Whatsapp CloneLast Updated on May 3, 2021
This is a WhatsApp clone, in which users can visit different chat rooms and send messages. Users can also create their own chat rooms. Users first have to sign in using their Google accounts.
Though their is still some functionality like searching left to be integrated, the main functions like sending messages, and creating new rooms are there.
This project uses ReactJS on the Front end. Node.js, Express.js and Firebase are used to implement the backend.
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Spotify Data AnalysisLast Updated on May 3, 2021
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
From theses 19 attributes I had made a total of 13 visualizations based on different factors, and had assembled them in 6 dashboards.
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.
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
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
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
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
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
Eda On Sample_SuperstoreLast Updated on May 3, 2021
In this project I have performed Exploratory Data Analysis on a Sample_Superstore Dataset and concluded some of the major key insights which helps the store to generate more revenue. I have used python programming language to perform operations on my dataset. Firstly I identify the missing values , null values and the outliers. I used the libraries to make my work more easier, some of them are pandas, numpy, matplotlib and seaborn . It is must to remove outliers from our dataset because if we don't it will effect our result and produce wrong observations. So to remove them I have used IQR i.e. Interquartile range . With the help of matplotlib and seaborn library I visualize some of my observations with the help of graphs and charts. I have used heatmap to define the co-relation between different features present in our dataset which gives us a brief idea about how one feature is related to other features. I have used different different segments from my dataset for performing analysis and I have concluded some of the parameters like which segment is producing highest profit, loss , discount . Not just finding out the problems I have also mentioned some of the solutions by observing my result in order to increase the profit gain and to reduce the losses faced by the the store. I have performed detailed analysis on this dataset and All these observations that I have performed will definitely help the store to overcome with the problems
Long Term ToolLast Updated on May 3, 2021
My previous project was shear project project that is Long term tool .This tool is used by wind farm owners who want to know in which location it is going to give best profits.
Suppose A wants to start a wind farm business A is having money but he is not aware of wind speeds at particular location ,so he took help from B (The wind pioneers) wind pioneers uses sensor for every wind station to find the wind speed and wind direction. Here wind pioneers role is to record the data which contain wind speeds and wind directions for every hour.
wind pioneers measuring wind speeds at various heights of sensor like ws_120m,ws_100m. For each minute we have some observations ,for every hour the number of observations will increases ,so it is very large data to deal. so we cannot do manual calculations for analyzing this big data. So here we come up with one tool that is long term tool.
I worked on this project along with team this tool provide you interactive software for performing all the analysis like plots, correlation values, scatter plots for finding relationship between two variables. You can just simply download the files that you are working for. It will going to give you everything in detail.
Here we are taking Reference data as NASA data of past 30 years which contains wind speed and wind direction In order to predict the wind speeds of particular location for next 30 years by making use of linear regression model .
Here we are predicting wind speeds of next 30 years for particular location by taking reference data as NASA data.
We are performing linear model for various time periods 1hr,6hr,1 day,3day,7day,10 day,1 month. Again sometimes your weather file and climate file may be differ with time In order to compensate time period we are using time shifting for reference file.
Personal AssistanceLast Updated on May 3, 2021
PERSONAL COMPUTER ASSISTANT
This project work same as siri in iphone google assistance in android.
It will take input from user voice and will manage many things like youtube, google, stackoverflow, date, time
can play music and can shut-down your pc as well.
Necessary pip installation requires are
1. pip install pyttsx3
2. pip install speechRecognition
3. pip install wikipedia
4. pip install webbrowser
5. pip install pipwin
6. pip install PyAudio
What is it capable of doing ?
Input format :
This piece of code tak input from the voice of user
1. strongly assist and open Google,Youtube,Tell date, time, open stackoverflow,gmail and play music for user.
2. This piece of code can also shut down your pc if you ask it
to shutdown and give premission of 'yes'.
Module installation instruction:
1. Please install the modules written at the beggining of the
code in case it throw error.(in mine pc it's working smoothly).
2. Use pip install module_name to install packages or modules.
- Please run this code on jupyter notebook or any offline text edidtor ie (VS Code, Pycharm, Atom, Sublime)
- Please install the module in case not installed using pip install modulename.
- Module list are written at the begging of the code
Age And Gender DetectionLast Updated on May 3, 2021
objective :To build a gender and age detector that can approximately guess the gender and age of the person (face) in a picture or through webcam.
Description : In this Python Project, I had used Deep Learning to accurately identify the gender and age of a person from a single image of a face. I used the models trained by Tal hassner and Gil levi. The predicted gender may be one of ‘Male’ and ‘Female’, and the predicted age may be one of the following ranges- (0 – 2), (4 – 6), (8 – 12), (15 – 20), (25 – 32), (38 – 43), (48 – 53), (60 – 100) (8 nodes in the final softmax layer). It is very difficult to accurately guess an exact age from a single image because of factors like makeup, lighting, obstructions, and facial expressions. And so, I made this a classification problem instead of making it one of regression.
For this python project, I had used the Adience dataset; the dataset is available in the public domain. This dataset serves as a benchmark for face photos and is inclusive of various real-world imaging conditions like noise, lighting, pose, and appearance. The images have been collected from Flickr albums and distributed under the Creative Commons (CC) license. It has a total of 26,580 photos of 2,284 subjects in eight age ranges (as mentioned above) and is about 1GB in size. The models I used had been trained on this dataset.
Working : Open your Command Prompt or Terminal and change directory to the folder where all the files are present.
- Detecting Gender and Age of face in Image Use Command :
python detect.py --image image_name
- Detecting Gender and Age of face through webcam Use Command :