redfoxkingsley Goto Github PK

agricultural-price-prediction-and-visualization-on-android-app

In Agriculture Price Monitioring , I have used data provided by open government site data.gov.in, which updates prices of market daily . Working Interface Details: We have provided user choice to see current market prices based on two choices: market wise or commodity wise use increase assesibility options. Market wise: User have to provide State,District and Market name and then select market wise button. Then user will be shown the prices of all the commodities present in the market in graphical format, so that he can analyse the rates on one scale. This feature is mostly helpful for a regular buyer to decide the choice of commodity to buy. He is also given feature to download the data in a tabular format(csv) for accurate analysis. Commodity Wise: User have to provide State,District and Commodity name and then select Commodity wise button. Then user will be shown the prices of all the markets present in the region with the commodity in graphical format, so that he can analyse the cheapest commodity rate. This feature is mostly helpful for wholesale buyers. He is also given feature to download the data in a tabular format(csv) for accurate analysis. On the first activity user is also given forecasting choice. It can be used to forecast the wholesale prices of various commodities at some later year. Regression techniques on timeseries data is used to predict future prices. Select the type of item and click link for future predictions. There are 3 java files Forecasts, DisplayGraphs, DisplayGraphs2 ..... Please change the localhost "server_name" at time of testing as the server name changes each time a new server is made. Things Used: We have used pandas , numpy , scikit learn , seaborn and matplotlib libraries for the same . The dataset is thoroughly analysed using different function available in pandas in my .iPynb file . Not just in-built functions are used but also many user made functions are made to make the working smooth . Various graphs like pointplot , heat-map , barplot , kdeplot , distplot, pairplot , stripplot , jointplot, regplot , etc are made and also deployed on the android app as well . To integrate the android app and machine learning analysis outputs , we have used Flask to host our laptop as the server . We have a separate file for the Flask as server.py . Where all the the necessary stuff of clint request and server response have been dealt with . We have used npm package ngrok for tunneling purpose and hosting . A different .iPynb file is used for the time series predictions using regression algorithms and would send the csv file of prediction along with the graph to the andoid app when given a request .

algorithm-analytics

stock sentiment (twitter) analysis using Python

allcars-gears

#It is a good practice to understand the data first and try to gather as many insights from it. #EDA is all about making sense of data in hand,before getting them dirty with it.

apmc-time-series-analysis

awesome-quant

A curated list of insanely awesome libraries, packages and resources for Quants (Quantitative Finance)

coronavirus-data

The Health Department classifies the start of the outbreak in NYC as the date of the first laboratory-confirmed case, February 29, 2020.

correlationfunnel

Speed Up Exploratory Data Analysis (EDA). The goal of correlationfunnel is to speed up EDA.

feature-engineered-currency-forecast-for-nigeria

Machine Learning Techniques to Foreign Exchange Forecast

kaggle-projects

This repo contains 4 different projects. Built various machine learning models for Kaggle competitions. Also carried out Exploratory Data Analysis, Data Cleaning, Data Visualization, Data Munging, Feature Selection etc

regression-model-fitting

share price+currency+inflation+ growth forecast

scsqccq

statistical-time-series-analysis-on-agricultural-commodity-prices

This project aims to understand trends in agricultural prices through quantity arrival data for different commodities in state of Maharashtra, India.

statistical_testng_narrative_analytics-

Through A/B Experiment and data analysis, an approach to validating the effectiveness of state-level firearm provisions with respect to murder and population data pulled from the U.S. Census organization.

stock-time-series-analysis

Mathematical modeling for finantial time series data

supervised_model_analysis

The purpose of this study is to assist in the use and interpretation of intrapartum cardiotocography (CTG), as well as in the clinical management of specific CTG patterns.

telematic-obs-car-gear-prediction

Exploratory Data Analysis (EDA) & Machine Learning (ML) Techniques to Car Data and Deep Code check

the-cost-of-healcare-related-to-longevity

Exploratory data analysis to see if there is any country, demonstrating more effective use of Healthcare cost to maintain its longevity than other countries.

time-series-analysis-and-forecasting-with-python

trading-risk

water-energy-demand

water_demand

The purpose of the research is to estimate the relationship between climatological variables and peak demand with emphasis on the magnitude or size of the effect and the policy direction. In forecasting short-term demand, linear and multiple regression models are used to quantify the effects of the climatological variables (rainfall and temperature at current and past values) on peak water demand using daily data from 2010 -2019. An autoregressive integrated moving-average (ARIMA) model will be used to forecast the water demand trend.