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File Name:Structured_test
Step 1: –
import pandas as pd
import numpy as np
import dataframe as df
Step 2:-
weather = pd.read_csv(‘weather.csv’, sep=’,’)
Step 3:-
data_size=weather.shape
print(data_size)
weather_col_names = list(weather.columns)
print(weather_col_names)
print(weather.describe())
print(weather.head(3))
Step 4:-
weather_target=weather[‘RainTomorrow’]
print(weather_target)
Step 5:-
cols_to_drop = [‘Date’,’RainTomorrow’]
weather_feature = weather.drop(cols_to_drop,axis = 1)
print(weather_feature.head(5))
Step 6: –
weather_categorical = weather.select_dtypes(include=[object])
print(weather_categorical.head(15))
Step 7:-
yes_no_cols = [“RainToday”]
weather_feature[yes_no_cols] = weather_feature[yes_no_cols] == ‘Yes’
print(weather_feature.head(5))
Step 8:-
weather_dumm=pd.get_dummies(weather_feature, columns=[“Location”,”WindGustDir”,”WindDir9am”,”WindDir3pm”], prefix=[“Location”,”WindGustDir”,”WindDir9am”,”WindDir3pm”])
weather_matrix = weather_dumm.values.astype(np.float)
Step 9:-
from sklearn.impute import SimpleImputer
imp=SimpleImputer(missing_values=np.nan,strategy=’mean’, fill_value=None,verbose=0,copy=True)
weather_matrix=imp.fit_transform(weather_matrix)
Step 10:-
from sklearn.preprocessing import StandardScaler
#Standardize the data by removing the mean and scaling to unit variance
scaler = StandardScaler()
#Fit to data, then transform it.
weather_matrix = scaler.fit_transform(weather_matrix)
Step 11:-
from sklearn.model_selection import train_test_split
seed=5000
train_data,test_data, train_label, test_label = train_test_split(weather_matrix,weather_target,test_size=0.1,random_state = seed)
Step 12:-
from sklearn.svm import SVC
classifier = SVC(kernel=”linear”,C=0.025,random_state=seed )
classifier = classifier.fit(train_data,train_label)
churn_predicted_target=classifier.predict(test_data)
score = classifier.score(test_data,test_label)
print(‘SVM Classifier : ‘,score)
with open(‘output.txt’, ‘w’) as file:
file.write(str(np.mean(score)))
Step 13:-
from sklearn.ensemble import RandomForestClassifier
classifier = RandomForestClassifier(max_depth=5,n_estimators=10,max_features=10,random_state=seed)
classifier = classifier.fit(train_data,train_label)
churn_predicted_target=classifier.predict(test_data)
score = classifier.score(test_data,test_label)
print(‘Random Forest Classifier : ‘,score)
with open(‘output1.txt’, ‘w’) as file:
file.write(str(np.mean(score)))