A one-stop repository for new-comers in Machine Learning and A.I.
This repository has two projects -
Cancer_prediction.ipynb
L&T_Stock_Price_prediction.ipynb
Machine learning is not new to cancer research. Artificial neural networks (ANNs) and decision trees (DTs) have been used in cancer detection and diagnosis for nearly 20 years.The fundamental goals of cancer prediction and prognosis are distinct from the goals of cancer detection and diagnosis.
Stock market prediction aims to determine the future movement of the stock value of a financial exchange. The accurate prediction of share price movement will lead to more profit investors can make.
The idea
The idea is to predict whether a cell is cancerous or non-cancerous based on different features of cell using different Machine learning algorithms or Deep learning techniques
The idea is to predict the future stock pricing based on different dependencies of a stock using different Machine learning algorithms or Deep learning techniques
Project structure
. ├── Classification │ ├── Cancer_prediction.ipynb Jupyter notebook for Cancer prediction │ ├── Datasets Dataset for Cancer prediction │ │ ├── cancer_data.csv │ │ └── dataset.txt │ └── classification.txt Basic information about Classification ├── Regression │ ├── Datasets Dataset for L&T stock price prediction │ │ ├── LT.csv │ │ └── dataset.txt │ ├── L&T_Stock_Price_prediction.ipynb Jupyter notebook for Stock price prediction │ └── regression.txt Basic information about Regression ├── LICENSE ├── code_of_conduct.md ├── contributing.md └── readme.md
Project roadmap
The project currently does the following things-
Following things can be implemented -
Very basic understanding of git and github:
For EDA and Visualisation
python
.(This is a must)pandas
library. Reading this blog might help.matplotlib
library. Reading this blog might help.seaborn
library. Reading this blog might help.scikit learn
library. Reading this blog might help.tensorflow
library. Reading this blog might help.However the code is well explained, so anyone knowing the basics of Python can get a idea of what's happenning and contribute to this.
A step by step series of examples that tell you how to get a development env running.
There are two ways of running the code.
Running the code on web browser.(Google Colab) [Recommended]
Upload Notebook
Tab.Run All
.
You can also run the code locally in your computer by installing Anaconda.
conda
. Follow these steps to install jupyter notebook.pandas
,matplotlib
,seaborn
and scikit-learn
to run the notebook.Notebook will be opened in Google Colab
Cancer_prediction
previewL&T_Stock_Price_prediction
previewPlease read contributing.md for details on our code of conduct, and the process for submitting pull requests to us.
This project is licensed under the MIT License - see the LICENSE file for details.