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
54382 Version:1.2.2scikit-learn: machine learning in Python
scikit-learnby scikit-learn
Python 54382 Version:1.2.2 License: Permissive (BSD-3-Clause)
pandasby pandas-dev
38499 Version:v2.0.2Flexible 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
23587 Version:v1.24.3The 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
17428 Version:v3.7.1
License: No License (null)matplotlib: plotting with Python
matplotlibby matplotlib
Python 17428 Version:v3.7.1 License: No License
auto-sklearnby automl
6954 Version:v0.15.0Automated Machine Learning with scikit-learn
auto-sklearnby automl
Python 6954 Version:v0.15.0 License: Permissive (BSD-3-Clause)
cheatsheetsby matplotlib
6898 Version:CurrentOfficial 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