Python Best Projects

     Python Best Projects is our awe-inspiring research zone which is organized with the massive scope of serve researchers and students with an extraordinary quality in everything. We have well-experienced and record-breaking knowledgeable research team members who have 10+ years of experience in Python programming. Today, we are pioneers in project and research development in the area of computer science & engineering, electrical and communication engineering and electronic engineering. We have the unlimited chances to have the things we want that nobody can rival with. Why you are waiting still? Take your mobile and call us.

Python Best Projects

    Python Best Projects offer overwhelmingly-impressive strategy for you to successfully travel among more challengeable research environment. Python is the best programming language which can be used for data analytics, cryptography, computer vision, robotics, game development, and lot of other fields that popular among students, researchers and programmers. Recently, we have implemented 1000+ Python Best Projects with guaranteed results. We provide well-developed projects for our students with the Project Abstract, Implemented Source Code with the Brief Explanation, Project Report, and PPT Presentation for Review.

Best Things that we supported on Python Programming

-Best Supported Features of Python:

  • Python can easily parse lengthy files using Regular Expressions in Pattern Matching and get the results in few seconds
  • Python can support any type of files (CSV, WORD, JSON, EXCEL, PDF, and TEXT) and can easily read, write and open file contents in automated manner.
  • Using Python, we take necessary decisions using file contents
  • Normally, Python can able to process thousands of file every hour. This makes it most popular in many applications
  • Python can easily spot fix the code errors since it has in-built debugging feature
  • One of the most powerful feature of Python is “Web Scraping”, which can able to automate something from local systems to Internet level
  • Python is very accurate in the time bound scheduled tasks and can easily automate users tasks like Text messages and Sending Emails

-Best Python Libraries:

  • Zappa (Open source Python library)
  • TensorFlow (Support for numerical computation)
  • Bot3 (AWS SDK like EC2 and S3)
  • Bohesh (support Interactive Visualization)
  • Arrow (Data type)

-Best Python Tools:

  • Scikit-learn (Machine Learning)
  • Bokeh (Data Visualization)
  • Numpy (Numerical Python)
  • Numba (App high-perf)
  • Pandas (Data Manipulation)
  • Matplotlib (Data Visualization)
  • Pygr and BioPython (Bioinformatics)

Some Cool Things You can do with Python,

      Our Python Best Projects Service introduces current trend concepts and provides strong base to build deeper knowledge in the field of Python. You can get the source code for the following, like:

  • Machine Learning
  • Deep Learning
  • Raspberry PI
  • Cloud Service Discovery
  • Arduino

Machine Learning

      Python support some modules like Theano, Tensorflow and Scikit-Learn. Using ML, we can find stocks rate, spam and fingerprint. Let’s take one example for ML,

import pandas

import matplotlib.pyplot as plt

from sklearn import model_selection

from sklearn.linear_model import LogisticRegression

from sklearn.tree import DecisionTreeClassifier

from sklearn.neighbors import KNeighborsClassifier

from sklearn.discriminant_analysis import LinearDiscriminantAnalysis

from sklearn.naive_bayes import GaussianNB

from sklearn.svm import SVC

# load dataset[diabetes dataset ]

url = https://archive.ics.uci.edu/ml/machine-learning-databases/pima-indians-diabetes/pima-indians-diabetes.data

# dataset Attributes_names

Attributes_names = [‘Number_of_times_pregnant’, ‘Plasma_glucose’, ‘blood_pressure(mm_Hg)’, ‘skin_fold_thickness(mm)’, ‘Two_Hour_serum_insulin ‘, ‘Body_mass_index’, ‘ Diabetes_pedigree ‘, ‘age’, ‘class’]

read_data = pandas.read_csv(url, Attributes_names=Attributes_names)

array = read_data.values

Columns= array[:,0:8]

data= array[:,8]

# prepare configuration for cross validation test harness

seed = 10

# prepare Machine Learning Algorithm models

models = []

models.append((‘LR’, LogisticRegression()))

models.append((‘LDA’, LinearDiscriminantAnalysis()))

models.append((‘KNN’, KNeighborsClassifier()))

models.append((‘CART’, DecisionTreeClassifier()))

models.append((‘NB’, GaussianNB()))

models.append((‘SVM’, SVC()))

# evaluate each model in turn

FinalRes = []

Attributes_names = []

# Accuracy calculation

scoring = ‘accuracy’

for name, model in models:

kfold = model_selection.KFold(n_splits=10, random_state=seed)

cv_FinalRes = model_selection.cross_val_score(model, Columns, data, cv=kfold, scoring=scoring)

FinalRes.append(cv_FinalRes)

Attributes_names.append(name)

msg = “%s: %f (%f)” % (name, cv_FinalRes.mean(), cv_FinalRes.std())

print(msg)

# boxplot graph  Machine Learning Algorithms Comparison

fig = plt.figure()

fig.suptitle(‘Machine Learning Algorithms Comparison’)

ax = fig.add_subplot(111)

plt.boxplot(FinalRes)

ax.set_xticklabels(Attributes_names)

plt.show()

Raspberry PI

Demand based object which is supported in the field of Electronics and Computers  using Python Programming

Using this, we can watch videos in both online/offline.

Ideas using Raspberry PI Camera:

Thermal Polaroid Camera

3D Printed Raspberry PI Skycam

Webcam Robot

Interactive Led-Mirror

/* Sample Code using Raspberry PI Camera Module*/

# Import Required Package

import picamera

camera = picamera.PiCamera()

#Take a picture (or) capture image save c::\\ drive location

camera.capture(‘c:\\image.jpg’)

In the above code, Raspberry PI camera module has used. To install this, follow the below steps like:

install

$ sudo apt-get update

$ sudo apt-get install python-picamera

 

       We have provided the overall Python Programming Ideas, we follow during Python Best Projects Preparation. Students, you take one step to start your miles of academic journey with us.

 

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