In this project we will go through a dataset containing students performane in a particular exam of maths, reading and writing.we will dig deep into the factors that have affected the score of the students and which category of students have performed well and which category of students need improvement. I have collected the dataset from kaggle.The following course Data Analysis with Python: Zero to Pandas has helped me a lot into the insights of python and various libraries and tools which has helped me to complete this project with ease.
How to run the code
This is an executable Jupyter notebook hosted on Jovian.ml, a platform for sharing data science projects. You can run and experiment with the code in a couple of ways: using free online resources (recommended) or on your own computer.
Option 1: Running using free online resources (1-click, recommended)
The easiest way to start executing this notebook is to click the "Run" button at the top of this page, and select "Run on Binder". This will run the notebook on mybinder.org, a free online service for running Jupyter notebooks. You can also select "Run on Colab" or "Run on Kaggle".
Option 2: Running on your computer locally
Install Conda by following these instructions. Add Conda binaries to your system
PATH, so you can use the
condacommand on your terminal.
Create a Conda environment and install the required libraries by running these commands on the terminal:
conda create -n zerotopandas -y python=3.8
conda activate zerotopandas
pip install jovian jupyter numpy pandas matplotlib seaborn opendatasets --upgrade
- Press the "Clone" button above to copy the command for downloading the notebook, and run it on the terminal. This will create a new directory and download the notebook. The command will look something like this:
jovian clone notebook-owner/notebook-id
- Enter the newly created directory using
cd directory-nameand start the Jupyter notebook.
You can now access Jupyter's web interface by clicking the link that shows up on the terminal or by visiting http://localhost:8888 on your browser. Click on the notebook file (it has a
.ipynb extension) to open it.
Downloading the Dataset
I have downloaded the dataset in my local computer and have performed the operations below
from urllib.request import urlretrieve
<http.client.HTTPMessage at 0x1a6cc515208>)
import pandas as pd