Binita72/Data_science_R

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Created about 4 years ago
Updated about 4 years ago

Binita72/Data_science_R

Binita72

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Analytics_pandas_part_2

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t-SNE

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Web_Development

Machine-Learning-Projects

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Indexing_knowledge

Object selection has had a number of user-requested additions in order to support more explicit location based indexing.

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Patterns_using_codes

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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

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Decision-Tree_C5.0_CART

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practice2

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Hypothesis_Example_4

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Project_09_Web_scraping_Practice

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Logistic_Regression

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Movies_Analysis

The model is build on "movies" dataset by using explanatory analysis process with pickle file at the end.

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Homework_2

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Hypothesis_Example_3

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Working_with_APIs2

Next file added here

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Hypothesis_Example_2

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DBSCAN

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logistic.py

Customer_Personality_Analysis

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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.

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read_csv

This is just for knowing about what does pandas library do and how to read different files with help of pandas.

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Virtualqaptive

Car_Price_Prediction

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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.

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Ensemble_Techniques

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Hotel_Booking_Prediction

To find the best classification model for predicting bookings cancellations and finding the best explaining variables for customer cancellations.

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