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Machine Learning Projects – Dynamic ANN based Parking Allocation with Machine Learning Support


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

Contact Number – 7667668009 / 7667664842

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Smart urban transportation management can be considered as a multifaceted big data challenge. It strongly relies on the information collected into multiple, widespread, and heterogeneous data sources as well as on the ability to extract actionable insights from them. The selection of suitable car parks could be influenced by driving and waiting time, parking prices, availability, and accessibility while the supply of unused parking slots might depend on parking location, events within the area, traffic flow, and weather. This project is focused on presenting the research results regarding a solution to predict the number of available parking slots.

Existing system :

  • In the existing system, Allocation of cars is happened in the manual way.
  • Human errors are unavoidable and the cars will be allocated unevenly too.
  • Integrated centralized management by human manually is impossible.

Proposed system :

  • In the Proposed system, An automated machine learning Neural model will allocate the cars.
  • A degenerate technique of finding the free space and allocating based on the time slots and location is done in this project.
  • Automation involves,
    Fast and efficient
    No manual intervention and preference given



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


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


Gradient descent algorithm:

  • Gradient descent, also known as steepest descent
  • It requires information from the gradient vector and manipulate based on the data received.
  • The Output can either set to a fixed value or found by one-dimensional optimization along the training direction at each step.
  • An optimal value for the training rate obtained by line minimization at each successive step is generally preferable

In case of learning these technologies, you can opt for

Sample Code for – Machine Learning Projects 

#import libraries

import math

import pandas_datareader as web

import numpy as np

import pandas as pd

from sklearn.preprocessing import MinMaxScaler

from keras.models import Sequential

from keras import Dropout

import matplotlib as plt


# projects on machine learning

#Create the training dataset

#Create the scaled training dataset

train_data = final_dataset[0:training_data_len , :]

valid_data = final_dataset[training_data_len:,:]

#Split the data into x_train and y_train datasets



x_train,y_train = [],[]

for i in range(60, len(train_data)):


y_train.append(scaled_data[i, 0])

if i<= 60:




Machine Learning Projects Screenshots


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