Crime Data Analysis Project in Machine Learning

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Crime Data Analysis Project in Machine Learning .Crime analyses is one among the important application of knowledge mining. data processing contains many tasks and techniques including Classification, Association, Clustering, Prediction each of them has its own importance and applications It can help the analysts to spot crimes faster and help to form faster decisions.
The main objective of crime analysis is to seek out the meaningful information from great deal of knowledge and disseminates this information to officers and investigators within the field to help in their efforts to apprehend criminals and suppress criminal activity. In this project, Kmeans Clustering is employed for crime data analysis.

Technologies Used

Web Technologies

Html , Css , JavaScript , Bootstrap , Django

Machine Learning Library In Python

Numpy , Pandas , Scipy
matplotlib
scikit-learn
seaborn

Database

SQLite

Supported Operating System :-

  1. We can configure this project on following operating system.
  2. Windows : This project can easily be configured on windows operating system. For running this project on Windows system, you will have to install
  3. Python 3.6.10, PIP, Django.
  4. Linux : We can run this project also on all versions of Linux operating systemMac : We can also easily configured this project on Mac operating system.

Installation Step : -

  1. python 3.6.8
  2. command 1 - python -m pip install --user -r requirements.txt
  3. command 2 - python app.py

Read Before Purchase  :

  1. One Time Free Installation Support.
  2. Terms and Conditions on this page: https://projectworlds/terms
  3. We offer Paid Customization installation Support
  4.  If you have any questions please contact  Support Section
  5. Please note that any digital products presented on the website do not contain malicious code, viruses or advertising. You buy the original files from the developers. We do not sell any products downloaded from other sites.
  6. You can download the product after the purchase by a direct link on this page.

Movie Recommendation System Project Using Collaborative Filtering, Python Django, Machine Learning

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Latest Machine Learning Project with Source Code

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( Note : Project Included with Complete Source code Database Plus Documentation, Synopsis, Report)

Recommender systems are one of the most successful and widespread application of machine learning technologies in business. You can find large scale recommender systems in retail, video on demand, or music streaming.

A Web Base user-item Movie Recommendation Engine using Collaborative Filtering By matrix factorizations algorithm and thus the advice supported the underlying concept is that if two persons both liked certian common movies,then the films that one person has liked that the opposite person has not yet watched are often recommended to him.

A recommender system is a type of information recommend movies to user according to their area of interest. Our recommender system provide personalized information by learning the user‟s interests from previous interactions with that user[2]. In pattern recognition, the knearest neighbours algorithm (k-NN) is a flexible method used for classification. In following cases, the input consists of the k closest examples in given space. If k = 1, then the object is simply assigned to the class of that single nearest neighbour.

Project Features :-

  1. User can register and login.
  2. User can search through various movies and look through its details.
  3. User can give rating to the movies.
  4. User can add movie to their watch list.
  5. User can get movie recommendation (Recommendation algorithm (Collaborative Filtering) which suggests new movies based on the ratings given by user.)

Algorithm :

Collabortive Filtering (Recommender Algorithm)
  1. Collaborative filtering filters information by using the interactions and data collected by the system from other users. It's based on the idea that people who agreed in their evaluation of certain items are likely to agree again in the future.

  2. When we want to find a new movie to watch we'll often ask our friends for recommendations. Naturally, we have greater trust in the recommendations from friends who share tastes similar to our own.

  3. Collaborative-filtering systems focus on the relationship between users and items. The similarity of items is determined by the similarity of the ratings of those items by the users who have rated both items.

  4. There are two types of collaborative filtering

    1. User-based, which measures the similarity between target users and other users.
    2. Item-based, which measures the similarity between the items that target users rate or interact with and other items.

    I have used user based collaborative filtering in this project.

Technologies Used

Web Technologies

Html , Css , JavaScript , Bootstrap , Django

Machine Learning Library In Python3

Numpy , Pandas , Scipy

Database

SQLite

Requirements
python 3.7

pip3

virtualenv

Installation

pip install -r requirements.txt --user

Run server locally

$ python manage.py runserver

Go to localhost:8000

  1. Admin email - admin@admin.com
  2. admin pass - admin

Read Before Purchase  :

  1. One Time Free Installation Support.
  2. Terms and Conditions on this page: https://projectworlds/terms
  3. We offer Paid Customization installation Support
  4.  If you have any questions please contact  Support Section
  5. Please note that any digital products presented on the website do not contain malicious code, viruses or advertising. You buy the original files from the developers. We do not sell any products downloaded from other sites.
  6. You can download the product after the purchase by a direct link on this page.

Online Assignment Submission Project on Python Django

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Latest Python Projects with source code

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Online Assignment Submission Project on Python Django is a system that enable the student to submit their assignment or project online without submitting any physical file. The proposed system helps reducing and minimizing human error, capable to assist supervisors in process controlling and managing students. During lockdown its very help full project.

Functional requirements to explain

  1. identity:administrator, teacher, teaching assistant, student (each person has their own school ID number, within 10 digits, there can be letters, and everyone has a password)
  2. Administrator: issue questions, delete questions 、Teacher information management, class organization (a teaching class has a main teacher, there can be no teaching assistant, or more than one teaching assistant)
  3. Teachers: teaching assistant management (specify the teaching assistant's authority), class management, problem setting, assignment assignment, corrective assignment, statistics job completion
  4. Assistants: rights specified by the teacher to complete all or part of the work, classroom management, the question, assignments, correcting homework, work statistics completion
  5. Students: complete and submit the Assignment.

Technology Used in the project Online Assignment Submission 

  1. We have developed this project using the below technology
  2. HTML : Page layout has been designed in HTML
  3. CSS : CSS has been used for all the desigining part
  4. JavaScript : All the validation task and animations has been developed by JavaScript
  5. Python : All the business logic has been implemented in Python
  6. SQLite : SQLite database has been used as database for the project
  7. Django : Project has been developed over the Django Framework

Supported Operating System

  1. We can configure this project on following operating system.
  2. Windows : This project can easily be configured on windows operating system. For running this project on Windows system, you will have to install
  3. Python 2.7, PIP, Django.
  4. Linux : We can run this project also on all versions of Linux operating systemMac : We can also easily configured this project on Mac operating system.

Online Assignment Submission Project on Django Installation Steps :-

  1. Install Python 3.7 Or Higher
  2. Install Django version 2.2.0
  3. Install all dependencies cmd -python -m pip install --user -r requirements.txt
  4. Finally run cmd - python manage.py runserver

Student Login Details :

username: student@2
password: visg123456

teacher  Login Details :

username: teacher@2
password: visg123456

Admin  Login Details :

Link - http://127.0.0.1:8000/admin
username: visg
password: visg123456

Django Project on Medical Shop Management System

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Medical Store Database Management System using Django

The main objective of the Django Project on Medical Shop Management System is to manage the details of Sells, Medicines, Stocks, Company,Inventory. It manages all the information about Sells, Medical Shop, Inventory, Sells. The project is totally built at administrative end and thus only the administrator is guaranteed the access. The purpose of the project is to build an application program to reduce the manual work for managing the Sells, Medicines, Medical Shop, Stocks. It tracks all the details about the Stocks, Company, Inventory.

Functionalities Provided By Django Project On Medical Shop Management System Are As Follows:

Dealer Management.

  • Add Dealer.
  • View Dealers.

Medicine  Management.

  • Add Medicine.
  • View Medicine.

Employees Management.

  • Add Employees.
  • View Employees.

Customers Management.

  • Add Customers.
  • View Customers

Purchase  Management.

  • New Purchase .
  • View All Purchase

Download Source Code

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Installation Steps Django Project On Medical Shop Management System

Setting up the project:

Download the project zip file. Extract it.

Install Python3 in your system. Download the latest version. https://www.python.org/downloads/

Install django in your system using the following command:

pip install Django==1.11.6

Current version is Django 2.0.9 but this project uses the older version.

You can make use of any text editor such as Sublime, Atom, Pycharm, Webstorm etc. The link for Pycharm is mentioned below: Download the community(free) version. https://www.jetbrains.com/pycharm/download/#section=windows

Open Pycharm, open the extracted project folder in Pycharm. Go to Pycharm terminal and enter the following command:

python manage.py runserver

URL routing is handled in the file: pharma/urls.py

All the functionalities such as Create, update, delete, retrieve are present in the file: pharma/views.py

The database by default used with Django is SQLite3:

The database models are created in the file: pharma/models.py

Iff any changes are made in the models.py file such as adding, deleting fields or new tables, run the following two commands:

python manage.py makemigrations pharma python manage.py migrate

All the SQL queries are generated by Django implicitly. You can view the SQL commands using the following command:

python manage.py sqlmigrate pharma migration_name

"migration_name" is the file name generated during each Model file update, choose any filename from the folder and enter in the command to see the SQL commands of that update.