Kaggle competition: Aerial Cactus Identification
<SOS> metric: AUC Kaggle data source: aerial-cactus-identification<save_to_csv>
from fastai.vision import *
import pandas as pd
import numpy as np
survivors.to_csv('Submission.csv', index = False )<load_from_csv>
train_df = pd.read_csv(".. /input/aerial-cactus-identification/train.csv")
 test_df = pd.read_csv(".. /input/aerial-cactus-identification/sample_submission.csv" )
dataframe = pd.read_csv("/kaggle/input/titanic/train.csv")
 test = pd.read_csv('/kaggle/input/titanic/test.csv')
<create_dataframe>
learn = cnn_learner(train_img, models.resnet50, metrics=[error_rate, accuracy] )
sns.countplot(dataframe['Survived'], hue = dataframe['Sex'])
 Dead, lives = dataframe.Survived.value_counts()
male, female = dataframe.Sex.value_counts()
print("Percentage of Male on ship:", round(male/(male+female)*100))
print("Percentage of Female on ship:", round(female/(male+female)*100))<count_values>
learn.fit_one_cycle(5 )
dataframe.Pclass.unique()
dataframe.Pclass.value_counts() 
<create_dataframe>
learn.lr_find(start_lr=1e-5, end_lr=1e-1)
 learn.recorder.plot()
sns.countplot(dataframe['Embarked'], hue = dataframe['Survived'] )<count_missing_values>
lr = 1e-04
dataframe.isnull().values.any()
dataframe.isnull().sum() 
<count_missing_values>
learn.fit_one_cycle(2, max_lr=lr )
test.isnull().sum()<count_missing_values>
probability, classification = learn.get_preds(ds_type=DatasetType.Test)
 test_df.has_cactus = probability.numpy() [:, 0]
test_df.head()
<count_missing_values><EOS> 
test_df.to_csv("submission.csv", index=False )