# **ABSTRACT** Main Objective: The main agenda of this project is: Perform extensive Exploratory Data Analysis(EDA) on the Zomato Dataset. Build an appropriate Machine Learning Model that will help various Zomato Restaurants to predict their respective Ratings based on certain features DEPLOY the Machine learning model via Flask that can be used to make live predictions of restaurants ratings A step by step guide is attached to this documnet as well as a video explanation of each concpet. Zomato is one of the best online food delivery apps which gives the users the ratings and the reviews on restaurants all over india.These ratings and the Reviews are considered as one of the most important deciding factors which determine how good a restaurant is. We will therefore use the real time Data set with variuos features a user would look into regarding a restaurant. We will be considering Banglore City in this analysis. Content The basic idea of analyzing the Zomato dataset is to get a fair idea about the factors affecting the establishment of different types of restaurant at different places in Bengaluru, aggregate rating of each restaurant, Bengaluru being one such city has more than 12,000 restaurants with restaurants serving dishes from all over the world. With each day new restaurants opening the industry has’nt been saturated yet and the demand is increasing day by day. Inspite of increasing demand it however has become difficult for new restaurants to compete with established restaurants. Most of them serving the same food. Bengaluru being an IT capital of India. Most of the people here are dependent mainly on the restaurant food as they don’t have time to cook for themselves. With such an overwhelming demand of restaurants it has therefore become important to study the demography of a location. What kind of a food is more popular in a locality. Do the entire locality loves vegetarian food. If yes then is that locality populated by a particular sect of people for eg. Jain, Marwaris, Gujaratis who are mostly vegetarian. These kind of analysis can be done using the data, by studying the factors such as • Location of the restaurant • Approx Price of food • Theme based restaurant or not • Which locality of that city serves that cuisines with maximum number of restaurants • The needs of people who are striving to get the best cuisine of the neighborhood • Is a particular neighborhood famous for its own kind of food. “Just so that you have a good meal the next time you step out” The data is accurate to that available on the zomato website until 15 March 2019. The data was scraped from Zomato in two phase. After going through the structure of the website I found that for each neighborhood there are 6-7 category of restaurants viz. Buffet, Cafes, Delivery, Desserts, Dine-out, Drinks & nightlife, Pubs and bars. Phase I, In Phase I of extraction only the URL, name and address of the restaurant were extracted which were visible on the front page. The URl's for each of the restaurants on the zomato were recorded in the csv file so that later the data can be extracted individually for each restaurant. This made the extraction process easier and reduced the extra load on my machine. The data for each neighborhood and each category can be found here Phase II, In Phase II the recorded data for each restaurant and each category was read and data for each restaurant was scraped individually. 15 variables were scraped in this phase. For each of the neighborhood and for each category their onlineorder, booktable, rate, votes, phone, location, resttype, dishliked, cuisines, approxcost(for two people), reviewslist, menu_item was extracted. See section 5 for more details about the variables. Acknowledgements The data scraped was entirely for educational purposes only. Note that I don’t claim any copyright for the data. All copyrights for the data is owned by Zomato Media Pvt. Ltd.. Source: Kaggle

Makine öğrenmesi uygulamalarının uçtan uca nasıl geliştirildiğini ele aldığımız bitirme tezimizin açık kaynak kodlarıdır.

This project uses machine learning to predict the prices of used phones based on various features like brand, operating system, and new price. The model is built using Python and Flask, and deployed as a web app on Azure.

A set of different models, that can be used to predict price range of a mobile phone.

Machine learning models are used to predict the prices of mobile phones based on their specifications, due to the increasing demand for smartphones and the vast number of models available. Logistic regression, decision trees, random forest, and XGBoosting algorithms are commonly used to make these predictions.

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Find out some relation between features of a mobile phone(eg:- RAM, Internal Memory etc) and its selling price. In this problem you do not have to predict the actual price but a price range indicating how high the price is.

Applied ML pipeline using various classifiers and made prediction in Python

This repository contains the files related to our project "Smart Mobile Phone Price Prediction Using Machine Learning".

Mobile phone purchase prediction project. Classification model based on phone features to recommend the best price range/category for customers.

Mobile Price Range Prediction: Use sales data to build a classification model for mobile phone price ranges. Features include battery power, camera, memory, and connectivity. Split data, apply logistic regression, KNN, SVM (linear and rbf), and evaluate using confusion matrices. Select the most accurate model.

This is a prediction project using a variety of regression models: linear regression, random forest regression, ridge, regression, SVR regression, light gbm regression.

Price prediction of used phones using R and Python

This repository uses machine learning to predict smartphone prices based on features like camera, processor, and battery. Models such as Random Forest and LightGBM achieved over 88% accuracy. Future work includes improving feature engineering and exploring neural networks.

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A dataset of mobile phone information such as brand, model, specs, and historical prices is used to train a machine learning model for mobile price prediction. The model predicts future prices based on patterns found in the data. A large and diverse dataset is important for accurate predictions.

The Mobile Price Range Prediction app uses a Random Forest model to estimate a phone's price category. Users input specs like battery power, camera quality, and memory. The app then predicts the price range, making it easy for consumers and retailers to assess mobile phones based on their features.

📱 Predict mobile phone price ranges using specifications with a Support Vector Machine (SVM) model. Includes an interactive Streamlit web app for single & batch predictions, model evaluation, and visualization.

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Real-world Phone Price Prediction project using messy industry data. Focused on data understanding, feature engineering, XGBoost & CatBoost, and Flask deployment.

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Estimates the price of a phone based on the phone information users enter on the web.

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Building a regression model to predict the used price of phones and tablets.

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A simple mobile price prediction classifier

Now you can predict the price of the next iPhone—though it might cost you an arm and a leg—all with just 6 lines of Python code! I created this prediction tool using PyCharm, a popular Python code editor.

The project aims to help Bob's mobile company predict phone price ranges using machine learning. Tasks include data analysis, model building, evaluation, and business impact analysis. Insights will guide pricing strategy, optimize resources, and enhance competitiveness using data-driven decisions.