Data Science Internship Tasks by @COGNIFY Technologies🎉.
Role Data Science Intern❤️💫.
The role I've involved:
Collecting Data, Cleaning Data, Interpreting Data, and development of data-driven solutions.
In this the tasks are divided into 3 Levels and each level has 3 Tasks.
Data Exploration and Preprocessing.
Exploring the dataset and identifying the number of rows and columns. Checking the missing values in each column and handling them accordingly. Performing data type conversion if necessary. Analyzing the distribution of the target variable ("Aggregate rating") and identifying any class imbalances.
Descriptive Analysis.
Calculating basic statistical measures (mean, median, standard deviation, etc.) for numerical columns. Exploring and the distribution of categorical variables like "Country Code", "City", and "Cuisines". Identifying the top cuisines and cities with the highest number of restaurants.
Geospatial Analysis.
Visualizing the locations of restaurants on a map using latitude and longitude information. Analyzing the distribution of restaurants across different cities or countries. Determining if there is any correlation between the restaurant's location and its rating.
Table Booking and Online Delivery.
Determining the percentage of restaurants that offer table booking and online delivery. Comparing the average ratings of restaurants with table booking and those without. Analyzing the availability of online delivery among restaurants with different price ranges.
Price Range Analysis.
Determining the most common price range among all the restaurants. Calculating the average rating for each price range. Identifying the color that represents the highest average rating among different price ranges.
Feature Engineering.
Extracting additional features from the existing columns, such as the length of the restaurant name or address. Creating new features like "Has Table Booking" or "Has Online Delivery" by encoding categorical variables.
Predictive Modeling.
Building a regression model to predict the aggregate rating of a restaurant based on available features. Splitting the dataset into training and testing sets and evaluating the model's performance using appropriate metrics. Experiment with different algorithms (e.g.,linear regression, decision trees, random forest) and comparing their performance.
Customer Preference Analysis.
Analyzing the relationship between the type of cuisine and the restaurant's rating. Identifying the most popular cuisines among customers based on the number of votes. Determining if there are any specific cuisines that tend to receive higher ratings.
Data Visualization.
Creating visualizations to represent the distribution of ratings using different charts (histogram, bar plot, etc.). Comparing the average ratings of different cuisines or cities using appropriate visualizations. Visualizing the relationship between various features and the target variable to gain insights.
You can Reach Me or Find the Each and Every Task Videos here: https://www.linkedin.com/in/saiharsha3377/
Thanks to COGNIFYZ Technologies for providing this opportunity❤️.