Classification-Model-Project

Goal

• The purpose of this model is to predict customer churn at Telco.
• My goal is to find specific features that drive customer churn at Telco.

Initial hypotheses

• Null Hypothesis: contract type and monthly charges features do not drive customers to churn
• Alternative Hypothesis: contract type and monthly charges features do drive customers to churn

Data dictionary

contract_type: The type of contract the customer has agreed to

paperless_billing: Whether the customer utilizes paperless billing or not

senior_citizen: Whether or not customer is a senior citizen

partner: Whether or not the customer has a partner

dependents: Whether or not the customer has dependents

monthly_charges: How much the customers pays for service monthly

multiple_lines: Whether or not the customer has multiple lines

online_security: Whether or not the customer has online security

online_backup: Whether or not the customer has online backup

device_protection: Whether or not the customer has device protection

tech_support: Whether or not the customer has tech support

streaming_tv: Whether or not the customer streams tv

streaming_movies: Whether or not the customer streams movies or not

internet_service_type: Type of internet service the customer has

payment_type: Type of payment the customer uses

Planning:

• Generate questions to ask about the data set based off of what I want my model to predict. Do any features have an impact on churn?. What features significantly drive customer churn. It should predict customer churn.
• Determine the format. Final report should be in .ipynb, Modules should be in .py, Predictions should be in .csv.
• Determine audience and develop speech and presention accordingly. Audience will be lead data scientist.
• Determine correlation between features and customer churn
• Develop my null hypothsisis and alternative hypothesis.
• Determine what model to create

Acquisition:

• Using a function with my credentials stored in a env.py file, I connected to codeup's MySQL server.
• Created a query pertinent to the project
• Using a deliverable I created with the query, I called a function I built to acquire and read telco data from codeup's MySQL server.

Preparation:

• perform tasks such as handling null values, outliers, normalizing text, binning of data, changing data types, etc.
• I dropped columns, changed data types.

Exploration & pre-processing:

• Made visuals and used stats to understand which features had a significant correalation, relationship
• I dropped features that had no significance

Modeling:

• I choose a classification random forest model
• I used my train data set to train my model
• I made predictions with my model
• I made multple models and choose the best one

Delivery:

• Deployed my model and a reproducable report
• Made recommendations

Key findings, recommendations, and takeaways

• Monthly contracts amd charges are key features driving customer churn
• I recommend incentivizing yearly contracts by offering promotion deals and a loyalty program

Instructions or an explanation of how someone else can reproduce project and findings

Enviroment setup:

• Install Conda, Python, MySql, VS Code or Jupyter Notebook
• Clone this repo