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Churn-prediction

Data preprocessing:: Label encoder is used to normalize the categorical variable

Data Transformation:: For each data transformation sepeate fiels are used Base line (RAW) is used for Original dataset without any data transformation

Feature selection:: Univarient feature selection is used for feature selection

Paremeter tunning:: Grid Search CV is used for parameter tuning for the classifiers

Dataset:: Four datasets are used in our study

data set 1 Telecom_customer churn (10000).rar
churn-5000.csv
churn-data-333.csv
churn-7043.csv

Name		Name	Last commit message	Last commit date
Latest commit History 12 Commits
.gitattributes		.gitattributes
BOX-COX-Recursive feature elimination(RFE).ipynb		BOX-COX-Recursive feature elimination(RFE).ipynb
BOX-COX-Univariate feature selection-3333.ipynb		BOX-COX-Univariate feature selection-3333.ipynb
BOX-COX-Univariate feature selection-5000.ipynb		BOX-COX-Univariate feature selection-5000.ipynb
BOX-COX-Univariate feature selection-7043.ipynb		BOX-COX-Univariate feature selection-7043.ipynb
BOX-COX-Univariate feature selection.ipynb		BOX-COX-Univariate feature selection.ipynb
Baseline(RAW)-cross-validation-3333-data.ipynb		Baseline(RAW)-cross-validation-3333-data.ipynb
Baseline(RAW)-cross-validation-5000-data.ipynb		Baseline(RAW)-cross-validation-5000-data.ipynb
Baseline(RAW)-cross-validation-7043-data.ipynb		Baseline(RAW)-cross-validation-7043-data.ipynb
Baseline(RAW)-cross-validation-7043.ipynb		Baseline(RAW)-cross-validation-7043.ipynb
Baseline(RAW)-cross-validation.ipynb		Baseline(RAW)-cross-validation.ipynb
Box-Cox-cross-validation-3333-data.ipynb		Box-Cox-cross-validation-3333-data.ipynb
Box-Cox-cross-validation-5000-data.ipynb		Box-Cox-cross-validation-5000-data.ipynb
Class-AsiaConnect.ipynb		Class-AsiaConnect.ipynb
DISCRITIZATION-Recursive feature elimination(RFE).ipynb		DISCRITIZATION-Recursive feature elimination(RFE).ipynb
DISCRITIZATION-Univariate feature selection-3333.ipynb		DISCRITIZATION-Univariate feature selection-3333.ipynb
DISCRITIZATION-Univariate feature selection-5000.ipynb		DISCRITIZATION-Univariate feature selection-5000.ipynb
DISCRITIZATION-Univariate feature selection-7043.ipynb		DISCRITIZATION-Univariate feature selection-7043.ipynb
DISCRITIZATION-Univariate feature selection.ipynb		DISCRITIZATION-Univariate feature selection.ipynb
Graph.ipynb		Graph.ipynb
LOG-Recursive feature elimination(RFE).ipynb		LOG-Recursive feature elimination(RFE).ipynb
LOG-Univariate feature selection-3333.ipynb		LOG-Univariate feature selection-3333.ipynb
LOG-Univariate feature selection-5000.ipynb		LOG-Univariate feature selection-5000.ipynb
LOG-Univariate feature selection.ipynb		LOG-Univariate feature selection.ipynb
Log-Univariate feature selection-7043.ipynb		Log-Univariate feature selection-7043.ipynb
Model-selection.ipynb		Model-selection.ipynb
Pearson Correlation Graph.ipynb		Pearson Correlation Graph.ipynb
RANK-Recursive feature elimination(RFE).ipynb		RANK-Recursive feature elimination(RFE).ipynb
RANK-Univariate feature selection-3333.ipynb		RANK-Univariate feature selection-3333.ipynb
RANK-Univariate feature selection-5000.ipynb		RANK-Univariate feature selection-5000.ipynb
RANK-Univariate feature selection-7043.ipynb		RANK-Univariate feature selection-7043.ipynb
RANK-Univariate feature selection.ipynb		RANK-Univariate feature selection.ipynb
README.md		README.md
SPARK_TELECOM_BOX-COX_DT_Logistic.txt		SPARK_TELECOM_BOX-COX_DT_Logistic.txt
TEST_BED.ipynb		TEST_BED.ipynb
WOE-Recursive feature elimination(RFE).ipynb		WOE-Recursive feature elimination(RFE).ipynb
WOE-Univariate feature selection--asus laptop.ipynb		WOE-Univariate feature selection--asus laptop.ipynb
WOE-Univariate feature selection-3333.ipynb		WOE-Univariate feature selection-3333.ipynb
WOE-Univariate feature selection-5000.ipynb		WOE-Univariate feature selection-5000.ipynb
WOE-Univariate feature selection-7043.ipynb		WOE-Univariate feature selection-7043.ipynb
WOE-Univariate feature selection.ipynb		WOE-Univariate feature selection.ipynb
WeightOfEvidence--NaiveBayes-RF-deepLearning.ipynb		WeightOfEvidence--NaiveBayes-RF-deepLearning.ipynb
WeightOfEvidence-Cross-validation.ipynb		WeightOfEvidence-Cross-validation.ipynb
ZScore-Recursive feature elimination(RFE).ipynb		ZScore-Recursive feature elimination(RFE).ipynb
ZScore-Univariate feature selection-3333.ipynb		ZScore-Univariate feature selection-3333.ipynb
ZScore-Univariate feature selection-5000.ipynb		ZScore-Univariate feature selection-5000.ipynb
ZScore-Univariate feature selection-7043.ipynb		ZScore-Univariate feature selection-7043.ipynb
ZScore-Univariate feature selection.ipynb		ZScore-Univariate feature selection.ipynb
churn-5000.csv		churn-5000.csv
churn-data-3333.csv		churn-data-3333.csv
data set 1 Telecom_customer churn (100000).rar		data set 1 Telecom_customer churn (100000).rar
feature_selection_test_bed.ipynb		feature_selection_test_bed.ipynb
new-DISCRITIZATION-Univariate feature selection.ipynb		new-DISCRITIZATION-Univariate feature selection.ipynb

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Churn-prediction

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