Crop Detection Using Ml

Last Updated on May 3, 2021

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Agricultural monitoring, in particular in developing countries, can help prevent famine and support humanitarian efforts. A central challenge is yield estimation, which is to predict crop yields before harvesting. We introduce a scalable, accurate, and inexpensive method to predict crop yields using publicly available remote sensing data. This solution if implemented at the soil health centers which have been set up by the government could help all the farmers to use minimum fertilizers, so as to maintain the soil health and also would provide them an opportunity to gain at most revenue from the same piece of land. Predictive analysis to suggest the top three more suitable crop based on the nutrition levels of the soil, temperature and also the expected revenue that this particular crop could generate. 

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Breast Cancer Analysis And Prediction Using Ml

Last Updated on May 3, 2021

About

Project EDA-

Done by using module called Pandas Profiling


Data Set Information:

Features are computed from a digitized image of a fine needle aspirate (FNA) of a breast mass. They describe characteristics of the cell nuclei present in the image. n the 3-dimensional space is that described in: [K. P. Bennett and O. L. Mangasarian: "Robust Linear Programming Discrimination of Two Linearly Inseparable Sets", Optimization Methods and Software 1, 1992, 23-34].

This database is also available through the UW CS ftp server: ftp ftp.cs.wisc.edu cd math-prog/cpo-dataset/machine-learn/WDBC/

Also can be found on UCI Machine Learning Repository: https://archive.ics.uci.edu/ml/datasets/Breast+Cancer+Wisconsin+%28Diagnostic%29


Attribute Information:

  1. ID number
  2. Diagnosis (M = malignant, B = benign) 3-32)

Ten real-valued features are computed for each cell nucleus:

a) radius (mean of distances from center to points on the perimeter) b) texture (standard deviation of gray-scale values) c) perimeter d) area e) smoothness (local variation in radius lengths) f) compactness (perimeter^2 / area - 1.0) g) concavity (severity of concave portions of the contour) h) concave points (number of concave portions of the contour) i) symmetry j) fractal dimension ("coastline approximation" - 1)

The mean, standard error and "worst" or largest (mean of the three largest values) of these features were computed for each image, resulting in 30 features. For instance, field 3 is Mean Radius, field 13 is Radius SE, field 23 is Worst Radius.

All feature values are recoded with four significant digits.

Missing attribute values: none

Class distribution: 357 benign, 212 malignant

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Enterprise Ai

Last Updated on May 3, 2021

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Enterprise AI is about enhancing the customer satisfaction index and to ensure customer stickiness to your organization. By infusing emerging technologies like artificial intelligence to engage and retain the set of customers. Using AI algorithm, we should address the use case. The business operation

processes like determining the customer sentiments from various different media like - Social media, Audio Calls, Video Calls, Images, Emails & Chats, interact with customers to provide quick and effortless

solutions, analyze and learn from buying behaviour to generate next best offer, ascertain customer retention and ensure lesser churn, derive AI-based Customer Segmentation, manage customer

touchpoints, evaluate customer feedback and engage with the customers. We provide a membership card to all the customers who purchase stocks in the store. By scanning the QR code the customer can fill the

feedback. Through the user can easily complete the feedback (Bad, Good, Very good) after purchasing. We are providing three categories (Bronze, Gold and Platinum) for our customers to categorize their

purchasing list to calculate the purchasing efficiency based on their quality ,they purchase. The customer who gives feedback as very good, they come under platinum category, best offers are provided to

them (free purchase for Rs.1000). Notifications will be sent to customers through the messages about the new products available along with its price. Best offers are also provided on festival occasions. We classify the feedback using classification algorithms like random forest to get the positive and negative feedbacks.

Negative feedback will be collected and rectified soon. Through this approach, the shopkeeper is able to get clear feedback about his shop easily.



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Hacktube

Last Updated on May 3, 2021

About

A Chrome extension that fights online harassment by filtering out comments with strong language.

Inspiration

YouTube is a place for millions of people to share their voices and engage with their communities. Unfortunately, the YouTube comments section is notorious for enabling anonymous users to post hateful and derogatory messages with the click of a button. These messages are purely meant to cause anger and depression without ever providing any constructive criticism. For YouTubers, this means seeing the degrading and mentally-harmful comments on their content, and for the YouTube community, this means reading negative and offensive comments on their favorite videos. As young adults who consume this online content, we feel as though it is necessary to have a tool that combats these comments to make YouTube a safer place.

What it does

HackTube automatically analyzes every YouTube video you watch, targeting comments which are degrading and offensive. It is constantly checking the page for hateful comments, so if the user loads more comments, the extension will pick those up. It then blocks comments which it deems damaging to the user, listing the total number of blocked comments at the top of the page. This process is all based on user preference, since the user chooses which types of comments (sexist, racist, homophobic, etc) they do not want to see. It is important to note that the user can disable the effects of the extension at any time. HackTube is not meant to censor constructive criticism; rather, it combats comments which are purely malicious in intent.

How we built it

HackTube uses JavaScript to parse through every YouTube comment almost instantly, comparing its content to large arrays that we made which are full of words that are commonly used in hate speech. We chose our lists of words carefully to ensure that the extension would focus on injurious comments rather than helpful criticism. We used standard HTML and CSS to style the popup for the extension and the format of the censored comments.

Challenges we ran into

We are trying to use cookies to create settings for the user which would be remembered even after the user closes the browser. That way anyone who uses HackTube will be able to choose exactly which types of comments they don't want to see and then have those preferences remembered by the extension. Unfortunately, Chrome blocks the use of cookies unless you use a special API, and we didn't have enough time to complete our implementation of that API at this hackathon.

Accomplishments that we're proud of

We are proud of making a functional product that can not only fight online harassment and cyberbullying but also appeal to a wide variety of people.

What we learned

We learned how to dynamically alter the source code of a webpage through a Chrome extension. We also learned just how many YouTube comments are full of hate and malicious intent.

What's next for HackTube

Right now, for demo purposes, HackTube merely changes the hateful comments into a red warning statement. In the future, HackTube will have an option to fully take out the malicious comment, so users’ YouTube comments feed will be free of any trace of hateful comments. Users won’t have to worry about how many comments were flagged and what they contained. Additionally, we will have a way for users to input their own words that offend them and take the comments that contain those words out of the section.

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Long Term Tool

Last Updated on May 3, 2021

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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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Machine Learning (Heart Disease Prediction Model)

Last Updated on May 3, 2021

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This is web based API model which predicts the probability of having a heart disease

Here I had a dataset of few patients where I had information like CRF, Hypothrodism, HT,DM.

I have splitted the data so that I can train , and then test our prediction by finding out accuracy using various Python Algorithms.

The library used here are numpy , matplotlib, pandas, sklearn and pickle of Python.

I preprocessed the data and performed various splitting options.

I observed various plots using library matplotlib.

I have used numpy and pandas to to read the data and observe various statistical things.

I have used various algorithms like:

Random forest ( model file in github as modelRF.py)

Decision tree ( modelDT.py).

SVM (modelSVM.py)

ANN (modelANN.py)

Naive Bayes (modelNB.py)

In each algorithm I fitted my training data, saved model to the disk , loaded the model using Pickle library and then finally compared the result .

All the accuracy was found out for each algorithm and all of them showed accuracy greater than 85%.

All this model building was done in model.py files , modelNB (naive bayes) modelSVM (support vector machine) etc . according to the algorithm

After finding accuracy from every algorithm.

I finally built a model using library flask , request,jsonify,render_template ,keras and loaded the model using pickle .

The final features of the model was predicted and finally created as app.py.

As the model runs on local host we also added various html tags and styling using CSS to make it more presentable.

The code is shared freely on Github platform.

Link added below

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