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pred_total_amount_pipeline.py
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pred_total_amount_pipeline.py
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import pandas as pd
from sklearn.model_selection import train_test_split
#Algorithms packages from scikit-learn
from sklearn.linear_model import LogisticRegression
from sklearn.ensemble import AdaBoostClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.naive_bayes import GaussianNB
from sklearn.neighbors import KNeighborsClassifier
from sklearn.neural_network import MLPClassifier
from sklearn.svm import LinearSVC
#For Pipelining
from sklearn.preprocessing import StandardScaler
from sklearn.decomposition import PCA
from sklearn.pipeline import Pipeline
pred_amt_with_all_columns = pd.read_csv("./Github/NYC-College-Taxi/extracted_data/pred_amt_with_all_columns.csv")
print pred_amt_with_all_columns.shape
file_list = [pred_amt_with_all_columns]
print pred_amt_with_all_columns.shape
print pred_amt_with_all_columns.columns.values
# Construct some pipelines
pipeline_lr = Pipeline([('scl', StandardScaler()),
('pca', PCA(n_components=15)),
('lr', LogisticRegression())])
pipeline_svm = Pipeline([('scl', StandardScaler()),
('pca', PCA(n_components=15)),
('svc', LinearSVC())])
pipeline_rf = Pipeline([('scl', StandardScaler()),
('pca', PCA(n_components=15)),
('rfc', RandomForestClassifier(random_state=1))])
pipeline_gnb = Pipeline([('scl', StandardScaler()),
('pca', PCA(n_components=15)),
('gnb', GaussianNB())])
pipeline_ada = Pipeline([('scl', StandardScaler()),
('pca', PCA(n_components=15)),
('ada', AdaBoostClassifier(random_state=1))])
pipeline_knn = Pipeline([('scl', StandardScaler()),
('pca', PCA(n_components=15)),
('mlp', MLPClassifier())])
pipeline_mlp = Pipeline([('scl', StandardScaler()),
('pca', PCA(n_components=15)),
('knn', KNeighborsClassifier())])
pipelines = [pipeline_lr, pipeline_svm, pipeline_rf, pipeline_gnb, pipeline_ada, pipeline_knn, pipeline_mlp]
X = pred_amt_with_all_columns.iloc[:, :]
X = X.drop("total_amount", 1)
y = pred_amt_with_all_columns.total_amount
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=42)
pipeline_dictionary = {0: 'Logistic Regression',
1: 'Support Vector Machine',
2: 'Random Forest',
3: 'Gaussian Naive Bayes',
4: 'ADABoost',
5: 'K-Nearest Neighbour',
6: 'Multi-Layer Perceptron'
}
for pipe in pipelines:
pipe.fit(X_train, y_train)
for index, value in enumerate(pipelines):
print('%s pipeline accuracy: %.3f' % (pipeline_dictionary[index], value.score(X_test, y_test)))