Cognifyz_Internship_Tasks

The project provides Four Tasks which is given by Cognifyz Technology.

Task 1 : Build a machine learning model to predict the aggregate rating of a restaurant based on other features.

Steps:

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

Task 2 : Create a restaurant recommendation system based on user preferences.

Steps:

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

Task 3 : Develop a machine learning model to classify restaurants based on their cuisines.

Steps:

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

Task 4 : Perform a geographical analysis of the restaurants in the dataset.

Steps:

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