Weather Data Using Decision Trees

import pandas as pd from sklearn.metrics import accuracy_score from sklearn.model_selection import train_test_split from sklearn.tree import DecisionTreeClassifier

data = pd.read_csv('./weather/daily_weather.csv')

data.columns

data

data[data.isnull().any(axis=1)]

del data['number']

before_rows = data.shape[0] print(before_rows)

data = data.dropna()

after_rows = data.shape[0] print(after_rows)

before_rows - after_rows

clean_data = data.copy() clean_data['high_humidity_label'] = (clean_data['relative_humidity_3pm'] > 24.99)*1 print(clean_data['high_humidity_label'])

y=clean_data[['high_humidity_label']].copy() #y

clean_data['relative_humidity_3pm'].head()

y.head()

morning_features = ['air_pressure_9am','air_temp_9am','avg_wind_direction_9am','avg_wind_speed_9am', 'max_wind_direction_9am','max_wind_speed_9am','rain_accumulation_9am', 'rain_duration_9am']

X = clean_data[morning_features].copy()

X.columns

y.columns

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=324)

#type(X_train) #type(X_test) #type(y_train) #type(y_test) #X_train.head() #y_train.describe()

humidity_classifier = DecisionTreeClassifier(max_leaf_nodes=10, random_state=0) humidity_classifier.fit(X_train, y_train)

type(humidity_classifier)

predictions = humidity_classifier.predict(X_test)

predictions[:10]

y_test['high_humidity_label'][:10]

accuracy_score(y_true = y_test, y_pred = predictions)