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https://github.com/barasedih11/finding_donors

First stage project at Udacity on the 'Intro to Machine Learning with TensorFlow' program using sckit-learn in python
https://github.com/barasedih11/finding_donors

csv machine-learning matplotlib numpy pandas python sckiit-learn seaborn sklearn udacity udacity-nanodegree

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First stage project at Udacity on the 'Intro to Machine Learning with TensorFlow' program using sckit-learn in python

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README 

        





  

  ![Finding_Donors](https://github.com/BaraSedih11/finding_donors/assets/98843912/600353c1-08fe-4a41-854c-36d896a4952d)



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This repository contains a training and prediction model, along with tuning and testing, to identify the best estimators and features for our dataset.



## Introduction

We explored three models and ultimately chose the Random Forest model, which proved to be the most suitable for our dataset. We then fine-tuned the hyperparameters to obtain the best estimators and identified the top 5 features. Finally, we trained a reduced model using these features.



## Contents



- `finding_donors.ipynb`: Jupyter Notebook containing the implementation of Random Forest using Python.

- `report.html`: An html page presenting the jupyter notebook.

- `README.md`: This file providing an overview of the repository.

- `census.csv`: This is the working dataset.



## Requirements

To run the code in the Jupyter Notebook, you need to have Python installed on your system along with the following libraries:



* NumPy

* pandas

* scikit-learn

* matplotlib

* seaborn

You can install these libraries using pip:



```bash

pip install numpy pandas scikit-learn matplotlib seaborn

```



## Usage



1. Clone this repository to your local machine:



```bash

git clone https://github.com/BaraSedih11/finding_donors.git

```



2. Navigate to the repository directory:



```bash

cd finding_donors

```



3. Open and run the Jupyter Notebook `finding_donors.ipynb` using Jupyter Notebook or JupyterLab.



4. Follow along with the code and comments in the notebook to understand how Random Forest and training and tuning is implemented using Python.



## Acknowledgements



- [scikit-learn](https://scikit-learn.org/): The scikit-learn library for machine learning in Python.

- [NumPy](https://numpy.org/): The NumPy library for numerical computing in Python.

- [pandas](https://pandas.pydata.org/): The pandas library for data manipulation and analysis in Python.

- [matplotlib](https://matplotlib.org/): The matplotlib library for data visualization in Python.

- [seaborn](https://seaborn.pydata.org/): The seaborn library for data visualization in Python.