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Background

  • Operators are losing in today’s competitive market. Churn is a key driver and is an industry wide challenge. A churned customer provides less revenue or zero revenue and increases competitor market share. Increase acquisition cost for the service provide if the customer churned to competition. It costs up to 5 times as much for a Service provider to acquire a new subscriber as to retaining an existing one.

Goal

  • This analysis will asses the classification of subscribers, asses insights on churn behavior of subscribers and using the information to strategize new marketing initiatives.

Dataset info

  • Sample Dataset containing Telco customer data and showing Customers that left last month

Methodology

  1. Install and load Python libraries
  2. Explore the data
  3. Train Test Split
  4. Decision Tree Classifier
  5. Random Forest Classifier
  6. Performing PCA
  7. Pickling the model and Deployment

Tools

-Python, Jupyter notebooks

Install and load Python libraries

import pandas as pd
from sklearn import metrics
from sklearn.model_selection import train_test_split
from sklearn.metrics import recall_score
from sklearn.metrics import classification_report
from sklearn.metrics import confusion_matrix
from sklearn.tree import DecisionTreeClassifier
from imblearn.combine import SMOTEENN
Reading csv and Explore the data
df=pd.read_csv("tel_churn.csv")
df.head()