Project Objective : Predict Customer Churn:

• Problem: Customer churn is a major problem for businesses of all sizes. When customers churn, they take their business with them, which can lead to lost revenue and profits. Businesses can use machine learning to predict which customers are likely to churn, so that they can take steps to prevent them from doing so.
• Dataset: I will use Telco Customer Churn dataset from Kaggle for this dataset contains information on over 7000 customers, including their demographics, account information, and service usage.
• Tasks: The tasks involved in this project will be:
  • Data cleaning and preparation: The data must be cleaned and prepared before it can be used for machine learning. This includes removing missing values, imputing missing values, and transforming categorical variables.
  • Exploratory data analysis: Exploratory data analysis (EDA) will be used to gain insights into the data, this will include looking at the distribution of the data, identifying correlations between variables, and creating visualizations.
  • Feature engineering: Feature engineering is the process of creating new features from existing features. This will probably help improve the performance of the machine learning models.
  • Model selection: Several machine learning models can be used to predict customer churn. The best model for a particular dataset will depend on the specific features and the desired performance metrics.
  • Model evaluation: The performance of the machine learning model will be evaluated using a holdout dataset for this will help to determine how well the model will generalize to new data.
• Deliverables: The deliverables for this project are:
  • A report that will describe the steps involved in the project and the results of the analysis.
  • A presentation that will summarize the findings of the project.
  • A machine learning model that can be used to predict customer churn.