When talking about any companies growth within market customers play an essential role in it , having the correct insights about customer behaviour and their requirements is the current need in this customer driven market . Preserving the interests of customers by providing new services & products helps in maintaining business relations . Customer churn is great problem faced by companies nowadays due to lagging in understanding their behaviour & finding solutions for it . In this project we have found causes of the churn for a telecom industry by taking into consideration their past records & then recommending them new services to retain the customers & also avoid churns in future . We used pie charts to check churning percentage later analysed whether there are ant outliers [using box plot] then dropped some features which were of less importance then converted all categorical data into numerical by using [Label Encoding for multiple category data & map function for two category data] plotted the ROC curve to get to know about true positive & false negative rate getting line at 0.8 then spitted the data using train test split .We used algorithms decision tree , Random Forest for feature selection wherein we got feature importance , then used logistic regression & found feature with highest weight assigned leading to cause of churn . Now in order to retain customers we can recommend them new services.