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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