0 0 0 0 0
2 3 677.000000 1 False False False 1.0 0 0 0 ... 0 0 0 0 0 0 0 0 0 0
3 4 500.000000 1 False False False 1.0 0 0 0 ... 0 0 0 0 0 0 0 0 0 0
4 5 3170.000000 1 False False False 1.0 0 0 0 ... 0 0 0 0 0 0 0 0 0 0

5 rows × 80 columns

features_one_hot.columns
Index(['FlightNumber', 'PayloadMass', 'Flights', 'GridFins', 'Reused', 'Legs',
       'Block', 'ReusedCount', 'Orbit_ES-L1', 'Orbit_GEO', 'Orbit_GTO',
       'Orbit_HEO', 'Orbit_ISS', 'Orbit_LEO', 'Orbit_MEO', 'Orbit_PO',
       'Orbit_SO', 'Orbit_SSO', 'Orbit_VLEO', 'LaunchSite_CCAFS SLC 40',
       'LaunchSite_KSC LC 39A', 'LaunchSite_VAFB SLC 4E',
       'LandingPad_5e9e3032383ecb267a34e7c7',
       'LandingPad_5e9e3032383ecb554034e7c9',
       'LandingPad_5e9e3032383ecb6bb234e7ca',
       'LandingPad_5e9e3032383ecb761634e7cb',
       'LandingPad_5e9e3033383ecbb9e534e7cc', 'Serial_B0003', 'Serial_B0005',
       'Serial_B0007', 'Serial_B1003', 'Serial_B1004', 'Serial_B1005',
       'Serial_B1006', 'Serial_B1007', 'Serial_B1008', 'Serial_B1010',
       'Serial_B1011', 'Serial_B1012', 'Serial_B1013', 'Serial_B1015',
       'Serial_B1016', 'Serial_B1017', 'Serial_B1018', 'Serial_B1019',
       'Serial_B1020', 'Serial_B1021', 'Serial_B1022', 'Serial_B1023',
       'Serial_B1025', 'Serial_B1026', 'Serial_B1028', 'Serial_B1029',
       'Serial_B1030', 'Serial_B1031', 'Serial_B1032', 'Serial_B1034',
       'Serial_B1035', 'Serial_B1036', 'Serial_B1037', 'Serial_B1038',
       'Serial_B1039', 'Serial_B1040', 'Serial_B1041', 'Serial_B1042',
       'Serial_B1043', 'Serial_B1044', 'Serial_B1045', 'Serial_B1046',
       'Serial_B1047', 'Serial_B1048', 'Serial_B1049', 'Serial_B1050',
       'Serial_B1051', 'Serial_B1054', 'Serial_B1056', 'Serial_B1058',
       'Serial_B1059', 'Serial_B1060', 'Serial_B1062'],
      dtype='object')

TASK 8: Cast all numeric columns to float64

Now that our features_one_hot dataframe only contains numbers cast the entire dataframe to variable type float64

features_one_hot.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 90 entries, 0 to 89
Data columns (total 80 columns):
 #   Column                               Non-Null Count  Dtype  
---  ------                               --------------  -----  
 0   FlightNumber                         90 non-null     int64  
 1   PayloadMass                          90 non-null     float64
 2   Flights                              90 non-null     int64  
 3   GridFins                             90 non-null     bool   
 4   Reused                               90 non-null     bool   
 5   Legs                                 90 non-null     bool   
 6   Block                                90 non-null     float64
 7   ReusedCount                          90 non-null     int64  
 8   Orbit_ES-L1                          90 non-null     uint8  
 9   Orbit_GEO                            90 non-null     uint8  
 10  Orbit_GTO                            90 non-null     uint8  
 11  Orbit_HEO                            90 non-null     uint8  
 12  Orbit_ISS                            90 non-null     uint8  
 13  Orbit_LEO                            90 non-null     uint8  
 14  Orbit_MEO                            90 non-null     uint8  
 15  Orbit_PO                             90 non-null     uint8  
 16  Orbit_SO                             90 non-null     uint8  
 17  Orbit_SSO                            90 non-null     uint8  
 18  Orbit_VLEO                           90 non-null     uint8  
 19  LaunchSite_CCAFS SLC 40              90 non-null     uint8  
 20  LaunchSite_KSC LC 39A                90 non-null     uint8  
 21  LaunchSite_VAFB SLC 4E               90 non-null     uint8  
 22  LandingPad_5e9e3032383ecb267a34e7c7  90 non-null     uint8  
 23  LandingPad_5e9e3032383ecb554034e7c9  90 non-null     uint8  
 24  LandingPad_5e9e3032383ecb6bb234e7ca  90 non-null     uint8  
 25  LandingPad_5e9e3032383ecb761634e7cb  90 non-null     uint8  
 26  LandingPad_5e9e3033383ecbb9e534e7cc  90 non-null     uint8  
 27  Serial_B0003                         90 non-null     uint8  
 28  Serial_B0005                         90 non-null     uint8  
 29  Serial_B0007                         90 non-null     uint8  
 30  Serial_B1003                         90 non-null     uint8  
 31  Serial_B1004                         90 non-null     uint8  
 32  Serial_B1005                         90 non-null     uint8  
 33  Serial_B1006                         90 non-null     uint8  
 34  Serial_B1007                         90 non-null     uint8  
 35  Serial_B1008                         90 non-null     uint8  
 36  Serial_B1010                         90 non-null     uint8  
 37  Serial_B1011                         90 non-null     uint8  
 38  Serial_B1012                         90 non-null     uint8  
 39  Serial_B1013                         90 non-null     uint8  
 40  Serial_B1015                         90 non-null     uint8  
 41  Serial_B1016                         90 non-null     uint8  
 42  Serial_B1017                         90 non-null     uint8  
 43  Serial_B1018                         90 non-null     uint8  
 44  Serial_B1019                         90 non-null     uint8  
 45  Serial_B1020                         90 non-null     uint8  
 46  Serial_B1021                         90 non-null     uint8  
 47  Serial_B1022                         90 non-null     uint8  
 48  Serial_B1023                         90 non-null     uint8  
 49  Serial_B1025                         90 non-null     uint8  
 50  Serial_B1026                         90 non-null     uint8  
 51  Serial_B1028                         90 non-null     uint8  
 52  Serial_B1029                         90 non-null     uint8  
 53  Serial_B1030                         90 non-null     uint8  
 54  Serial_B1031                         90 non-null     uint8  
 55  Serial_B1032                         90 non-null     uint8  
 56  Serial_B1034                         90 non-null     uint8  
 57  Serial_B1035                         90 non-null     uint8  
 58  Serial_B1036                         90 non-null     uint8  
 59  Serial_B1037                         90 non-null     uint8  
 60  Serial_B1038                         90 non-null     uint8  
 61  Serial_B1039                         90 non-null     uint8  
 62  Serial_B1040                         90 non-null     uint8  
 63  Serial_B1041                         90 non-null     uint8  
 64  Serial_B1042                         90 non-null     uint8  
 65  Serial_B1043                         90 non-null     uint8  
 66  Serial_B1044                         90 non-null     uint8  
 67  Serial_B1045                         90 non-null     uint8  
 68  Serial_B1046                         90 non-null     uint8  
 69  Serial_B1047                         90 non-null     uint8  
 70  Serial_B1048                         90 non-null     uint8  
 71  Serial_B1049                         90 non-null     uint8  
 72  Serial_B1050                         90 non-null     uint8  
 73  Serial_B1051                         90 non-null     uint8  
 74  Serial_B1054                         90 non-null     uint8  
 75  Serial_B1056                         90 non-null     uint8  
 76  Serial_B1058                         90 non-null     uint8  
 77  Serial_B1059                         90 non-null     uint8  
 78  Serial_B1060                         90 non-null     uint8  
 79  Serial_B1062                         90 non-null     uint8  
dtypes: bool(3), float64(2), int64(3), uint8(72)
memory usage: 10.2 KB
# HINT: use astype function
features_one_hot = features_one_hot.astype('float64')
features_one_hot.dtypes
FlightNumber    float64
PayloadMass     float64
Flights         float64
GridFins        float64
Reused          float64
                 ...   
Serial_B1056    float64
Serial_B1058    float64
Serial_B1059    float64
Serial_B1060    float64
Serial_B1062    float64
Length: 80, dtype: object

We can now export it to a CSV for the next section,but to make the answers consistent, in the next lab we will provide data in a pre-selected date range.

features_one_hot.to_csv(‘dataset_part_3.csv’, index=False)

features_one_hot.to_csv('dataset_part_3.csv', index=False)

Authors

Joseph Santarcangelo has a PhD in Electrical Engineering, his research focused on using machine learning, signal processing, and computer vision to determine how videos impact human cognition. Joseph has been working for IBM since he completed his PhD.

Nayef Abou Tayoun is a Data Scientist at IBM and pursuing a Master of Management in Artificial intelligence degree at Queen’s University.

Change Log

Date (YYYY-MM-DD) Version Changed By Change Description
2021-10-12 1.1 Lakshmi Holla Modified markdown
2020-09-20 1.0 Joseph Modified Multiple Areas
2020-11-10 1.1 Nayef updating the input data

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