The rise of the food industry over the last decade in India has resulted in a huge number of investors. This has increased the intensity of competition amongst the various companies/start-ups. Food culture analysis has thus gained its importance over the last couple of years. Entrepreneurs and investors are willing to invest enormous amounts of money today. They need to consider a lot of aspects that would help make their business a success.

FoodMood is a project that aims at analysing the food culture of a locality by finding the importance of features that determine the success of a restaurant in that area, and by identifying the correlation between the features etc. Major aspects include:

• Location-wise feature importance (Using Decision Trees)
• Recommendation of the best options for the important features (Using a Combinatorial Approach)
• Prediction of Ideal Pricing Range for a restaurant (Using Logistic Regression and an Artificial Neural Network)
• Sentiment Analysis of Reviews

With our analysis, we aim at helping upcoming entrepreneurs take the right decisions for the best growth of their food chain business.

Zomato Dataset available on Kaggle : https://www.kaggle.com/himanshupoddar/zomato-bangalore-restaurants
The dataset contains data of around 50,000 restaurants in Bangalore. It gives us information on the type of restaurant, cuisines offered, online ordering facilities, reservation facilities, pricing ranges, votes obtained, reviews, rating, etc.

• final_project.py : The entire implementation in Python. Contains code for the Exploratory Data Analysis, Pre-processing and Modelling.
• phase1.py and preprocessingEDA.R contain the code for Pre-processing and Exploratory Data Analysis in Python and R, respectively.