The Sparks Foundation -Graduation Rotational Internship Program

This repository is dedicated to the completion of all my tasks with videos from The Sparks Foundation (Graduate Rotational Internship Program). As of now, I will be updating the tasks from my domain : Data Science and Business Analytics for the May 2021 batch.

Tools/IDE : Python/Google Colab/Jupyter Notebook

Technical : Task 1 - Prediction using Supervised ML (Level - Beginner)

Predict the percentage of an student based on the no. of study hours.

This is supposed to be done with linear regression as we will be using just 2 variables.

Dataset for this model can be found at : http://bit.ly/w-data.

Code for this model can be found at : Task_1_Code.

Video for this model can be found at : Task_1_Video.

What are we supposed to do with the given dataset?

We need to predict the score of the student if he/she studies for 9.25 hrs/day.

Technical : Task 2 - Prediction using Unsupervised ML (Level - Beginner)

Predict the optimum number of clusters, from the given "iris" dataset and represent it visually.

I will be implementing this with the help of K-Means Clustering algorithm.

Dataset for this model can be found at : https://bit.ly/3kXTdox.

Code for this model can be found at : Task_2_Code.

Video for this model can be found at : Task_2_Video.

What are we supposed to do with the given dataset?

We need to predict the optimum number of clusters and it's visualization.

Technical : Task 3 - Exploratory Data Analysis - Retail (Level - Beginner)

Perform ‘Exploratory Data Analysis’ on dataset ‘SampleSuperstore’

I will be doing this with the help of python libraries i.e. matplotlib, plotly, plotnine and seaborn.

Dataset can be found at : https://bit.ly/3i4rbWl.

Code for this model can be found at : Task_3_Code.

Video for this model can be found at : Task_3_Video.

What are we supposed to do with the given dataset?

As a business manager, we will try to find out the weak areas where we can work tomake more profit. Also, what all business problems can be derived by exploring the data.

Technical : Task 4 - Exploratory Data Analysis - Terrorism (Level - Intermediate)

Perform ‘Exploratory Data Analysis’ on dataset ‘Global Terrorism’

I will be doing this with the help of seaborn, plotly and folium libraries in python.

Dataset can be found at : https://bit.ly/2TK5Xn5.

Code for this model can be found at : Task_4_Code.

Video for this model can be found at : Task_4_Video.

What are we supposed to do with the given dataset?

As a security/defense analyst, we will try to find out the hot zone of terrorism. Also, what all security issues and insights can be derived by EDA.

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the-spark-foundation-internship's Introduction

The Sparks Foundation -Graduation Rotational Internship Program

Tools/IDE : Python/Google Colab/Jupyter Notebook

Technical : Task 1 - Prediction using Supervised ML (Level - Beginner)

Predict the percentage of an student based on the no. of study hours.

What are we supposed to do with the given dataset?

Technical : Task 2 - Prediction using Unsupervised ML (Level - Beginner)

Predict the optimum number of clusters, from the given "iris" dataset and represent it visually.

What are we supposed to do with the given dataset?

Technical : Task 3 - Exploratory Data Analysis - Retail (Level - Beginner)

Perform ‘Exploratory Data Analysis’ on dataset ‘SampleSuperstore’

What are we supposed to do with the given dataset?

Technical : Task 4 - Exploratory Data Analysis - Terrorism (Level - Intermediate)

Perform ‘Exploratory Data Analysis’ on dataset ‘Global Terrorism’

What are we supposed to do with the given dataset?

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