最初の提出を準備する

Pythonで挑むKaggleコンペティション

Yauhen Babakhin

Kaggle Grandmaster

提出とは

Kaggle のコンペプロセス

NYC タクシー運賃の予測

# Read train data
taxi_train = pd.read_csv('taxi_train.csv')
taxi_train.columns.to_list()

['key',
 'fare_amount',
 'pickup_datetime',
 'pickup_longitude',
 'pickup_latitude',
 'dropoff_longitude',
 'dropoff_latitude',
 'passenger_count']

問題の種類

import matplotlib.pyplot as plt

# Plot a histogram
taxi_train.fare_amount.hist(bins=30, alpha=0.5)
plt.show()

運賃のヒストグラム

モデルを構築する

from sklearn.linear_model import LinearRegression

# Create a LinearRegression object
lr = LinearRegression()

# Fit the model on the train data
lr.fit(X=taxi_train[['pickup_longitude', 'pickup_latitude', 'dropoff_longitude',
                     'dropoff_latitude', 'passenger_count']],
       y=taxi_train['fare_amount'])

テストセットで予測する

# Select features
features = ['pickup_longitude', 'pickup_latitude',
            'dropoff_longitude', 'dropoff_latitude',
            'passenger_count']

# Make predictions on the test data
taxi_test['fare_amount'] = lr.predict(taxi_test[features])

提出を準備する

# Read a sample submission file
taxi_sample_sub = pd.read_csv('taxi_sample_submission.csv')
taxi_sample_sub.head(1)

                              key     fare_amount
0     2015-01-27 13:08:24.0000002     11.35

# Prepare a submission file
taxi_submission = taxi_test[['key', 'fare_amount']]

# Save the submission file as .csv
taxi_submission.to_csv('first_sub.csv', index=False)

Let's practice!

Pythonで挑むKaggleコンペティション