solar data prediction
by guruprasanth6901 Updated: Jan 9, 2022
Solution Kit
It is solar prediction tool. we taking the consideration of the battery power and the load powers both 1 and 2. if the net rate of the both battery power and inverted power per day is greater than the load power ,then outcome will be zero. else the outcome flag will be 1.
Group Name 1
pandas is a software library written for the Python programming language for data manipulation and analysis. In particular, it offers data structures and operations for manipulating numerical tables and time series. Scikit-learn is an indispensable part of the Python machine learning toolkit . It is very widely used across all parts of the bank for classification, predictive analytics, and very many other machine learning tasks. NumPy is a Python library used for working with arrays
scikit-learnby scikit-learn
scikit-learn: machine learning in Python
scikit-learnby scikit-learn
Python
54382
Version:1.2.2
License: Permissive (BSD-3-Clause)
pandasby pandas-dev
Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more
pandasby pandas-dev
Python
38499
Version:v2.0.2
License: Permissive (BSD-3-Clause)
numpyby numpy
The fundamental package for scientific computing with Python.
numpyby numpy
Python
23587
Version:v1.24.3
License: Permissive (BSD-3-Clause)
Group Name 2
Matplotlib is a plotting library for the Python programming language and its numerical mathematics extension NumPy.
matplotlibby matplotlib
matplotlib: plotting with Python
matplotlibby matplotlib
Python
17428
Version:v3.7.1
License: No License
auto-sklearnby automl
Automated Machine Learning with scikit-learn
auto-sklearnby automl
Python
6954
Version:v0.15.0
License: Permissive (BSD-3-Clause)
cheatsheetsby matplotlib
Official Matplotlib cheat sheets
cheatsheetsby matplotlib
Python
6898
Version:Current
License: Permissive (BSD-2-Clause)
Group Name 3
here it is our github repo .
Deployment Information
1.preparation of data 2.importing the libraries 3.cleaning the data(collecting only every days last time datas) 4.visualize the data (battery and load power) 5.train the data 6.use sklearn libraray 7.use knn alogoithm to predict the output