fadhilahnur Goto Github PK

agriculture-crop-recommendation-algorithm

To recommend optimum crops to be cultivated by farmers based on several parameters and help them make an informed decision before cultivation

agriculture-crop-recommendation-system

By using external factors on fertilizers composition, climate change and geographical features, I used classification method in machine learning model by using Random Forest in Python to create this agriculture crop recommendation system as an innovative approach to plan and improve crop yield. Data is stored in MSSQL database, then connect Python to the database to code the machine learning model. The user interface (UI) is created by using Python TKinter. The calculation from Random Forest is pack using pickle module in Python, where it can be connected to UI to called the result for crop yield suggestion when user key in their data. This dataset contains 22 types of crops yield with details on Nitrogen, Potassium, Phosphorus, temperature, humidity, pH Level and rainfall. This model is tested using Decision Tree, Naïve Bayes, Support Vector Machine, Logistic Regression and Random Forest. Out of this 5 models, Random Forest has the highest accuracy. This approach may be able to reduce vulnerability in agriculture landscape and questioned raised on meeting global food demand sustainability. This also can be used as solution for young farmers to fully optimize their planning on what crop they can plant based on the current geographical features and mineral composition.

billing-software

Python Language

dates-fruits-classification-with-xgboost-model-in-python

The aim of this study is to classify the types of date fruit, that are, Barhee, Deglet Nour, Sukkary, Rotab Mozafati, Ruthana, Safawi, and Sagai. 898 images of seven different date fruit types were obtained via the computer vision system (CVS). Through image processing techniques, a total of 34 features, including morphological features, shape, and color, were extracted from these images

demand-forecasting-with-holt-s-winter-method-in-python

Demand forecasting time series data using Holt's Winter Method in Python

fadhilahnur

Config files for my GitHub profile.

github-slideshow

A robot powered training repository :robot:

impact-oil-and-usd-towards-customer-price-index-cpi-time-series-forecasting-with-python

Estimate the impact of oil and USD changes towards Customer Price Index (CPI) using Python

ml_gui

This repo contains the code for a GUI which can be used for training different ML models as well as for data visualisation

myr-vs.-usd-forecast-with-random-walk-time-series-forecasting-

Search for best model for MYR vs. USD series using Random Walk method with EViews.

myr-vs.-usd-time-series-forecasting-with-average-mean-square-errors-calculations-with-eviews

By using exponential smoothing (ETS), I search for the best model for MYR vs. USD series using average mean square errors calculations. Then, display the actual and smoothed series over the estimation and forecast sample. This program is using EViews Programming.

newsvendor

oil-and-usd-towards-cpi-time-series-analysis-and-forecasting-

Estimate the impact of OIL and USD towards CPI using least squares method using R

python-apps-software

Application & Software created here using Python, integrating with MySQL as database

retail-sales-forecast-with-xgboost

Retail Sales Forecast With XGBoost Model

unemployment-rate-time-series-forecasting-with-arima-box-jenkins-method-

The change of unemployment rate is affected by the economic transformation that had taken place whether the economy is in recession or booming. This program is to see how important unemployment towards Malaysian economy and how COVID- 19 pandemic affects the unemployment. Other than that, it is also to analysis the forecast for unemployed that are obtained from ARIMA data.