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Supervised_Learning_Regression_Customer_Churn_Prediction

Context: Customer behavior prediction to retain customers

Each cell description:

Cell 3: lib load

Cell 4: read data from csv file

Cell 5: helper function to find unique value in a feature

Cell 6: Apply label Encoder to convert into numerical value

Cell 7: Exploratory Data Analysis (EDA)

		a. Correlation matrix
		
		b. Correlation matrix visualization
		
		c. Pair plot analysis
		
		d. Histogram analysis
		
		e. Density analysis
		
		f. Scatter matrix analysis
		
		g. Pie chart of Churn

Cell 8: PCA analysis

Cell 9: Best parameter search for 4 models

Cell 10: KNN model

Cell 11: Random forests

Cell 12: Logistic Regression Model

Cell 13: Decision Tree Classifier Model

Cell 14: Single LSTM Model

Cell 15: LSTM Model Evaluation

Cell 16: ROC curves analysis

Cell 17: Precision recall curves analysis

Cell 18: Save Best model(LR)

Cell 19: Saved Model Execution

Dataset : Telco Customer Churn

Dataset Link: https://www.kaggle.com/blastchar/telco-customer-churn

Dataset includes 21 features:

customerID

gender

SeniorCitizen

Partner

Dependents

tenure

PhoneService

MultipleLines

InternetService

OnlineSecurity

OnlineBackup

DeviceProtection

TechSupport

StreamingTV

StreamingMovies

Contract

PaperlessBilling

PaymentMethod

MonthlyCharges

TotalCharges

Churn

We have considered "Churn" as a label