https://github.com/vikas-ukani/predict-flight-delays-creating-a-machine-learning-model-python
Import airline arrival data into a Jupyter notebook and use Pandas to clean it. Then, build a machine learning model with Scikit-Learn and use Matplotlib to visualize output.
https://github.com/vikas-ukani/predict-flight-delays-creating-a-machine-learning-model-python
Last synced: 3 months ago
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Import airline arrival data into a Jupyter notebook and use Pandas to clean it. Then, build a machine learning model with Scikit-Learn and use Matplotlib to visualize output.
- Host: GitHub
- URL: https://github.com/vikas-ukani/predict-flight-delays-creating-a-machine-learning-model-python
- Owner: vikas-ukani
- Created: 2020-05-29T11:14:27.000Z (over 5 years ago)
- Default Branch: master
- Last Pushed: 2020-05-29T11:26:07.000Z (over 5 years ago)
- Last Synced: 2025-03-16T22:44:37.381Z (7 months ago)
- Language: HTML
- Size: 166 KB
- Stars: 3
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# predict-flight-delays-creating-a-machine-learning-model-python
Import airline arrival data into a Jupyter notebook and use Pandas to clean it. Then, build a machine learning model with Scikit-Learn and use Matplotlib to visualize output.
In this module, you will:
Create an Azure Notebook and import flight data
Use Pandas to clean and prepare data
Use Scikit-learn to build a machine-learning model
Use Matplotlib to visualize the output
#!/usr/bin/env python
# coding: utf-8
# In[1]:
get_ipython().system(u'curl https://topcs.blob.core.windows.net/public/FlightData.csv -o flightdata.csv')
# In[ ]:
# In[2]:
import pandas as pd
# In[4]:
df = pd.read_csv('flightdata.csv')
df.head()
# In[8]:
df.shape
# In[13]:
# Check for empty or null values availables
df.isnull().values.any()
# In[14]:
df.isnull().sum()
# In[15]:
# Drop
df = df.drop('Unnamed: 25', axis=1)
df.isnull().sum()
# In[16]:
# Filter columns
# In[17]:
df = df[["MONTH", "DAY_OF_MONTH", "DAY_OF_WEEK", "ORIGIN", "DEST", "CRS_DEP_TIME", "ARR_DEL15"]]
df.isnull().sum()
# In[20]:
# Full 0 to ARR_DEL15 column
df[df.isnull().values.any(axis=1)].head()
# In[23]:
# Fill 1 to all null values
df = df.fillna({"ARR_DEL15": 1})
df.iloc[177:185]
# In[25]:
df.head()
# In[26]:
# Binning the depature time
import math
for index, row in df.iterrows():
df.loc[index, 'CRS_DEP_TIME'] = math.floor(row[ 'CRS_DEP_TIME'] / 100 )
df.head()
# In[73]:
df = pd.get_dummies(df, columns=['ORIGIN' , 'DEST'])
df.head()
# In[ ]:
#
# # Build Machine Learning Model
# In[74]:
# Load package
from sklearn.model_selection import train_test_split
# In[75]:
# Train model
X = df.drop('ARR_DEL15', axis=1)
y = df['ARR_DEL15']
train_x, test_x, train_y, test_y = train_test_split(df.drop('ARR_DEL15', axis=1), df['ARR_DEL15'], test_size=0.2, random_state=42)
# In[76]:
print(train_x.shape)
print(train_y.shape)
# In[77]:
print(test_x.shape)
print(test_y.shape)
# # RandomForestClassifire Model Applying
#
# In[78]:
from sklearn.ensemble import RandomForestClassifier
# import warnings
# warnings.filterwarnings('ignore')
# In[79]:
# Fit the data
model = RandomForestClassifier(random_state=13)
model.fit(train_x, train_y)
# In[80]:
# Predict Model
predicted = model.predict(test_x)
model.score(test_x, test_y)
# In[ ]:
# # Area Under Receiver Operating Characteristic Curve Applying
# In[81]:
from sklearn.metrics import roc_auc_score
# In[82]:
probabilities = model.predict_proba(test_x)
# In[84]:
# Generating an AUC score
roc_auc_score(test_y, probabilities[:, 1])
# In[ ]:
# # produce a confusion matrix
# In[85]:
from sklearn.metrics import confusion_matrix
# In[86]:
confusion_matrix(test_y, predicted)
# In[88]:
# Measuring precision
from sklearn.metrics import precision_score
train_predictions = model.predict(train_x)
precision_score(train_y, train_predictions)
# In[89]:
# Masering Recall score
from sklearn.metrics import recall_score
recall_score(train_y, train_predictions)
# In[ ]:
# # Visualize Output of Model
# In[95]:
# Import packages for visulaize data
import matplotlib.pyplot as plt
get_ipython().magic(u'matplotlib inline')
import seaborn as sns
sns.set()
# In[100]:
# Create first plot
from sklearn.metrics import roc_curve
fpr, tpr, _ = roc_curve(test_y, probabilities[:, 1])
# ROC curve generated with Matplotlib
plt.plot(fpr, tpr)
plt.plot([0,1], [0,1], color='grey', lw=1, linestyle='--')
plt.xlabel("False Positive Rate")
plt.ylabel("True Positive Rate")
plt.show()
# In[102]:
def predict_delay(departure_date_time, origin, destination):
from datetime import datetime
try:
departure_date_time_parsed = datetime.strptime(departure_date_time, '%d/%m/%Y %H:%M:%S')
except ValueError as e:
return 'Error parsing date/time - {}'.format(e)
month = departure_date_time_parsed.month
day = departure_date_time_parsed.day
day_of_week = departure_date_time_parsed.isoweekday()
hour = departure_date_time_parsed.hour
origin = origin.upper()
destination = destination.upper()
input = [{'MONTH': month,
'DAY': day,
'DAY_OF_WEEK': day_of_week,
'CRS_DEP_TIME': hour,
'ORIGIN_ATL': 1 if origin == 'ATL' else 0,
'ORIGIN_DTW': 1 if origin == 'DTW' else 0,
'ORIGIN_JFK': 1 if origin == 'JFK' else 0,
'ORIGIN_MSP': 1 if origin == 'MSP' else 0,
'ORIGIN_SEA': 1 if origin == 'SEA' else 0,
'DEST_ATL': 1 if destination == 'ATL' else 0,
'DEST_DTW': 1 if destination == 'DTW' else 0,
'DEST_JFK': 1 if destination == 'JFK' else 0,
'DEST_MSP': 1 if destination == 'MSP' else 0,
'DEST_SEA': 1 if destination == 'SEA' else 0 }]
return model.predict_proba(pd.DataFrame(input))[0][0]
# In[107]:
predict_delay('01/05/2020 21:45:00', 'JFK', 'ATL')
# In[108]:
predict_delay('2/10/2018 10:00:00', 'ATL', 'SEA')
# In[118]:
# Use differ
import numpy as np
label = ('Oct 1', 'Oct 2', 'Oct 3', 'Oct 4', 'Oct 5', 'Oct 6', 'Oct 7')
values = (predict_delay('1/10/2018 21:45:00', 'JFK', 'ATL'),
predict_delay('2/10/2018 21:45:00', 'JFK', 'ATL'),
predict_delay('3/10/2018 21:45:00', 'JFK', 'ATL'),
predict_delay('4/10/2018 21:45:00', 'JFK', 'ATL'),
predict_delay('5/10/2018 21:45:00', 'JFK', 'ATL'),
predict_delay('6/10/2018 21:45:00', 'JFK', 'ATL'),
predict_delay('7/10/2018 21:45:00', 'JFK', 'ATL'))
alabels = np.arange(len(label))
plt.bar(alabels, values, align='center', alpha=0.5)
plt.xticks(alabels, label)
plt.ylabel('Probability of On-Time Arrival')
plt.ylim((0.0, 1.0))
plt.title('Probability of on-time arrivals for a range of dates')
plt.show()
# In[ ]: