Intermediate Pandas Python Library For Data Science

Last Updated on May 3, 2021

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the advanced methods to handle missing values

how to sort, select and slice data for easier manipulation.

Learn about different types of joins, sorting and binning data

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Javascript Form Validation Project

Last Updated on May 3, 2021

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I have made JavaScript Form Validation using HTML/CSS/JavaScript. JavaScript form validation is a technical process where a web-form checks if the information provided by a user is correct. This form can be used for updating your information like name,city,etc. , and then after click on submit button the information which you have updated will be shown.

In the JavaScript Form Validation Project, when the user provides all the data and submits the form, usually by hitting the button, the information is sent to the server and validated. The response of the validator is sent back to the user's computer and it's visualised as either a confirmation message.

JavaScript Form Validation is an important feature of good user experience; by catching invalid data, the user can fix it straight away.


Working of JavaScript Form Validation Project:-

In this Project, when you enter data, the browser and/or the web server will check to see that data is in the correct format and within the constraint set by the application.If the information is correctly formatted, the application allows the data to be submitted and saved in a database.

This project is done by using validation attributes on form elements .These are:-

1 . required :- Specifies whether a form field needs to be filled in before the form can be submitted.

2. type :- Specifies whether the data needs to be a number, an emaill address, or some other specific preset type.

3. pattern :- Specifies a regular expression that defines a pattern the entered data needs to follow.

In this Project, after giving your personal information like your name, your address,etc, and then if you click on submit button , then the information which you have filled will be shown .

If you want to update some information ,then you can also do by clicking on update button.The updated information will be shown.

If you want to delete some record ,then you can also delete it by clicking on delete button.


Duration of Project:- 10 months

My role in this project is of Developer

Skills used :- HTML/CSS/JavaScript


More Details: JavaScript Form Validation Project

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Dog And Cat Image Classification

Last Updated on May 3, 2021

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Dog and cat image classification

The project classifies an image into a dog or a cat. The model has been built by using Convolutional Neural Network or also known as CNN. CNN is a part of deep learning which deals with analysing images. It is widely used for image recognition and classification. This project was developed by using Python. Python is an interpreted, high-level and general-purpose programming language. Python was implemented on Jupyter Notebook.

Libraries and Functions used-

Various Python libraries were used while developing the ML model. The tools used were:

1. tensorflow- It focusses on training of neural networks

2. load_model- This library is used to load a model and construct it identically

3. tkinter- It is a python GUI toolkit

4. PIL- It is Python Image Library that supports in doing operations with images

5. Filedialog- It is used for selecting a file/directory

6. Playsound- It is used for playing audios

7. ImageDataGenerator- It is a class of Keras library used for real-time data augmentation

8. Flow_from_directory- It is an image augmentation tool

9. keras Preprocessing- It is the data preprocessing module of keras which provides utilites for working with image data.

10. load_img- It loads the image in PIL format.

11. img_to_array- It changes the image into a numpy array.

12. expand_dim- It expands the dimension to add an extra dimension for a batch of only one image with axis=0.

In this neural network 2 activation functions were used-

1. ReLu

2. Sigmoid

The methods followed were:

1.     Pre-processing of data

1.1  Training data

1.2  Testing data

2.     Building CNN

2.1  Adding the first convolution layer

2.2  Pooling

2.3  Adding the second convolution layer

2.4  Flattenng

2.5  Full connection

2.6  Output layer

The accuracy of last(50) epoch was 97%

Prediction Function

This function loads the ML model and take the image input given by the user and then pre-process it. Later the pre-processed image goes as an input to ML model which gives the prediction. For our output, this code plays a sound corresponding to the prediction.

Model

The final page asks the user to select an image from the local computer. The tab’s name is ‘Image Classifier’.

Once the user selects the image, the model successfully predicts whether the image is of a dog or a cat. The model also plays a sound stating about the prediction.

More Details: Dog and Cat Image classification

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Research Paper On Face Detection Using Haar Cascade Classifier

Last Updated on May 3, 2021

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Abstract:

In the last several years, face detection has been listed as one of the most engaging fields in research. Face detection algorithms are used for the detection of frontal human faces. Face detection finds use in many applications such as face tracking, face analysis, and face recognition. In this paper, we are going to discuss face detection using a haar cascade classifier and OpenCV. In this study, we would be focusing on some of the face detection technology in use.



Conclusion:

In this study, we covered and studied in detail about face detection technique using haar cascades classifier and OpenCV to get the desired output. Using the OpenCV library, the haar cascade classifier was able to perform successful face detection with high accuracy and efficiency. We also used the OpenCV package to extract some of the features of the face to compare them. Also, we discussed some popular face detection methods. Further, we discussed the scope of face detection in the future and some of its applications. At last, we conclude that the future of facial detection technology is bright Security and surveillance is the major segments that will be deeply influenced. Other areas that are now welcoming it are private industries, public buildings, and schools

More Details: Research paper On FACE DETECTION USING HAAR CASCADE CLASSIFIER

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Task-Manager Backend Rest-Api(Node.Js)

Last Updated on May 3, 2021

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Technology Used:

  • Node.js
  • Express js
  • MongoDB


Library Used:

  • jwt (json web token)
  • bcrypt
  • validator
  • sharp
  • multer


General Description:


  • In this project user can create its own tasks.
  • User can manage their tasks according to their preferences.
  • User can edit or delete the particular task and also user can track the status of task (i.e completed or pending).


Usage:


  • In order to use application you should register in an application. You can make it by calling Sign Up API.
  • The password is stored in Encrypted format in database.
  • In Login API we generate an access token using jwt.
  • In order to call create,update,delete API'S we have to pass an access token in header section of the request.
  • If we don't pass an access token then user will got a message 'Please Authenticate'.


Database Structure:


Task:

description : String,

completed :Boolean,

owner : ObjectId,

timestamps :true


User:

name : String,

email :String,

password :String

age : Number,

tokens:[{

token:type:String

}],

avtar : Buffer


API'S:


User:

URL TYPE Description

  • /users/login POST login
  • /users/ POST SignUp
  • /users/me GET Profile
  • /users/logout POST logout
  • /users/logoutall POST logout from all devices
  • /users/me DELETE delete user
  • /users/me PUT Updating user
  • /upload POST Uploading avtar
  • /users/me/avtar DELETE delete user avtar



Task:

URL TYPE Description

  • /task POST Create Task
  • /task GET Getting Task
  • /task/:id PUT Updating Task
  • /task/:id DELETE Deleting Task
  • /users/logoutall POST logout from all devices
  • /users/me DELETE delete user


More Details: Task-Manager Backend REST-API(Node.js)

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Loan Prediction

Last Updated on May 3, 2021

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A Company wants to automate the loan eligibility process (real time) based on customer detail provided while filling online application form. These details are Gender, Marital Status, Education, Number of Dependents, Income, Loan Amount, Credit History and others. To automate this process, they have provided a dataset to identify the customers segments that are eligible for loan amount so that they can specifically target these customers. So in this project our main objective is to predict whether a individual is eligible for loan or not based on given dataset.


For simplicity i divided my projects into small parts-


  1. Data Collection :- I collected data from 'Anylitical Vidhya' as a CSV file. We have two CSV file one is train data which is used for training the data and other is test data which is used for prediction based on training of model.
  2. Import Libraries:- I import differnt Sklearn package for algorithm and different tasks.
  3. Reading data:- i read the data using pandas 'read csv()' function.
  4. Data Preprocessing -: In this part i first found missing values then i remove a column or imputed some value (mean, mode, median) According to the amount of data missing for a particular column.

I checked the unique value in each column. Then i did label encoding to convert all string types data to integer value. I used dummie function to convert each unique value to different columns . I find out correlation matrix which shows the correlation between columns to each other.

Then i split the data. I did analysis on each column and row of dataset.


Here i selected a classifier algorithm because it is a classification problem i.e. in this problem target value is of categorial datatype.


Then i create a model . I trained that model using Logistic regression Algorithm , which is a classification algorithm. I feed training dataset to model using Logistic regression algorithm. After creating model i did similiar data preprocessing to test dataset . And then i feed test dataset to trained model which predict the values of this test dataset. And then i found accuracy of this model using actual target value which is given in training dataset. and predict target value which we predict from test dataset.

After this i used another algorithm which is random forest classifier. i did traied the model using random forest classifier and then calculate the accuracy.

I compared the accuracy of both algorithm and i preffered algorithm which had better accuracy.


In this project i got 78.03% accuracy when i create model using random forest classifier and got 80.06% when i create model using logistic regression.


More Details: Loan prediction

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