Restaurant Rating Prediction, Recommendation and Analysis

This repo contains the files I worked on during my Machine Learning internship at Cognifyz technologies. The internship was undertaken in Jan-Feb 2024. They provided me a large dataset of restaurants and my main role for me was to build a model to predict the ratings of restaurants and gather meaningful insights from the data. My regression model achieved an accuracy of 98% and further details are listed below.

Task 1

Objective: Build a machine learning model to predict the aggregate rating of a restaurant based on other features.

Steps:

• Preprocess the dataset by handling missing values, encoding categorical variables, and splitting the data into training and testing sets.
• Select a regression algorithm (e.g., linear regression, decision tree regression) and train it on the training data.
• Evaluate the model's performance using appropriate regression metrics (e.g., mean squared error, R-squared) on the testing data.
• Interpret the model's results and analyze the most influential features affecting restaurant ratings.

Task 2

Objective: Create a restaurant recommendation system based on user preferences.

Steps:

• Preprocess the dataset by handling missing values and encoding categorical variables.
• Determine the criteria for restaurant recommendations (e.g., cuisine preference, price range).
• Implement a content-based filtering approach where users are recommended restaurants similar to their preferred criteria.
• Test the recommendation system by providing sample user preferences and evaluating the quality of recommendations.

Task 3

Objective: Develop a machine learning model to classify restaurants based on their cuisines.

Steps:

• Preprocess the dataset by handling missing values and encoding categorical variables.
• Split the data into training and testing sets.
• Select a classification algorithm (e.g., logistic regression, random forest) and train it on the training data.
• Evaluate the model's performance using appropriate classification metrics (e.g., accuracy, precision, recall) on the testing data.
• Analyze the model's performance across different cuisines and identify any challenges or biases.

Task 4

Objective: Perform a geographical analysis of the restaurants in the dataset.

Steps:

• Explore the latitude and longitude coordinates of the restaurants and visualize their distribution on a map.
• Group the restaurants by city or locality and analyze the concentration of restaurants in different areas.
• Calculate statistics such as the average ratings, cuisines, or price ranges by city or locality.
• Identify any interesting insights or patterns related to the locations of the restaurants.

Dataset

CSV file is uploaded here in the repo

Platform

Google Colab

Libraries

pandas, numpy, matplotlib, seaborn, scikitlearn

Data Preprocessing and splitting

• Cuisines had 9 null values. So dropped
• Removed features that will inhibit model performance
• The target variables were balanced
• Split training data and test data in the ratio 8:2

Model Evaluation

• My restaurant rating prediction model obtained a R2 score of 0.98.
• Random Forest performs better on the model than logistic regression.

Insights from EDA

• Expensive restaurants tend to have higher ratings.
• Visualized the geospatial distribution of restaurants on the map using their co-ordinates.
• New Delhi has the highest number of restaurants.
• 'North Indian' is the most popular cuisine overall.