Michael Lukianoff’s Post

🔍 Excited to share my latest project with Cognifyz Technologies, where data meets intelligence! 🔍 I've had the opportunity to dive deep into restaurant data, tackling various analysis tasks across multiple levels: ✅ Top Cuisines: Identified the most common cuisines and calculated their percentages. 🏙 City Analysis: Found the city with the most restaurants and the highest average ratings. 💸 Price Range Distribution: Visualized and analyzed the distribution of restaurant price ranges. 📦 Online Delivery: Compared the ratings of restaurants with and without online delivery services. And that's just Level 1! Moving onto more complex tasks like geographic analysis, cuisine combinations, and even text review sentiment analysis. A huge thank you to Cognifyz Technologies for this incredible learning experience! 🚀 #DataAnalysis #Cognifyz #DataScience #RestaurantData #Analytics #MachineLearning #CareerGrowth

Predict Restaurant Ratings In this project, our goal is to build a machine learning model that can predict the aggregate rating of a restaurant based on various features. We will preprocess the dataset by handling missing values, encoding categorical variables, and splitting the data into training and testing sets. Next, we will select a regression algorithm, such as linear regression or decision tree regression, and train it on the training data. To evaluate the model's performance, we will use appropriate regression metrics like mean squared error and R-squared on the testing data. By analyzing the model's results, we can identify the most influential features affecting restaurant ratings. This project will provide valuable insights for restaurant owners and reviewers, helping them understand the factors that contribute to a restaurant's rating. Repository Link:-https://lnkd.in/gb_-SEGx #MachineLearning #PredictiveModeling #RestaurantRatings #DataAnalysis #RegressionAnalysis #CognifyzTechnologies #Cognifyz #CognifyzTech

#thoughtoftheday I came across this image over the weekend that resonated with me, especially in the #foodtech world. For years, restaurant operators have gathered data without fully unlocking its potential. Now, they're delving into the depth of these insights, transforming them into more than mere figures. They're weaving strategies, predictions, and crafting narratives that could define their future. This shift from passive data collection to active storytelling is not just change—it's evolution. #fooddeliverysales #fooddeliverytech #saasfood #datascience #fooddelivery #dataworld #datanarrative

Join this session at RLC. Discover how Fourth, the leading restaurant software company, is revolutionizing prime cost management through artificial intelligence. In a panel discussion led by Fourth's CEO, Clinton Anderson, industry leaders from top restaurant brands such as TGI Fridays, Buffalo Wild Wings, Arby's, and Pizza Hut will share their experiences and insights on leveraging AI to tame prime costs.  Click here to add the session to your calendar. https://lnkd.in/eQi-xkgK

Day 6 INSIGHTS AND RECOMMENDATIONS To give highlights from our project objectives from day 1, here's my report: 💎 The pizza restaurant had it's peaked of sales at 6.00pm, 12.00pm and 1.00pm respectively. I recommend that the restaurant can take measures of preparing resources needed on time to avoid shortage during the list times 💎 The category of pizza that sold the most is the classic pizza and large size pizza's recorded the highest number of sales. I'd recommend that the restaurant invest more in producing pizza of this size and can also use other sizes as incentive to the customers. An instance, buy two large pizza get one small free. 💎 Day 2 recorded the highest sales followed by day 1, day 3 and day 4. It is important to note that the company is looking through the events that occured that day can find out if it was a festive or discounted period and ensured to prepare for days like that for future references. 💎 The total sales made is 1805.85. this some reflect the collective impact of various pizza sales. 🥁 Drum rows🥁🎶 I'm excited to let you know this is how this particular story ends and No, I'm not sad because I'm about to jump right into another. I hope I was able to give an entertaining storyline 😌☺️✨ I remain your favourite data analytics: Osi Susan Onto the next guys #6daysdataanalyticschallengewithharvoxx #data #dataanalytics #dashboard #datascience #artificialintelligence #ai #msexcel #excel #dsp100c2 #harvoxxtechhub #10daysdataanalyticschallenge

🍽️📊 Excited to share my latest project! 🎉 I've built a predictive model for restaurant revenue 🏆. Using factors like location 🗺️, cuisine type 🍕, and customer reviews 🌟, I've crafted insights to help restaurant owners plan strategically. 📈 Whether you're in food service or data science 🍔🧑💼, this project demonstrates the power of analytics in boosting business success. 🚀 Let's innovate and thrive together! #RestaurantAnalytics #DataScience #BusinessStrategy

🚀 Excited to showcase our journey through Level 1 tasks at Cognifyz Technologies, where data meets intelligence! 📊 🌟 Task 1: Top Cuisines Objective: Identify the top three most common cuisines in our dataset and calculate the percentage of restaurants that serve each. Key Insights: Top 3 cuisines discovered. Percentage breakdown of each cuisine among the total restaurants. 🌟 Task 2: City Analysis Objective: Identify the city with the highest number of restaurants, calculate the average rating for restaurants in each city, and determine the city with the highest average rating. Key Insights: City with the most restaurants. Average rating per city. City with the highest average rating. 🌟 Task 3: Price Range Distribution Objective: Visualize the distribution of price ranges among restaurants and calculate the percentage of restaurants in each price range category. Key Insights: Histogram/bar chart of price range distribution. Percentage of restaurants in each price range. 🌟 Task 4: Online Delivery Objective: Determine the percentage of restaurants that offer online delivery and compare the average ratings of restaurants with and without online delivery. Key Insights: Percentage of restaurants offering online delivery. Comparative analysis of ratings for restaurants with and without online delivery. With these tasks, we're unlocking valuable insights from our dataset, driving smarter decisions and better outcomes. Stay tuned for more updates from Cognifyz! #DataScience #Cognifyz #DataAnalytics #BusinessIntelligence #RestaurantData #DataVisualization #OnlineDelivery 

🎙️ Tomorrow on Episode 4: Jim and Derek Dive into the "Fear of Data"! 📊 🤔 Ever wondered why some restaurateurs choose gut feelings over data-driven decisions? Jim and Derek unravel the mystery behind the "Fear of Data" in the restaurant industry. Explore the possible reasons, share eye-opening insights, and discuss how embracing data and analytics can transform your business.

In the realm of data analysis, data quality shines as the gold standard. Just as a chef artfully enhances flavors to present a dish in its best light, there's an art to refining data without compromising its integrity. Thoughtful 'data cooking', when done ethically, can help in emphasizing key narratives while maintaining the core truth. It's a delicate balance of presenting data compellingly and authentically. By embracing this approach, we ensure our insights resonate, fostering trust and paving the way for informed, impactful decisions. #AIGenerated #DataQuality #DataDrivenDecisions #DataCooking #DataManipulation #DALL_E

Hello everyone 😁 I will be embarking on a 6 days challenge on a data analysis project titled “Exploratory Data analysis and visualization on a pizza Restaurant sales. The dataset contains 500 rows, which was gotten in 4days(2015). This pizza sales dataset makes up of 12 relevant variables: 1. Order-id: Unique identifier for each order placed by a table 2. Order details-id: Unique identifier for each pizza placed within each order (pizzas of the same type and size are kept in the same row, and the quantity increases) 3. Pizza-id: Unique key identifier that ties the pizza ordered to its details, like size and price 4. Quantity: Quantity ordered for each pizza of the same type and size 5. Order-date: Date the order was placed (entered into the system prior to cooking & serving) 6. Order-time: Time the order was placed (entered into the system prior to cooking & serving) 7. Unit-price: Price of the pizza in USD 8. Total-price: unit-price * quantity 9. Pizza-size: Size of the pizza (Small, Medium, Large, X Large, or XX Large) 10. Pizza-type: Unique key identifier that ties the pizza ordered to its details, like size and price 11. Pizza-ingredients: ingredients used in the pizza as shown in the menu (they all include Mozzarella Cheese, even -+-if not specified; and they all include Tomato Sauce, unless another sauce is specified) 12. Pizza-name: Name of the pizza as shown in the menu THE MAIN OBJECTIVE OF THIS PROJECT IS: • To determine the exploratory data analysis and the visualization of a pizza restaurant sales dataset. The specific objectives are: • To determine the overall sales trend over time. • To determine the pizza category that generates the highest and lowest revenue for the restaurant. • The determine the peak period for sales in the restaurant. • To determine the particular pizza that is frequently ordered daily. #6daysdatanalyticschallengewithharvoxx #dataanalytics #dashboard #datascience #artificialintelligence #ai #msexcel #excel #harvoxxtechhub #10daysdataanalyticschallenge #6daysdataanalyticschallenge