207 skills found · Page 1 of 7
MelihGulum / Comprehensive Data Science AI Project PortfolioA curated collection of AI, data engineering, and DevOps projects featuring real-world applications, advanced techniques, and tutorials—ideal for learners and practitioners exploring data science and machine learning.
drshahizan / Python EDAThis topic explains about the implementation of exploratory data analysis (EDA). A total of 21 EDA case studies have been implemented using the Malaysian dataset.
business-science / CorrelationfunnelSpeed Up Exploratory Data Analysis (EDA)
vishnukanduri / Credit Risk Modeling In PythonModeled the credit risk associated with consumer loans. Performed exploratory data analysis (EDA), preprocessing of continuous and discrete variables using various techniques depending on the feature. Checked for missing values and cleaned the data. Built the probability of default model using Logistic Regression. Visualized all the results. Computed Weight of Evidence and price elasticities.
hsbc / TslumenA library for Time Series EDA (exploratory data analysis)
Asifdotexe / DORADORA (Data Oriented Report Automator) automates Exploratory Data Analysis (EDA) to help you effortlessly explore datasets. Generate insightful statistics, visualizations, and reports with just a click! Streamline your data exploration workflow and uncover trends, patterns, and relationships in your data with ease.
datamole-ai / EdvartAn open-source Python library for Data Scientists & Data Analysts designed to simplify the exploratory data analysis process. Using Edvart, you can explore data sets and generate reports with minimal coding.
SouRitra01 / Exploratory Data Analysis EDA In Banking Python Project EDA Project using Python & Pandas Framework
mrankitgupta / Spotify Data Analysis Using PythonAn exploratory data analysis (EDA) and data visualization project using data from Spotify using Python.
saizhang1 / Analytic Projects In Python EDAThere are several exploratory data analysis (EDA) analyzes in this file. More data analytics and business approached than machine learning.
SarangGami / Capstone EDA Project Airbnb Bookings AnalysisExploratory data analysis of Airbnb bookings in New York City to gain insights into the travel industries and Uncovers trends, patterns, user preferences and behavior. Utilizes Python libraries for data exploration, data cleaning, manipulation, and visualization. Provides valuable insights for travelers, hosts, and the Airbnb business.
Elysian01 / CodifyCodify enables data scientists to perform all the tedious and time-consuming tasks such as EDA (exploratory data analysis), data cleaning, data pre-processing, data visualization, modeling, and evaluation in the data-science life cycle, by only conveying the logic of the task in natural language (English) and the system will automatically give out all the relevant python code snippets.
MrBriit / FLASK End To End Zomato Restaurant Price Prediction And Deployment# **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
rohanmistry231 / Exploratory Data Analysis And Feature EngineeringA Python-based repository for mastering exploratory data analysis (EDA) and feature engineering, using libraries like Pandas, Seaborn, and Scikit-learn. Includes practical examples, datasets, and techniques for data visualization, cleaning, and feature optimization.
Madhuarvind / Retail Sales AnalysisA complete exploratory data analysis (EDA) and forecasting project focused on retail sales data. The project identifies key sales patterns, seasonal trends, and builds predictive models to forecast future demand at the item-store level.
roshankoirala / PySpark TutorialImplementation of Spark code in Jupyter notebook. Topics include: RDDs and DataFrame, exploratory data analysis (EDA), handling multiple DataFrames, visualization, Machine Learning
VenkyAdi / EDA ProjectsExploratory Data Analysis (EDA) Projects A collection of EDA projects exploring various datasets to uncover patterns, gain insights, and visualize trends across different industries. Projects include analyses of Amazon Prime content, banking fraud detection, logistics performance, hotel booking trends, and more.
admond1994 / E Commerce Data EDAExploratory Data Analysis (EDA) is performed on the E-Commerce data obtained from a UK-based and registered non-store online retail to discover interesting transactional patterns of different customers and countries.
Jey-krishna / EDA Marathon Using PythonAn exploratory data analysis (EDA) of a comprehensive dataset on ultra-marathon running events.
DheerajKumar97 / Automated ML Application For EDA Streamlit Deployment HerokuThis project is designed as Automated Application for performing Exploratory Data Analysis for given Dataset to generate insights using Python, Streamlit. For executing all the operations customized function has been created and with support of these functions every step will be executed. EDA like basic information about data, Tabulation Analysis, Distribution Analysis, Correlation Analysis and it has been extended to perform Advance Statistical Analysis with some Basic Feature Engineering has been Automated. This Project has been Deployed with Streamlit in Heroku Cloud Platform
