Long Term Tool

Last 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.

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Multi Label Question Classification For Agricultural Domain

Last Updated on May 3, 2021



Basically, Multi-Label means, the questions asked by a client are of what type? 

Here our ML model should be able to segregate the questions into descriptive type question or one-word type question or both and then answer it effectively.

We have classified the data into three major types:

1) Definition Type Question 2) Descriptive Type Questions 3) Factoid Type Questions 

KEYWORD IDENTIFIER Keywords are used to identify the question type.

The input query is scanned and the primary keywords such as who, when etc. are identified.

These keywords help in finding out the expected answer type. But words like how and which do not give a clear idea about the question type.

To get a clear idea about the question type and obtain the relevant answer, additional keywords are required.

These are known as secondary keywords. The secondary keywords provide additional information about the question type which further helps in extracting the answer from the document.

 For example

a)Which sector is the backbone of the Indian economy? Which is the best season for a particular crop?

 Here the “which” keyword acts as the primary key. This helps us understand the question type as which and the expected answer for such type of question is a factoid type of answer which precisely answers the type of “sector”


Data preprocessing techniques such as data redundancy, stop words, data cleaning, puntuactions, etc are done first.

Using the Naive Bayes Classifier &Python's Scikit-learn package we were able to upload the Model database containing 50 questions with their answers.

Naive Bayes and SVM is a simple technique for constructing classifiers: models that assign class labels to problem instances, represented as vectors of feature values, where the class labels are drawn from some finite set. 

Training and testing of data are done and then the model predicts the appropriate answer.

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Resume Up-Loader

Last Updated on May 3, 2021



Ever you apply to an organisation with cv through mail but it might happen that specific organisation don't know that actually candidate need like job preference or type of job, so it get easier when we use this app called resume up-loader.

working model:-

It is my first self project using Django (python

framework) called Resume Up-loader .

where you put every detail about yourself ,job location photos,signature,CV,after submitting the information load on the server and next page you can look all your information and download the Resume also ,i am continuously working on it and upgrading that it list all the company on that preference job location for your current qualification and skill it help the candidate to know in which company is he/she is suitable for and it also company to know their candidate batter

Under a projects section

To make this single page website I have use the python web framework called Django

Django is a high-level Python Web framework that encourages rapid development and clean, pragmatic design. Built by experienced developers, it takes care of much of the hassle of Web development, so you can focus on writing your app without needing to reinvent the wheel. It’s free and open source.

I have also use HTML to define the structure of front-end and use style tag to make this beautiful

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Hyderabad House Price Predictor

Last Updated on May 3, 2021


Hyderabad House Price Predictor

ML model which predicts the price of a house based on features like total Sq. ft area,total number of bedrooms,balconies etc.

The front-end of this model is made by boot-strap and Flask,where as the backend is a Machine learning model which is trained on the housing-price dataset and the algorithm used is Random-Forest

the model is hosted at------> https://homepricepredictor.herokuapp.com/

General Overview of the Project 

Starting of with the home page which is designed using bootstrap classes,here we in this template the general overview of the project is mentioned,along with that the parameters which are required for predicting the price of the house are also mentioned here,here's a glimpse of it