projects for python – Amran Battle Death Analysis using Python Machine Learning


machine learning projects

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The battle of Amran, refers to a battle that takes place in the year of 2014,  between the Houthi Zaydi movement, and the Yemeni government of president Abd Rabbuh Hadi. The Houthis eventually won the battle, leading them to the capture of Sana’a. Extracting the battle death data analysis with year and date using the Python library pandas and ploting the data with Matplotlib python library. The DataFrame is a tabular data structure comprising a set of ordered columns and rows. It can be thought of as a group of Series objects that share an index (the column names). There are a number of ways to initialize a DataFrame object.



The battle begin in the early days of July 2014 when Houthi rebels stormed the city of Amran, guarded by the general Hameed Al-Qushaibi. On 8 July 2014, army reinforcements sent to Amran on Sunday were locked in fierce clashes with Shiite Houthi in Dharawan, 15 kilometres (nine miles) from Sanaa, and in and around the city itself, military sources said. On the same day, Hadi fighter jets bombed Amran’s Warak neighborhood, hours after it was seized by rebels. During previous battles, 460 people left dead, with some 160 to be wounded, including civilians. In July 9, Yemeni government accused the Houthi rebels for atrocities, during a raid in the headquarters of the 310th Armored Brigade, looted weapons and equipment there, and killed a number of soldiers and officers, said Yemen’s Supreme Security Committee, quoted by state news agency Saba. Along the dead, was the general responsibly for the region, Hameed Al-Qushaibi. The general, later mourned for his death. The Houthi fighters, broken the deal between them and general al-Qushaibi, that has the Houthi to allowed his brigade to abandon the city, and bringing an end to the fight in Amran. However another pact made with the Houthis, to retreat from the Amran city, but the pact never took place, allowing the Houthis to attack and capture Sana’a. Amran was fully captured by 10 July 2014 Data is getting bigger and more diverse every day. Therefore, analyzing and processing data to advance human knowledge or to create value is a big challenge. To tackle these challenges, you will need domain knowledge and a variety of skills, drawing from areas such as computer science, artificial intelligence (AI) ( Students can opt AI Internship  to  learn basics of AI ) and machine learning (ML) ( Students can opt Machine learning Internship  to  learn basics of  ML) , Data Science ( Students can opt data science Internship  to  learn basics of AI )  statistics and mathematics, and knowledge domain.



  • System : Intel inside i3
  • System Type : 64-bit Operating System
  • Storage :500GB
  • RAM :4 GB



  • Operating system : Windows 10
  • Software : Anaconda , Python
  • Python Libraries: Matplotlib, Pandas.

Why, Machine Learning projects  

After the fall of Amran in August, the Houthis began holding mass demonstrations in Sana’a, pressuring President Abd Rabbuh Mansur Hadi to reverse a cut to fuel subsidies and calling on the government to step down. Representatives of the group met with government officials in an attempt to find a solution to the standoff, but the Houthis rejected the government’s concessions as insufficient. On 9 September, Houthi protesters in northwest Sana’a were fired upon by security forces as they marched on the cabinet office. Seven were killed.  the Houthis, finally stormed the Sana’a in 16 of September, and captured in 21 of the month. In the real world, we are surrounded by humans who can learn everything from their experiences with their learning capability, and we have computers or machines which work on our instructions. But can a machine also learn from experiences or past data like a human does? So here comes the role of Machine Learning inplant training .

Machine Learning is said as a subset of artificial intelligence that is mainly concerned with the development of algorithms which allow a computer to learn from the data and past experiences on their own. The term machine learning was first introduced by Arthur Samuel in 1959.

Machine learning enables a machine to automatically learn from data, improve performance from experiences, and predict things without being explicitly programmed.

With the help of sample historical data, which is known as training data, machine learning algorithms build a mathematical model that helps in making predictions or decisions without being explicitly programmed. Machine learning brings computer science and statistics together for creating predictive models. Machine learning constructs or uses the algorithms that learn from historical data. The more we will provide the information, the higher will be the performance.

A machine has the ability to learn if it can improve its performance by gaining more data.

How does Machine Learning work

A Machine Learning system ( machine learning projects ) learns from historical data, builds the prediction models, and whenever it receives new data, predicts the output for it. The accuracy of predicted output depends upon the amount of data, as the huge amount of data helps to build a better model which predicts the output more accurately.

Suppose we have a complex problem, where we need to perform some predictions, so instead of writing a code for it, we just need to feed the data to generic algorithms, and with the help of these algorithms, machine builds the logic as per the data and predict the output. Machine learning has changed our way of thinking about the problem.

Features of Machine Learning:

  • Machine learning uses data to detect various patterns in a given dataset.
  • It can learn from past data and improve automatically.
  • It is a data-driven technology.
  • Machine learning is much similar to data mining as it also deals with the huge amount of the data.

Need for Machine Learning

The need for machine learning is increasing day by day. The reason behind the need for machine learning is that it is capable of doing tasks that are too complex for a person to implement directly. As a human, we have some limitations as we cannot access the huge amount of data manually, so for this, we need some computer systems and here comes the machine learning to make things easy for us.

We can train machine learning algorithms by providing them the huge amount of data and let them explore the data, construct the models, and predict the required output automatically. The performance of the machine learning algorithm depends on the amount of data, and it can be determined by the cost function. With the help of machine learning, we can save both time and money.

The importance of machine learning can be easily understood by its uses cases, currently, machine learning is used in self-driving carscyber fraud detectionface recognition, and friend suggestion by Facebook, etc. Various top companies such as Netflix and Amazon have build machine learning models that are using a vast amount of data to analyze the user interest and recommend product accordingly.

Following are some key points ( To be noted for , projects for python  ) which show the importance of Machine Learning:

  • Rapid increment in the production of data
  • Solving complex problems, which are difficult for a human
  • Decision making in various sector including finance
  • Finding hidden patterns and extracting useful information from data.

Classification of Machine Learning

At a broad level, machine learning can be classified into three types:

  1. Supervised learning
  2. Unsupervised learning
  3. Reinforcement learning

1) projects for python  – using Supervised Learning

Supervised learning is a type of machine learning method in which we provide sample labeled data to the machine learning system in order to train it, and on that basis, it predicts the output.

The system creates a model using labeled data to understand the datasets and learn about each data, once the training and processing are done then we test the model by providing a sample data to check whether it is predicting the exact output or not.

The goal of supervised learning is to map input data with the output data. The supervised learning is based on supervision, and it is the same as when a student learns things in the supervision of the teacher. The example of supervised learning is spam filtering.

Supervised learning can be grouped further in two categories of algorithms:

  • Classification
  • Regression

2) projects for python  – using Unsupervised Learning

Unsupervised learning is a learning method in which a machine learns without any supervision.

The training is provided to the machine with the set of data that has not been labeled, classified, or categorized, and the algorithm needs to act on that data without any supervision. The goal of unsupervised learning is to restructure the input data into new features or a group of objects with similar patterns.

In unsupervised learning, we don’t have a predetermined result. The machine tries to find useful insights from the huge amount of data. It can be further classifieds into two categories of algorithms:

  • Clustering
  • Association

3) projects for python – using Reinforcement Learning

Reinforcement learning is a feedback-based learning method, in which a learning agent gets a reward for each right action and gets a penalty for each wrong action. The agent learns automatically with these feedbacks and improves its performance. In reinforcement learning, the agent interacts with the environment and explores it. The goal of an agent is to get the most reward points, and hence, it improves its performance.

The robotic dog, which automatically learns the movement of his arms, is an example of Reinforcement learning.



Python is a general purpose, dynamic, high-level, and interpreted programming language. It supports Object Oriented programming approach to develop applications. It is simple and easy to learn and provides lots of high-level data structures.

Python is easy to learn yet powerful and versatile scripting language, which makes it attractive for Application Development.

Python’s syntax and dynamic typing with its interpreted nature make it an ideal language for scripting and rapid application development.

Python supports multiple programming pattern, including object-oriented, imperative, and functional or procedural programming styles.

Python is not intended to work in a particular area, such as web programming. That is why it is known as multipurpose programming language because it can be used with web, enterprise, 3D CAD, etc.

Python Features

Python provides many useful features which make it popular and valuable from the other programming languages. It supports object-oriented programming, procedural programming approaches and provides dynamic memory allocation. We have listed below a few essential features. Most of the students will prefer to learn python through python internship

1) Easy to Learn and Use

Python is easy to learn as compared to other programming languages. Its syntax is straightforward and much the same as the English language. There is no use of the semicolon or curly-bracket, the indentation defines the code block. It is the recommended programming language for beginners.

2) Expressive Language

Python can perform complex tasks using a few lines of code. A simple example, the hello world program you simply type print(“Hello World”). It will take only one line to execute, while Java or C takes multiple lines.

3) Interpreted Language

Python is an interpreted language; it means the Python program is executed one line at a time. The advantage of being interpreted language, it makes debugging easy and portable.

4) Cross-platform Language

Python can run equally on different platforms such as Windows, Linux, UNIX, and Macintosh, etc. So, we can say that Python is a portable language. It enables programmers to develop the software for several competing platforms by writing a program only once.

5) Free and Open Source

Python is freely available for everyone. It is freely available on its official website It has a large community across the world that is dedicatedly working towards make new python modules and functions. Anyone can contribute to the Python community. The open-source means, “Anyone can download its source code without paying any penny.”

6) Object-Oriented Language

Python supports object-oriented language and concepts of classes and objects come into existence. It supports inheritance, polymorphism, and encapsulation, etc. The object-oriented procedure helps to programmer to write reusable code and develop applications in less code.

7) Extensible

It implies that other languages such as C/C++ can be used to compile the code and thus it can be used further in our Python code. It converts the program into byte code, and any platform can use that byte code.

8) Large Standard Library

It provides a vast range of libraries for the various fields such as machine learning, web developer, and also for the scripting. There are various machine learning libraries, such as Tensor flow, Pandas, Numpy, Keras, and Pytorch, etc. Django, flask, pyramids are the popular framework for Python web development.

9) GUI Programming Support

Graphical User Interface is used for the developing Desktop application. PyQT5, Tkinter, Kivy are the libraries which are used for developing the web application.

10) Integrated

It can be easily integrated with languages like C, C++, and JAVA, etc. Python runs code line by line like C,C++ Java. It makes easy to debug the code.

  1. Embeddable

The code of the other programming language can use in the Python source code. We can use Python source code in another programming language as well. It can embed other language into our code.

  1. Dynamic Memory Allocation

In Python, we don’t need to specify the data-type of the variable. When we assign some value to the variable, it automatically allocates the memory to the variable at run time. That’s why students prefer to learn python through python-training-in-chennai

In case of learning these technologies, you can opt for

Machine learning projects –   Coding

from datetime import datetime

import pandas as pd

import matplotlib as pyplot

data = {‘date’: [‘2014-05-01 18:47:05.069722’, ‘2014-05-01 18:47:05.119994’,

#Extract Data of 2014

df.index = df[‘date’]

del df[‘date’]


#Extract Data on May 2014


#Extract Data on May 3rd 2014

df[datetime(2014, 5, 3):]

#Extract Data between May 3rd and May 4th



Machine learning projects –  Project Output :


projects for python

Request for  Project –   [email protected] ; [email protected]

Contact Number – 7667668009 / 7667664842

For complete project lists  –  final year project for cse

For internship – internship  in chennai

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