Marketing Strategy - Personalised Offer

Problem Statement

The data is collected through a survey to understand the driver's behavior regarding their preference for discount/offer for dining/takeaway. The researcher collected these data by providing different scenarios to various users.

Example Scenario: You are driving from IIT Madras to Chennai Airport along with your family and you get an offer (10 percent discount on the bill) from the famous Chinese restaurant in Guindy. Will you avail of the offer while traveling?

Along with the user response, some basic information about the users is collected.

Using this data, we must predict whether the user will Accept the offer or Not Accept the offer. Submission files should contain two columns: id and Offer Accepted.

Offer Accepted should be a string Yes or No.

Dataset Description

• Datasets/train.csv - the training set
• Datasets/test.csv - the test set
• Datasets/sample.csv - a sample submission file in the correct format

Columns Description

• offer expiration - Number of days offer is valid
• income_range - income range
• no_visited_Cold drinks -Number of times visited cold drinks
• travelled_more_than_15mins_for_offer -Have you traveled more than 15 mins to avail an offer?
• Restaur_spend_less_than20 - Number of times spend less than 20 dollar in restaurant
• Marital Status - Marital status
• restaurant type - type of restaurant
• age - age
• Prefer western over Chinese - Do you Prefer western over chinese
• travelled_more_than_25mins_for_offer - Have you traveled more than 25 mins to avail an offer?
• travelled_more_than_5mins_for_offer - Have you traveled more than 5 mins to avail an offer?
• no_visited_bars - Number of times visited bar
• gender - gender
• car - type of vehicle do you use in your own words
• restuarant_same_direction_house
• Cooks regularly - Do you cook regularly?
• Customer type - Whom do you prefer to go with?
• Qualification - Qualification
• is foodie - Is foodie
• no_Take-aways - Number of times opted for take-away
• Job/Job Industry - Type of industry you work with
• restuarant_opposite_direction_house - is the coupon offered restaurant located opposite direction to your house?
• has Children -Do you have children?
• visit restaurant with rating (avg) - average rating of the restaurant which gave offer
• temperature -current temperature
• Restaur_spend_greater_than20 - Number of times spend greater than 20 dollar in restuarant
• Travel Time - travel time for the restaurant which gave offer
• Climate - current climate
• drop location - where are you heading to?
• Prefer home food - Do you prefer home food?
• Offer Accepted - Did you accept the offer?

Target Variable: Offer Accepted

Most of the column names are self-explanatory names.

Running the Notebook

1. Clone the repo

   git clone https://github.com/priyanka-maz/Marketing-Strategy-Personalised-Offer

2. Upload the Notebook.ipynb to Google Colab, along with the Datasets.

3. Click on Run All

Exploratory Models

The models used in exploratory training of the data are mentioned within commented code in the notebook and their names are listed below:

• AdaBoost Classifier
• Bagging Classifier
• Gradient Boosting Classifier
• Random Forest Classifier
• Logistic Regression
• Perceptron
• Stochastic Gradient Descent Classifier
• Ridge Classifier
• Naive Bayes Classifier
• K-Nearest Neighbours Classifier
• Multi-Layer Perceptron
• Linear Support Vector Machine
• XGBoost

Final Model & Scores

The final model that was used is the Random Forest Classifier after hyperparameter tuning using GridSearchCV.

Mean F1 score was used as the evaluation metric and the final scores were:

• Datasets/train.csv - 0.64072
• Datasets/test.csv - 0.63342