Day #58 - Hacker Earth Challenge

With Running projects, its bit challenging to manage multiple tasks. Bookmarking my thoughts until further analysis

Problem - Link

Data Analysis (Approach)

• Load data in SQL Tables
• Analyze each column, Continous or Discrete variables
• Outliers, missing data, summary of each Data Column
• Manage Class Imbalances
• Convert the dataset into numeric columns
• Ignore any non-critical columns
• Identify Data Correlations if it exists (Pending task)

The Approach
1. To eliminate class imbalance used smote technique
2. Used XGBoost to train and predict
3. Python 2.7 used. Two files one for Data cleanup, second for prediction

	#Approach #1
	from sklearn import svm
	from imblearn.over_sampling import SMOTE
	#SMOTE and SVM
	y1 = df['loan_status']
	x1 = df[features]
	sm = SMOTE(kind='svm')
	x, y = sm.fit_sample(x1, y1)
	model = svm.SVC(decision_function_shape='ovo')
	model.fit(x,y)
	predictions = model.predict(x_test)
	#Approach #2
	#PCA & SVM
	from sklearn.decomposition import PCA
	from sklearn.svm import SVC
	y1 = df['loan_status']
	x1 = df[features]
	pca = PCA(n_components=10)
	x1_train_pca = pca.fit_transform(x1)
	clf=SVC(probability=True)
	clf.fit(x1_train_pca,y1)
	pca = PCA(n_components=10)
	x_test_pca = pca.fit_transform(x_test)
	predictions = clf.predict_proba(x_test_pca)

view raw Approach.py hosted with ❤ by GitHub

Happy Learning!!!