- Data Collection
- Exploratory Data Analysis
- Predictive Analysis (Classification)
- How to Present Your Findings
The SpaceX REST API endpoints:
- https://api.spacexdata.com/v4/cores
- https://api.spacexdata.com/v4/capsules
- https://api.spacexdata.com/v4/launches/past
import requests
import pandas as pd
spacex_url = "https://api.spacexdata.com/v4/launches/past"
response = requests.get(spacex_url)
data = pd.json_normalize(response.json())Data Source: Wikipedia: List of Falcon 9 and Falcon Heavy launches
- Hands-on Lab : String Patterns, Sorting and Grouping
- Hands-on Lab: Built-in functions
- Hands-on Lab : Sub-queries and Nested SELECT Statements
- Hands-on Tutorial: Accessing Databases with SQL magic
- Hands-on Lab: Analyzing a real World Data Set
# Function to assign color to launch outcome
def assign_marker_color(launch_outcome):
if launch_outcome == 1:
return 'green'
else:
return 'red'
spacex_df['marker_color'] = spacex_df['class'].apply(assign_marker_color)marker_cluster = MarkerCluster()
# Add marker_cluster to current site_map
site_map.add_child(marker_cluster)
# for each row in spacex_df data frame
# create a Marker object with its coordinate
# and customize the Marker's icon property to indicate if this launch was successed or failed,
# e.g., icon=folium.Icon(color='white', icon_color=row['marker_color']
for index, record in spacex_df.iterrows():
# TODO: Create and add a Marker cluster to the site map
# marker = folium.Marker(...)
marker = folium.map.Marker(
[record['Lat'],record['Long']],
# Create an icon as a text label
icon=folium.Icon(color='white', icon_color=record['marker_color'])
)
marker_cluster.add_child(marker)SpaceX Dash App Assignment Guide
# Import required libraries
import pandas as pd
import dash
import dash_html_components as html
import dash_core_components as dcc
from dash.dependencies import Input, Output
import plotly.express as px
# Read the airline data into pandas dataframe
spacex_df = pd.read_csv("spacex_launch_dash.csv")
max_payload = spacex_df['Payload Mass (kg)'].max()
min_payload = spacex_df['Payload Mass (kg)'].min()
# Create a dash application
app = dash.Dash(__name__)
# Create an app layout
app.layout = html.Div(children=[
html.H1('SpaceX Launch Records Dashboard',
style={'textAlign': 'center', 'color': '#503D36', 'font-size': 40}),
# TASK 1: Add a dropdown list to enable Launch Site selection
# The default select value is for ALL sites
# dcc.Dropdown(id='site-dropdown',...)
dcc.Dropdown(
id='site-dropdown',
options=[
{'label': 'All Sites', 'value': 'ALL'},
{'label': 'CCAFS LC-40', 'value': 'CCAFS LC-40'},
{'label': 'CCAFS SLC-40', 'value': 'CCAFS SLC-40'},
{'label': 'KSC LC-39A', 'value': 'KSC LC-39A'},
{'label': 'VAFB SLC-4E', 'value': 'VAFB SLC-4E'}
],
value='all',
placeholder="Select a Launch Site here",
searchable=True
),
html.Br(),
# TASK 2: Add a pie chart to show the total successful launches count for all sites
# If a specific launch site was selected, show the Success vs. Failed counts for the site
html.Div(dcc.Graph(id='success-pie-chart')),
html.Br(),
html.P("Payload range (Kg):"),
# TASK 3: Add a slider to select payload range
#dcc.RangeSlider(id='payload-slider',...)
dcc.RangeSlider(id='payload-slider',
min=0, max=10000, step=1000,
# marks={0: '0',
# 100: '100'},
value=[min_payload, max_payload]),
# TASK 4: Add a scatter chart to show the correlation between payload and launch success
html.Div(dcc.Graph(id='success-payload-scatter-chart')),
])
# TASK 2:
# Add a callback function for `site-dropdown` as input, `success-pie-chart` as output
@app.callback(Output(component_id='success-pie-chart', component_property='figure'),
Input(component_id='site-dropdown', component_property='value'))
#Place to define the callback function .
def get_pie_chart(entered_site):
filtered_df = spacex_df
if entered_site == 'ALL':
fig = px.pie(filtered_df, values='class', # use value 1 to calc success rate
names='Launch Site',
title='Total Success Launches by Site')
return fig
else:
# return the outcomes piechart for a selected site
filtered_df = filtered_df[filtered_df['Launch Site'] == entered_site]
fig = px.pie(filtered_df, #values='class', # no need to use value 1 to calc, just count 0 or 1
names='class',
title=f'Total Success Launches for {entered_site}')
return fig
# TASK 4:
# Add a callback function for `site-dropdown` and `payload-slider` as inputs, `success-payload-scatter-chart` as output
@app.callback(Output(component_id='success-payload-scatter-chart', component_property='figure'),
[Input(component_id='site-dropdown', component_property='value'),
Input(component_id="payload-slider", component_property="value")])
#Place to define the callback function .
def get_scatter_plot(entered_site, payload):
filtered_df = spacex_df
if entered_site == 'ALL':
fig = px.scatter(filtered_df, x='Payload Mass (kg)', y="class",
color="Booster Version Category",
title='Correlation between Payload and Success for all Sites')
return fig
else:
# return the outcomes piechart for a selected site
filtered_df = filtered_df[(filtered_df['Launch Site']==entered_site) \
& (filtered_df['Payload Mass (kg)'] >= payload[0]) \
& (filtered_df['Payload Mass (kg)'] <= payload[1])]
fig = px.scatter(filtered_df, x='Payload Mass (kg)', y="class",
color="Booster Version Category",
title=f'Correlation between Payload and Success for {entered_site}')
return fig
# Run the app
if __name__ == '__main__':
app.run_server()Plotly Dash Reference links:
import pandas as pd
from sklearn import preprocessing
from sklearn.model_selection import train_test_split
from sklearn.model_selection import GridSearchCV
from sklearn.neighbors import KNeighborsClassifier
def plot_confusion_matrix(y,y_predict):
"this function plots the confusion matrix"
from sklearn.metrics import confusion_matrix
cm = confusion_matrix(y, y_predict)
ax= plt.subplot()
sns.heatmap(cm, annot=True, ax = ax); #annot=True to annotate cells
ax.set_xlabel('Predicted labels')
ax.set_ylabel('True labels')
ax.set_title('Confusion Matrix');
ax.xaxis.set_ticklabels(['did not land', 'land']); ax.yaxis.set_ticklabels(['did not land', 'landed'])
# load data
data = pd.read_csv("dataset_part_2.csv")
X = pd.read_csv('dataset_part_3.csv')
Y = data['Class'].to_numpy()
X = preprocessing.StandardScaler().fit(X).transform(X)
# split data
X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.2, random_state=2)
print ('Train set:', X_train.shape, Y_train.shape)
print ('Test set:', X_test.shape, Y_test.shape)
# build model to search best parameter
parameters = {'n_neighbors': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10],
'algorithm': ['auto', 'ball_tree', 'kd_tree', 'brute'],
'p': [1,2]}
KNN = KNeighborsClassifier()
knn_cv = GridSearchCV(KNN, parameters, cv=10)
knn_cv.fit(X,Y)
print("tuned hpyerparameters :(best parameters) ",knn_cv.best_params_)
print("accuracy :",knn_cv.best_score_)
# accuracy on test data
knn_cv.score(X_test, Y_test)
# plot the confusion matrix
yhat = knn_cv.predict(X_test)
plot_confusion_matrix(Y_test,yhat)- Cover Page
- Executive Summary
- Table of Contents
- Introduction
- Methodology
- Results
- Discussion
- Conclusion
- Appendix