#Prediction Task Below

Task 1: Predict Restaurant Ratings Objective: Build a machine learning model to predict theaggregate rating of a restaurant based on other features. Steps: Preprocess the dataset by handling missing values,encoding categorical variables, and splitting the datainto 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 appropriateregression metrics (e.g., mean squared error, R-squared)on the testing data. Interpret the model's results and analyze the mostinfluential features affecting restaurant ratings.

Task 2: Restaurant Recommendation 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: Cuisine Classification Objective: Develop a machine learning model toclassify restaurants based on their cuisines. Steps: Preprocess the dataset by handling missing valuesand encoding categorical variables. Split the data into training and testing sets. Select a classification algorithm (e.g., logisticregression, random forest) and train it on thetraining data. Evaluate the model's performance usingappropriate classification metrics (e.g., accuracy,precision, recall) on the testing data. Analyze the model's performance across differentcuisines and identify any challenges or biases.

Task 4: Location-based Analysis 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.