Bike Rental Prediction (Data Science [Data Mining])

Last Updated on May 3, 2021

About

This Mini-Project was done for Data Mining subject in college (5th sem).

In this project we gather the data of bike rentals by days and hours in csv file format.

Then we predict the rental of bikes based on different factors i.e Weather , Humidity and Situation of a particular day.

I had taken courses on Data Science, and learning from many online courses on Data Science, Coursera Project Network etc..

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Cert-It!

Last Updated on May 3, 2021

About

Cert It! is a web based and android based app that aims to provide and generate certificates over a range of many templates that can be chosen by the user. The user can enter his or her details through a .csv or .xlsx file (containing data in a predefined format having multiple users) or they can list out their own requirements to generate a single certificate.


Problem Statement

There are numerous companies and organizations out there that are providing certificates to their participants / winners. Sometimes , even educational organizations have to provide a load of generated certificates to their people. This process gets pretty hectic since its a very repetitive task.


Solution

Cert It! aims to solve this problem. We are providing an all round elucidation into this issue by providing an idea that automates these tasks & at the same time keep it user friendly. Through this application we want to provide our users with

  • Sample templates of our own on which they can choose and select the best possible fit for their organization and participants.
  • Allow the user to upload their own template and generate certificates.
  • Allow the user to upload a snapshot of the handwritten data in a specified format through which our app will recognize the necessary details and map it out to generate a certificate.


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Predicting Credit Card Approvals

Last Updated on May 3, 2021

About

Commercial banks receive a lot of applications for credit cards. Many of them get rejected for many reasons, like high loan balances, low income levels, or too many inquiries on an individual's credit report, for example. Manually analyzing these applications is mundane, error-prone, and time-consuming (and time is money!). Luckily, this task can be automated with the power of machine learning and pretty much every commercial bank does so nowadays. In this notebook, we will build an automatic credit card approval predictor using machine learning techniques, just like the real banks do! I have used the Credit Card Approval dataset from the UCI Machine Learning Repository.


The structure of this notebook is as follows: First, we will start off by loading and viewing the dataset. We will see that the dataset has a mixture of both numerical and non-numerical features, that it contains values from different ranges, plus that it contains a number of missing entries. We will have to preprocess the dataset to ensure the machine learning model we choose can make good predictions. After our data is in good shape, we will do some exploratory data analysis to build our intuitions. Finally, we will build a machine learning model that can predict if an individual's application for a credit card will be accepted.

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Churn Prediction

Last Updated on May 3, 2021

About

Predicting Customer Churn at a Fictitious Wireless Telecom Company


Churn Management has gotten great attention among the telecommunication Industry because it is proved that instead of going for advertisements to find new customers it’s better to find a technique, solution, and all the available resources in our service to figure out a pattern to make customers stay in the company. Every telecommunication company has huge competition and due to easy access of the plans and services provided by all the companies, a customer can switch the company anytime. For churn Prediction, it is most required to identify the customer who has the highest probability of leaving the service of the company and it will be effective if it’s done at the right time. Through this company can make a decision on what service to provide to make the customer not leave the service.


Here, we can reformulate the given problem as a Classification problem. My goal is to build a Classification model that can predict if Customers will stay with the company or not from the given features. To achieve this, first, I did data analysis and data cleaning, data preparation for training, and then model building. After this, based on the performance I find the best parameters of our model through GridSearchCV which best suits for the given data and gave the expected result.

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Hacktube

Last Updated on May 3, 2021

About

A Chrome extension that fights online harassment by filtering out comments with strong language.

Inspiration

YouTube is a place for millions of people to share their voices and engage with their communities. Unfortunately, the YouTube comments section is notorious for enabling anonymous users to post hateful and derogatory messages with the click of a button. These messages are purely meant to cause anger and depression without ever providing any constructive criticism. For YouTubers, this means seeing the degrading and mentally-harmful comments on their content, and for the YouTube community, this means reading negative and offensive comments on their favorite videos. As young adults who consume this online content, we feel as though it is necessary to have a tool that combats these comments to make YouTube a safer place.

What it does

HackTube automatically analyzes every YouTube video you watch, targeting comments which are degrading and offensive. It is constantly checking the page for hateful comments, so if the user loads more comments, the extension will pick those up. It then blocks comments which it deems damaging to the user, listing the total number of blocked comments at the top of the page. This process is all based on user preference, since the user chooses which types of comments (sexist, racist, homophobic, etc) they do not want to see. It is important to note that the user can disable the effects of the extension at any time. HackTube is not meant to censor constructive criticism; rather, it combats comments which are purely malicious in intent.

How we built it

HackTube uses JavaScript to parse through every YouTube comment almost instantly, comparing its content to large arrays that we made which are full of words that are commonly used in hate speech. We chose our lists of words carefully to ensure that the extension would focus on injurious comments rather than helpful criticism. We used standard HTML and CSS to style the popup for the extension and the format of the censored comments.

Challenges we ran into

We are trying to use cookies to create settings for the user which would be remembered even after the user closes the browser. That way anyone who uses HackTube will be able to choose exactly which types of comments they don't want to see and then have those preferences remembered by the extension. Unfortunately, Chrome blocks the use of cookies unless you use a special API, and we didn't have enough time to complete our implementation of that API at this hackathon.

Accomplishments that we're proud of

We are proud of making a functional product that can not only fight online harassment and cyberbullying but also appeal to a wide variety of people.

What we learned

We learned how to dynamically alter the source code of a webpage through a Chrome extension. We also learned just how many YouTube comments are full of hate and malicious intent.

What's next for HackTube

Right now, for demo purposes, HackTube merely changes the hateful comments into a red warning statement. In the future, HackTube will have an option to fully take out the malicious comment, so users’ YouTube comments feed will be free of any trace of hateful comments. Users won’t have to worry about how many comments were flagged and what they contained. Additionally, we will have a way for users to input their own words that offend them and take the comments that contain those words out of the section.

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Atm

Last Updated on May 3, 2021

About

Me and my friends have done this project with the help of mentor assigned to us.The project is about the performance of ATM machine developed by Python.


For this project we imported sqlite3 and tkinter as tk. We used Tkinter for GUI applications.Tkinter provides a powerful object-oriented interface to the Tk GUI toolkit. We have created user defined functions such as creating_db, insert_money, insert_atm, check_100, check_200, check_500, check_2000,wd_money, update_bal and main_page. When we run the code the GUI application is created.In this application we can see a note as 'Welcome to ATM' and in the next lines we can see 100/-,200/-,500/- &2000/- notes. If we want to insert money we can click on the option called insert money, if we want to withdraw money we can click on withdraw at the same time if we want to check the availability of respective notes we can click on Check Availability beside the notes. After checking for the availability of notes the result will be displayed on the Python shell. This Python shell is also known as REPL (Read, Evaluate, Print, Loop), where it reads the command, evaluates the command, prints the result, and loop it back to read the command again.For every insert or withdraw update will be done. By using all this we can perform the operation that is required. All this transaction details will be stored in SQLite.


I hope this would be helpful for the public.

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