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Commit e63f88cd authored by Fadi Gattoussi's avatar Fadi Gattoussi
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Add data for 2019

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%% Cell type:code id: tags:
``` python
import pandas as pd
import sklearn as sk
import matplotlib.pyplot as plt
import json
import math
```
%% Cell type:code id: tags:
``` python
cf_2018 = pd.read_csv('flight_data/Combined_Flights_2018.csv')
cf_2019 = pd.read_csv('flight_data/Combined_Flights_2019.csv')
cf_2018.head()
```
%% Output
FlightDate Airline Origin Dest Cancelled Diverted CRSDepTime \
0 2018-01-23 Endeavor Air Inc. ABY ATL False False 1202
1 2018-01-24 Endeavor Air Inc. ABY ATL False False 1202
2 2018-01-25 Endeavor Air Inc. ABY ATL False False 1202
3 2018-01-26 Endeavor Air Inc. ABY ATL False False 1202
4 2018-01-27 Endeavor Air Inc. ABY ATL False False 1400
DepTime DepDelayMinutes DepDelay ... WheelsOff WheelsOn TaxiIn \
0 1157.0 0.0 -5.0 ... 1211.0 1249.0 7.0
1 1157.0 0.0 -5.0 ... 1210.0 1246.0 12.0
2 1153.0 0.0 -9.0 ... 1211.0 1251.0 11.0
3 1150.0 0.0 -12.0 ... 1207.0 1242.0 11.0
4 1355.0 0.0 -5.0 ... 1412.0 1448.0 11.0
CRSArrTime ArrDelay ArrDel15 ArrivalDelayGroups ArrTimeBlk \
0 1304 -8.0 0.0 -1.0 1300-1359
1 1304 -6.0 0.0 -1.0 1300-1359
2 1304 -2.0 0.0 -1.0 1300-1359
3 1304 -11.0 0.0 -1.0 1300-1359
4 1500 -1.0 0.0 -1.0 1500-1559
DistanceGroup DivAirportLandings
0 1 0.0
1 1 0.0
2 1 0.0
3 1 0.0
4 1 0.0
[5 rows x 61 columns]
---------------------------------------------------------------------------
NameError Traceback (most recent call last)
Cell In[2], line 1
----> 1 cf_2018 = pd.read_csv('flight_data/Combined_Flights_2019.csv')
2 cf_2018.head()
NameError: name 'pd' is not defined
%% Cell type:code id: tags:
``` python
# Filter the dataframe to include only the delays from JFK
import RegressionModel
filtered_df = cf_2018[(cf_2018['Origin'] == 'JFK')]
filtered_df = cf_2018[(cf_2018['Origin'] == 'JFK')].copy()
RegressionModel.destinations = list(cf_2018['DestCityName'].unique())
filtered_df
```
%% Cell type:code id: tags:
``` python
relevant_columns = ['FlightDate', 'Airline', 'Dest', 'DepDelayMinutes', 'ArrDelayMinutes']
relevant_columns = ['FlightDate', 'DOT_ID_Operating_Airline', 'DestAirportID', 'DepDelayMinutes', 'ArrDelayMinutes']
jfk_flights_2018 = filtered_df[relevant_columns].copy()
jfk_flights_2018.dropna(inplace=True)
```
%% Cell type:code id: tags:
``` python
jfk_flights_2018
```
%% Output
FlightDate Airline Dest DepDelayMinutes \
5544 2018-01-22 JetBlue Airways SJC 0.0
5547 2018-01-22 JetBlue Airways MSY 86.0
5548 2018-01-22 JetBlue Airways JAX 29.0
5554 2018-01-22 JetBlue Airways ROC 0.0
5565 2018-01-22 JetBlue Airways BUF 0.0
... ... ... ... ...
5666920 2018-09-10 Delta Air Lines Inc. SFO 0.0
5666921 2018-09-10 Delta Air Lines Inc. MSP 0.0
5667057 2018-09-10 Delta Air Lines Inc. LAX 13.0
5667231 2018-09-10 Delta Air Lines Inc. SEA 48.0
5672362 2018-09-04 Endeavor Air Inc. BNA 0.0
ArrDelayMinutes
5544 0.0
5547 77.0
5548 11.0
5554 0.0
5565 0.0
... ...
5666920 3.0
5666921 0.0
5667057 0.0
5667231 44.0
5672362 0.0
[83754 rows x 5 columns]
%% Cell type:code id: tags:
``` python
# Convert 'FlightDate' into numerical components
jfk_flights_2018['FlightDate'] = pd.to_datetime(jfk_flights_2018['FlightDate'])
jfk_flights_2018['Year'] = jfk_flights_2018['FlightDate'].dt.year
jfk_flights_2018['Month'] = jfk_flights_2018['FlightDate'].dt.month
jfk_flights_2018['Day'] = jfk_flights_2018['FlightDate'].dt.day
# One-hot encoding for categorical variables
df_jfk_encoded = pd.get_dummies(jfk_flights_2018, columns=['Dest'])
df_jfk_encoded.head()
jfk_flights_2018 = jfk_flights_2018.dropna()
```
%% Output
FlightDate Airline DepDelayMinutes ArrDelayMinutes Year \
5544 2018-01-22 JetBlue Airways 0.0 0.0 2018
5547 2018-01-22 JetBlue Airways 86.0 77.0 2018
5548 2018-01-22 JetBlue Airways 29.0 11.0 2018
5554 2018-01-22 JetBlue Airways 0.0 0.0 2018
5565 2018-01-22 JetBlue Airways 0.0 0.0 2018
Month Day Dest_ABQ Dest_ACK Dest_ATL ... Dest_SFO Dest_SJC \
5544 1 22 False False False ... False True
5547 1 22 False False False ... False False
5548 1 22 False False False ... False False
5554 1 22 False False False ... False False
5565 1 22 False False False ... False False
Dest_SJU Dest_SLC Dest_SMF Dest_SNA Dest_SRQ Dest_STT Dest_SYR \
5544 False False False False False False False
5547 False False False False False False False
5548 False False False False False False False
5554 False False False False False False False
5565 False False False False False False False
Dest_TPA
5544 False
5547 False
5548 False
5554 False
5565 False
[5 rows x 78 columns]
%% Cell type:code id: tags:
``` python
from sklearn.model_selection import train_test_split
# Choose your target variable, e.g., 'DepDelayMinutes'
X = df_jfk_encoded.drop('DepDelayMinutes', axis=1)
y = df_jfk_encoded['DepDelayMinutes']
# plot the distribution of the target variable
plt.hist(y, bins=100)
plt.xlabel('Departure Delay (minutes)')
plt.ylabel('Frequency')
plt.show()
```
%% Output
X = jfk_flights_2018.drop('DepDelayMinutes', axis=1)
y = jfk_flights_2018['DepDelayMinutes']
%% Cell type:code id: tags:
``` python
# Splitting the dataset into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
X_train = X_train.apply(pd.to_numeric, errors='coerce')
y_train = pd.to_numeric(y_train, errors='coerce')
X_test = X_test.apply(pd.to_numeric, errors='coerce')
y_test = pd.to_numeric(y_test, errors='coerce')
jfk_flights_2018
```
%% Cell type:code id: tags:
``` python
# Splitting the dataset into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
X_train = X_train.apply(pd.to_numeric, errors='coerce')
y_train = pd.to_numeric(y_train, errors='coerce')
y_train = y_train.apply(pd.to_numeric, errors='coerce')
X_test = X_test.apply(pd.to_numeric, errors='coerce')
y_test = pd.to_numeric(y_test, errors='coerce')
y_test = y_test.apply(pd.to_numeric, errors='coerce')
```
%% Cell type:code id: tags:
``` python
from sklearn.linear_model import LinearRegression
# Initialize the model
model = LinearRegression()
# Train the model
model.fit(X_train, y_train)
```
%% Cell type:code id: tags:
``` python
from sklearn.metrics import mean_squared_error, r2_score
# Predict on the test set
y_pred = model.predict(X_test)
# Evaluate the model
mse = mean_squared_error(y_test, y_pred)
r2 = r2_score(y_test, y_pred)
print("Mean Squared Error:", mse)
print("R^2 Score:", r2)
```
%% Cell type:code id: tags:
``` python
print(list(X_test))
```
%% Cell type:code id: tags:
``` python
i = X_test.iloc[[3]].copy()
i['FlightDate'] = 1640995200000000000
i['Year'] = 2022
i['Month'] = 1
i['Day'] = 1
print(i)
```
%% Cell type:code id: tags:
``` python
X_mike = X_test.iloc[[3]]
```
%% Cell type:code id: tags:
``` python
model.predict(i)
```
......
training2.ipynb 0 → 100644
+ 220
− 0
View file @ e63f88cd
%% Cell type:code id: tags:
``` python
import pandas as pd
import sklearn as sk
import matplotlib.pyplot as plt
import json
import math
```
%% Cell type:code id: tags:
``` python
cf_2018 = pd.read_csv('flight_data/Combined_Flights_2018.csv')
cf_2019 = pd.read_csv('flight_data/Combined_Flights_2019.csv')
combined_data = pd.concat(cf_2018, cf_2019)
cf_2018 = combined_data
```
%% Output
The Kernel crashed while executing code in the current cell or a previous cell. Please review the code in the cell(s) to identify a possible cause of the failure. Click <a href='https://aka.ms/vscodeJupyterKernelCrash'>here</a> for more info. View Jupyter <a href='command:jupyter.viewOutput'>log</a> for further details.
%% Cell type:code id: tags:
``` python
# Filter the dataframe to include only the delays from JFK
import RegressionModel
filtered_df = cf_2018[(cf_2018['Origin'] == 'JFK')].copy()
RegressionModel.destinations = list(cf_2018['DestCityName'].unique())
filtered_df
```
%% Cell type:code id: tags:
``` python
relevant_columns = ['FlightDate', 'DOT_ID_Operating_Airline', 'DestAirportID', 'DepDelayMinutes', 'ArrDelayMinutes']
jfk_flights_2018 = filtered_df[relevant_columns].copy()
jfk_flights_2018.dropna(inplace=True)
```
%% Cell type:code id: tags:
``` python
jfk_flights_2018
```
%% Cell type:code id: tags:
``` python
# Convert 'FlightDate' into numerical components
jfk_flights_2018['FlightDate'] = pd.to_datetime(jfk_flights_2018['FlightDate'])
jfk_flights_2018['Year'] = jfk_flights_2018['FlightDate'].dt.year
jfk_flights_2018['Month'] = jfk_flights_2018['FlightDate'].dt.month
jfk_flights_2018['Day'] = jfk_flights_2018['FlightDate'].dt.day
jfk_flights_2018 = jfk_flights_2018.dropna()
```
%% Cell type:code id: tags:
``` python
from sklearn.model_selection import train_test_split
# Choose your target variable, e.g., 'DepDelayMinutes'
X = jfk_flights_2018.drop('DepDelayMinutes', axis=1)
y = jfk_flights_2018['DepDelayMinutes']
jfk_flights_2018
```
%% Cell type:code id: tags:
``` python
# Splitting the dataset into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
X_train = X_train.apply(pd.to_numeric, errors='coerce')
y_train = y_train.apply(pd.to_numeric, errors='coerce')
X_test = X_test.apply(pd.to_numeric, errors='coerce')
y_test = y_test.apply(pd.to_numeric, errors='coerce')
```
%% Cell type:code id: tags:
``` python
from sklearn.linear_model import LinearRegression
# Initialize the model
model = LinearRegression()
# Train the model
model.fit(X_train, y_train)
```
%% Cell type:code id: tags:
``` python
from sklearn.metrics import mean_squared_error, r2_score
# Predict on the test set
y_pred = model.predict(X_test)
# Evaluate the model
mse = mean_squared_error(y_test, y_pred)
r2 = r2_score(y_test, y_pred)
print("Mean Squared Error:", mse)
print("R^2 Score:", r2)
```
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