Aim of the Project : To predict, analyze and provide the counter measures to prevent the customers’ from Churning. Build a Model to predict the Customers’ likely to be churned using Data Science, and Machine Learning Applications. Business Understanding : The data used to build a Customer Churn Prediction Model, consists of data related Telecom domain, and their behavioral trends w.r.t. Churning across 4 years of time span. Life Cycle of the Project: Extracted the data from Kaggle open source.

Aim of the Project Predict if the patients have Breast Cancer or not. And also classify that it is Malignant / Benign using UCI dataset Life Cycle of the Project Collected the dataset from Kaggle open source. Performed Data Cleaning using Pandas and Seaborn. Used Pandas, matplotlib & Numpy for Data Pre-processing, to decrease the redundancy, by taking care of the missing values, and duplicates. Used Label Encoder to handle the imbalanced dataset, in order to avoid the OVERFITTING.