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Data_science_R
Analytics_pandas_part_2
t-SNE
Web_Development
Machine-Learning-Projects
Indexing_knowledge
Object selection has had a number of user-requested additions in order to support more explicit location based indexing.Ecommerce-website
The website displays products. Users can add and remove products to/from their cart while also specifying the quantity of each item. They can then enter their address and choose Stripe to handle the payment processing.Data-Science-Assignments_6093091572242797
Association-Rules
Decision-Tree_C5.0_CART
practice2
Hypothesis_Example_4
Project_09_Web_scraping_Practice
Logistic_Regression
Movies_Analysis
The model is build on "movies" dataset by using explanatory analysis process with pickle file at the end.Homework_2
Hypothesis_Example_3
Working_with_APIs2
Next file added hereHypothesis_Example_2
DBSCAN
logistic.py
Customer_Personality_Analysis
Email_Template
To get the nearest email template required by the user based on the key words specified by the user. Tools : Python, json, Streamlit, NLP libraries, web scrabbing methods.read_csv
This is just for knowing about what does pandas library do and how to read different files with help of pandas.Virtualqaptive
Car_Price_Prediction
ds_R
anime-Recommendation-system
Problem_statement.text
Poll_Application
The Poll Application is a web-based platform built using the Django web framework that allows users to create, vote, and view the results of polls. The application supports multiple polls creation by different users, with various types of questions, including multiple choice, single choice, and open-ended questions.BankNote_Authentication
This project is entirely based on finding out the bank note authentication. Here libraries are used for cleaning the data, Understanding the data, visualization of the data, and final building different types of model to find out best of it. The ML, Clustering Techniques, Heatmap etc are used for the better visualization.Ensemble_Techniques
Hotel_Booking_Prediction
To find the best classification model for predicting bookings cancellations and finding the best explaining variables for customer cancellations.Love Open Source and this site? Check out how you can help us