Places-Go

Last Updated on May 3, 2021

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A Web Application build over Django used to find the famous places in a city user want to visit. It will give top 20 places in result with weather report and added google maps which shows the location of that place.

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Password Checker

Last Updated on May 3, 2021

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This can be the most secure way for you to check if your password has ever been hacked. This is a password checker which checks whether this password has been used before or not. and if it has been used then the number of times it has been found. It makes it easy for you to understand that your password is strong enough to keep or is it too light. Its working is pretty simple, in my terminal i write the python file with my code checkmypass.py followed by the password to check if its ever been hacked , its gonna check as many passwords as we list in the terminal. I have used passwords API (pawned password) and SHAH1 (algorithm) to hash the given password into some complex output which is hard to hack also only the first five characters of hash version of password has been used for super privacy so that the real one is safe. The concept of k-anonymity is used  it provides privacy protection by guaranteeing that each record relates to at least k individuals even if the released records are directly linked (or matched) to external information. I have added this on my Github repository.

password-checker/checkmypass.py at main · THC1111/password-checker (github.com)

THIS CAN BE REALLY EFFECTIVE FOR SOME PERSONEL USE.

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Black Friday Sales Prediction

Last Updated on May 3, 2021

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Black Friday Sales Prediction is simply a prediction of sales of different products. Main goal of this project is to find out customer purchase behaviour against various products of different categories. I have  purchase summary of various customers for selected high volume products from last month. The data set also contains customer demographics (age, gender, marital status, city type, stay in current city), product details (product id and product category) and Total purchase amount from last month. Based on this data we will predict sales.

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 find out correlation matrix which shows the correlation between columns to each other.

Then i split the data. Then i create a regression model. I trained that regression model using Random Forest Algorithm .I feed training dataset to model using random forest algorithm. After creating model i did similiar data preprocessing to test dataset . And then i feed test dataset to trained regression 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.




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Spotify Data Analysis

Last Updated on May 3, 2021

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Spotify Data Analysis

This project was made by using Tableau Software. Tableau is an interactive visualization software. A lot of functions can be performed by using this software. Many charts can be drawn by using single or multiple attributes. Colours can be added to show variation in the charts or to show the intensity of a particular attribute. Charts/graphs that can be made are:

1.     Pie chart

2.     Bar graph

3.     Line graph

4.     Waterfall chart

In my project, I had used a dataset from Kaggle. The dataset was about the details of songs from Spotify app. The dataset had 119 different attributes out of which 2 were in string format and the rest were in numerical. A few attributes were:

1.     Song name

2.     Artist name

3.     Danceability

4.     Loudness

5.     Liveliness

6.     Speechiness

7.     Tempo

From theses 19 attributes I had made a total of 13 visualizations based on different factors, and had assembled them in 6 dashboards.

Dashboard 1:

It gives the analysis of the danceability. It shows 2 analysis:

1.     Artists who provide most danceability

It is a bar graph with danceability in the y-axis. It shows that the artist named Katy Perry had most danceability in her songs.

2.     Artists in top 10 with the most danceability

It is a bar graph, which dims its colour as the bar’s size decreases.

Dashboard 2:

It gives the analysis of the genre of songs. It shows 2 analysis:

1.     How the proposition of genres has changed in 10 years

Canadian pop was famous in 2009 as well as in 2020. While Detroit hip hop is not as famous now.

2.     Least famous artists and the genre of their songs

It is a point chart which shows which artist makes songs in which genre

Dashboard 3:

It gives the analysis of the popularity. It shows 2 analysis:

1.     Most popular artists and their popularity

It shows how the popularity of the artists have changed over the years.

2.     Most popular artists and their song’s popularity

It shows that the artist Sara Barailles has the most popularity with 71 average popularity

Dashboard 4:

It gives the analysis of the positivity. It shows 2 analysis:

1.     Loudness vs energy with respect to positivity

A colour changing bar graph which dims as the value decreases.

2.     Artist with most popularity

A bar graph showing artist Katy Perry with most positive songs

Dashboard 5:

It shows 2 analysis:

1.     Song names that start with question related phrases

Such songs had a popularity index of only 1055

2.     Change in speechiness vs beats

A bar graph that shows the change of speechiness vs beats over the years

Dashboard 6:

It gives the analysis of the most popular artist Katy Perry. It shows 3 analysis:

1.     Songs sung over the years

It is in tabular format with 2 columns

2.     Popularity of songs

It shows how much her songs have been popular over the years

3.     Popularity and number of times her songs appeared in top 10

It shows her most popular and hit songs popularity index

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Human Computer Interaction Using Iris,Head And Eye Detection

Last Updated on May 3, 2021

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HCI stands for the human computer interaction which means the interaction between the humans and the computer.

We need to improve it because then only it would improve the user interaction and usability. A richer design would encourage users and a poor design would keep the users at bay.

We also need to design for different categories of people having different age,color,gender etc. We need to make them accessible to older people.

It is our moral responsibility to make it accessible to disabled people.

So this project tracks our head ,eye and iris to detect the eye movement by using the viola Jones algorithm.But this algorithm does not work with our masks on as it calculated the facial features to calculate the distance.

It uses the eucledian distance to calculate the distance between the previous frame and the next frame and actually plots a graph.

It also uses the formula theta equals tan inverse of b/a to calculate the deviation.

Here we are using ANN algorithm because ANN can work with incomplete data. Here we are using constructive or generative neural networks which means it starts capturing our individual images at the beginning to create our individual patterns and track the eye.

Here we actually build the neural network and train it to predict

Finally we convert it to mouse direction and clicks and double clicks on icons and the virtual keyboard.

As a contributing or moral individuals it is our duty to make devices compatible with all age groups and differently abled persons.

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Natural Language Processing

Last Updated on May 3, 2021

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The problem statement is about allocation of projects using given dataset. We are provided with some requirements like project details (project name, project location and required project skills) and

candidate details (candidate id, location, candidate skills and description). From the given dataset, we have to filter the perfect candidate based on the requirements and their skills. Our work is to check whether the candidate is having required skills to do the project and also determine the evaluation status based on their location. If suppose the candidates is having required skills and match the location, the candidate is selected for that project, if does not match we reject the candidate for that project. In such case the rejected

candidates are checked with other projects. The foremost step is to clean up the data to highlight attributes.

Cleaning (or pre-processing) the data typically consists of a number of steps like remove punctuation, tokenization and remove stop words. I have taken a set of keywords which is most related to the skills that’s given in the project based on certain criteria .To describe the presence of keywords within the cleaned data we need to vectorize the data by Bag of Words. We are going to filter the candidate skills according to the current trends. Based on their number of skills known(languages) they will be prioritized. So, we want to use NLP Toolkit to arrange the candidates by their preferences. By doing this process in the given dataset, we can able to filter 50% of data. If the skills of the prioritized candidates match with same location of the project, the similarities will be calculated and the candidate is selected for that project else the candidate is rejected.

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