This is a Random forest classification model for a most common dataset, Telecom Churn prediction.In this repository, I have performed the end to end Exploratory Data Analysis, and idenfitied the characteristics of the customers that are more likely to churn, and I have used them wisely to create a model, and lately, have deployed the model.
In a Telecom industry, customers can choose from multiple service providers and actively switch from one operator to another. In this highly competitive market, the telecommunications industry experiences an average of 15-25% annual churn rate. Given the fact that it costs 5-10 times more to acquire a new customer than to retain an existing one, customer retention has now become even more important than customer acquisition. For many incumbent operators, retaining high profitable customers is the number one business goal.
To reduce customer churn, telecom companies need to predict which customers are at high risk of churn.
app = Flask("__name__")
The loadPage method calls our home.html.
@app.route("/")
def loadPage():
return render_template('home.html', query="")
The predict method is our POST method, which is basically called when we pass all the inputs from our front end and click SUBMIT.
@app.route("/", methods=['POST'])
def predict():
The run() method of Flask class runs the application on the local development server.
app.run()
The above given Python script is executed from Python shell.
Go to Anaconda Prompt, and run the below query.
python app.py
Below message in Python shell is seen, which indicates that our App is now hosted at http://127.0.0.1:5000/ or localhost:5000
* Running on http://127.0.0.1:5000/ (Press CTRL+C to quit)
HERE'S HOW OUR FRONTEND LOOKS LIKE:
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent