This repo contains the files I worked on during my Data Science internship at Protuner technologies. The internship was undertaken in April-May 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.
CSV file is uploaded here in the repo
Google Colab for Task 3 only.
pandas, numpy, matplotlib, seaborn, scikitlearn
- 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
- My restaurant rating prediction model obtained a R2 score of 0.98.
- Random Forest performs better on the model than logistic regression.
- 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.
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.
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.
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.