Category: Python projects

Welcome to the Python Projects Section.

Find the below some projects we have developed using Python.

  1. Google Finance currency calculator API.
  2. using aprori on website to get relevent data
  3. Game like tictactoe,2048,
  4. Note/content sharing platform for college students
  5. book information parsing tool developed for opencart
  6. AI Crooswords
  7. An algorithm for steganography of multiple heterogeneous files on a single video carrier.
  8. college-administration
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Medical Data Analysis Python Project

Abstract:

The idea of visualizing data by applying machine learning and pandas in python. Taking dataset from the medical background of different people ( prime Indians dataset from UCI repository). This data set consists of information of the user whose age, sex type of symptoms related to diabetes. Design a testing and training set and predict what are the chances of patients having diabetes in the coming five years. Data is classified and shown in the form of different graphs.

Project Objective:

To analyze data by considering exiting the user’s data set and predict what are the chances of diabetes in the coming five years. Information is shown in the form of different graphs.

Introduction:

Data analysis is playing an important part in analyzing datasets and predicting what are situations in the coming years. This analysis can give the option for departments and organizations to take steps in dealing with these problems. In this project prediction of diabetes in the coming years is considered as the main problem.

Existing System:

There was no chance of prediction in existing studies it was just by manual analysis based on existing data but analyzing large amounts of datasets is not considered.

Proposed System:

Data analysis and machine learning libraries and algorithms are used for prediction on diabetes and information is shown in detail in the form of different types of graphs (histogram, density plots, box and whisker plots, and correlation matrix plots.

SOFTWARE & HARDWARE REQUIREMENT:

OS: Windows 7 or above
Processor: I3 or above
Programming language: python 3.6
Distribution tool: Anaconda.
RAM: 4 GB
Hard Disk: 160 GB

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Spam Comments Detection Project in Python

Abstract

Spamming is the process of posting unwanted and not related comments on specific posts in any type of social sharing medium or video-sharing medium. These messages are posted by bots for reducing ranking or disturbing users viewing experience which ultimately reduces the rank of website and post. This spamming is done manually also which are mostly seen in most competitive pages.

There are few methods that can remove spamming methods that use data mining techniques but in this project, we are automating the process of spam comment detection using machine learning by taking a dataset of youtube spam messages and applying countvectorizer and navie base algorithm for clustering on the given dataset using python programming.

Proposed system:

This project, countvectorizer is used for extracting features form a given dataset and design model by generating tests and training sets from given data. Then the navie base classifier is applied for clustering and the test and training set is given as input based on this data given message is tested if it is spam or not.

Existing system:

In the existing system, data mining techniques are used for detecting spam messages. Most of these methods work only after posting messages. There is a need for a system that can automate this process before posting message.

SYSTEM REQUIREMENTS:

HARDWARE REQUIREMENTS:

  • Operating system: Windows 7 or 10.
  • Tool :Anaconda ( Jupiter )

SOFTWARE REQUIREMENTS:

  • Software :Python 3.5
  • Dependencies: numpy , OpenCV
  • Libraries: panda, keras, scipy, sklearn
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Online Book Store Python Project Synopsis

Using this Online book store application the Customers can buy the books using the internet by sitting at home. 

Usually, the book reader if they want to buy a book they should visit the book stalls, go through the book contents and they wish to purchase the book they do the payment and gets the book to the home. 

To overcome this manual visit to the book stalls we can use this application to visit the bookstalls virtually and make the payment of the purchase.

In this system, an Administrator is going to add a New Book details like ISBN Code, Book Name, Author, Publisher details into the system based on its Category, etc. 

The Administrator will receive the request from the Customer as Purchase of a book.  The Administrator will receive the payment from Customer for the delivery of the book through Credit Card and reserves the book for the Customer. 

Using the Courier delivery system the Administrator dispatches the book to the Customer.

The Customer at another end will register to the site, login into the system and go through the virtual bookstall.  He/she will select a book based on its Author, Category, etc. 

If the Customer would like to purchase the book, he/she will place an order of the book.  The book will delivery status can be captured by this system.  If the book courier found in a bad position, the Customer can also raise a complaint to the Administrator for the action.

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Food Maza – Food Ordering System Python Project

Using this application the Customers can buy the Food items using the internet by sitting at home.  Usually, the customer if they want to eat a food item they should visit the restaurants, and they wish to purchase the items they do the payment and gets the food to the home. 

To overcome this manual visit to the restaurants we can use this application virtually and make the payment of the purchase.

In this system, an Administrator is going to add a new food item details.  The Administrator will receive the request from the Customer as Purchase of a product. 

The Administrator will receive the payment from Customer for the delivery of the food item through Credit Card and reserves the item for the Customer.  Using the Courier delivery system the Administrator dispatches the food items to the Customer.

The Customer at another end will register to the site, login into the system and go through the virtual restaurant.  He/she will select a food item based on its quality and quantity, etc. 

If the Customer would like to purchase the food item, he/she will place an order of the item.  The product will delivery status can be captured by this system. 

If the product courier found in a bad position, the Customer can also raise a complaint to the Administrator for the action.

Functional Requirements:

In this system, an Administrator is going to add New food details.  The Administrator will receive the request from the Customer as Purchase of products. 

The Administrator will receive the payment from Customer for the delivery of the products through Credit Card and reserves the products for the Customer. 

Using the Courier delivery system the Administrator dispatches the products to the Customer.

The Customer at another end will register to the site, login into the system and go through the virtual shopping mall.  He/she will select a product. 

If the Customer would like to purchase the products, he/she will place an order of the products.  The products will delivery status can be captured by this system. 

If the products courier found in a bad position, the Customer can also raise a complaint to the Administrator for the action.

Modules:

  • Administrator
  • Customers
  • General Users
  • Web Registration
  • Search
  • Authentication

Users:

    1. Administrator
    2. Customer 

ENVIRONMENT:

Servers:

          Operating System Server: Windows

          Database Server: SQLite

          Client: google chrome

          Tools: Pycharm

          Code Behind: Python

 Hardware Specification:

Processor: Intel Pentium or More

RAM: 2 GB

Hard Disk: 80 GB

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Prediction of Breast Cancer Data Science Project in Python

The Prediction of Breast Cancer is a data science project and its dataset includes the measurements from the digitized images of needle aspirate of breast mass tissue.

The data has 100 examples of cancer biopsies with 32 features. From these features, we can predict whether the tumors are benign or malignant.

The steps to be done are:

  • Data Preparation by checking the target variable and removing the ID variable.
  • Visualization of the frequency of the type variable.
  • Normalizing the variables by using min-max normalizations and z-score methods.
  • Bifurcation of the train and test sets.
  • Prediction of the type of tumors using the classification algorithm.
  • Improving the models using different values of k and other methods of normalizations.

Other data Science Projects using python below:

1) Marketing Campaigns Prediction of the clientele subscribing to services in Bank.
2) Market Basket Analysis for the creation of Online Recommender System for Grocery Supermarket.
3) Movie Review Analysis using Natural Language Processing (NLP).
4) Analysis of Most Connected Hubs in Socially Connected People.
5) Analysis of Loan Sanction for particular customers in a Bank.
6) Prediction of Medical Expenses of Incoming Patients in a Particular Hospital.
7) Cluster Analysis of Collection of People impacting a Social Networking Site.
8) Text Mining with Predictive Analysis of Spam Filtration in Incoming Mails.
9) Expectation Maximization Analysis for Digit Classification.
10) Exploration of Murders according to Investigation Team Uniform Crime Reports.

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Art Auction Houses Data Science Project in Python

Art Auction Case Study:

Art auction application provides buying and selling of artworks. The normal way of sale of an artwork is auction places buy artworks from the art owners and sell it to the customers who bid more. The bidder bids a maximum price based on the art’s Naturality, quality, and artist value.
The dataset provided contains details of art auctions from various art houses and the details about the art piece auctioned and the other details about the auction

Task:

We would like you to perform an exploratory analysis on the dataset provided. Please take a shot at analyzing this dataset using any tool of your choice and create a summary of key insights and analysis charts.
The questions to be solved are:
a) How many art pieces were auctioned in zip codes mentioned?
b) Which country has contributed most in the auction?
c) Highlight the states which have sold most of the paintings?
d) How many paintings were made between the year 1947-1950?
e) Which artist has sold the most number of paintings?
f) Which dimensions of the art were used the most?
g) Categorize according to the sales of art in terms of year in which they were made?
h) List out the top 10 buyers.
i) Distribution of Acquisition price of art over the country.
j) How many arts are insured above the average minimum insurance price?
k) How much difference is there for actual cost and buyers cost?
l) How many arts were sold online according to year?
m) Which artist got the highest rating below 5?
n) In which year most of the arts were the good purchase?
o) Design out a predictive model to show whether the purchases were good purchases or not?
You can use your own way of visualization and Modelling.
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Handwritten Character Recognition using Deep Learning Approach

ABSTRACT:

Deep learning is a new area of machine learning research which has been introduced with the objective of moving machine learning closer to one of it’s goal i.e artificial intelligence.

There are various applications of deep learning. In this project we use deep learning for hand-written character  recognition.

The challenges associated with hand-written character recognition are- word and line seperation,segmentation of words into  characters, recognition of words when lexicons are large, and the use of the language models in aiding preprocessing and recognition, character extraction , types of hand writing, number of scriptors, size  of vocabulary and spatial layout.

We would like to attempt them with deep learning. Multi-layer with layer wise abstraction will be used for this purpose.

SOFTWARE REQUIREMENTS:

Python

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Digit Recognition Python Project

Project Domain: Image processing and Machine learning

Project Title: Digit Recognition

DESCRIPTION:

Digit recognition is one of the active research topics in digital image processing. It is a classic machine learning problem. The goal of this project is to take an image of handwritten digits and determine what those digits are. The principal task in digit recognition is to extract HOG features from the database of handwritten digits and to build a classifier on it. Then we predict the digits of the database using this classifier. To build the classifier we write python scripts.

EXISTING SYSTEM:

The current systems use resource intensive image processing methods and algorithms with varying accuracies and rejections.

PROPOSED SYSTEM:

The proposed system uses efficient pattern classification and machine learning approach to improve overall performance of predicting the digits.

FUTURE EXPANSION:

This digit recognition can be further scaled to achieve big data processing levels.

Hardware and software requirements:

Hardware Requirements:

  • RAM : 1 GB and above
  • OS : Linux, Windows

Software Requirements:

  • Python 2.7
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Smart Traffic Management System Project

Traffic management is the problem that most of the countries are taking special steps. Smart traffic management system project is a hardware and software application which will use latest technologies to calculate traffic and provide information to traffic police for taking steps.

Project category:

Traffic Management System

Project Introduction:

This application is designed by using PLC and SCADA technology.  Compare to existing method, in this project density of vehicles at each lane and their weight and then take required steps to control traffic. This system is useful for managing traffic in highways.

Working:

Sensors will detect weight of every vehicle and if vehicle weight is more than 80 sensors will send signals to PLC , PLC will guide vehicles to move to other lane by sending information to barricading.

Similarly if the weight of vehicle is less than 80 sensors will send message to PLC which will help vehicles to pass to respective LANE.

Download Smart Traffic Management System Project Source Code